This document summarizes a research paper that proposes using a fuzzy ant colony system approach to solve the fuzzy vehicle routing problem with time windows (VRPTW). The paper models the fuzzy VRPTW using credibility theory to handle uncertain travel times represented as triangular fuzzy numbers. An improved ant colony system is then used as a metaheuristic to find high-quality routes that satisfy time window constraints at a desired level of confidence. Computational results on benchmark problems demonstrate the effectiveness of integrating fuzzy concepts with the ant colony system to solve the fuzzy VRPTW.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document reviews a fuzzy microscopic traffic model that uses fuzzy logic to simulate traffic streams at signalized intersections. The model represents vehicle parameters like position and velocity as fuzzy numbers. It combines aspects of cellular automata models and fuzzy calculus. Compared to traditional cellular automata models, the fuzzy microscopic model requires fewer simulation runs, stores less data, and estimates output distributions in a single run. Future work could explore a stochastic cellular automata model with fuzzy decision rules to analyze more complex traffic situations.
Quantum inspired evolutionary algorithm for solving multiple travelling sales...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The document discusses fuzzy logic and its application in traffic modeling. It provides background on fuzzy set theory and fuzzy logic. It then summarizes research using fuzzy logic models for car-following behavior. One study developed a fuzzy logic car-following model using relative speed and distance as inputs and acceleration as the output. The model was validated using real-world driving data. Other research applied fuzzy rule-based models and inference systems to lane changing decisions under heavy traffic conditions.
In solving real life transportation problem we often face the state of uncertainty as well as hesitation due to various uncontrollable factors. To deal with uncertainty and hesitation many authors have suggested the intuitionistic fuzzy representation for the data. So, in this paper, we consider a transportation problem having uncertainty and hesitation in supply, demand and costs. We formulate the problem and utilize triangular intuitionistic fuzzy numbers (TrIFNs) to deal with uncertainty and hesitation. We propose a new method called PSK method for finding the intuitionistic fuzzy optimal solution for fully intuitionistic fuzzy transportation problem in single stage. Also the new multiplication operation on TrIFN is proposed to find the optimal object value in terms of TrIFN. The main advantage of this method is computationally very simple, easy to understand and also the optimum objective value obtained by our method is physically meaningful. Finally the effectiveness of the proposed method is illustrated by means of a numerical example which is followed by graphical representation of the finding.
ENTROPY-COST RATIO MAXIMIZATION MODEL FOR EFFICIENT STOCK PORTFOLIO SELECTION...cscpconf
This paper introduces a new stock portfolio selection model in non-stochastic environment.Following the principle of maximum entropy, a new entropy-cost ratio function is introduced as
the objective function. The uncertain returns, risks and ividends of the securities are considered as interval numbers. Along with the objective function, eight different types of constraints are used in the model to convert it into a pragmatic one. Three different models have been proposed by defining the future inancial market optimistically, pessimistically and in hecombined form to model the portfolio selection problem. To illustrate the effectiveness and tractability of the proposed models, these are tested on a set of data from Bombay Stock Exchange (BSE). The solution has been done by genetic algorithm.
This document summarizes a research paper that proposes using a genetic algorithm to solve the travelling salesman problem (TSP). It begins by defining the TSP and explaining that it is NP-hard. The document then reviews various existing approaches that have used genetic algorithms and other metaheuristics to solve TSP. It proposes a genetic algorithm with tournament selection, two-point crossover, and interchange mutation operators. The algorithm is tested on sample problems with 15 cities and is shown to find optimal or near-optimal solutions. In conclusion, the document argues that genetic algorithms can efficiently find good solutions to TSP, especially when combined with knowledge from heuristic methods.
A Fuzzy Mean-Variance-Skewness Portfolioselection Problem.inventionjournals
A fuzzy number is a normal and convex fuzzy subsetof the real line. In this paper, based on membership function, we redefine the concepts of mean and variance for fuzzy numbers. Furthermore, we propose the concept of skewness and prove some desirable properties. A fuzzy mean-variance-skewness portfolio se-lection model is formulated and two variations are given, which are transformed to nonlinear optimization models with polynomial ob-jective and constraint functions such that they can be solved analytically. Finally, we present some numerical examples to demonstrate the effectiveness of the proposed models
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document reviews a fuzzy microscopic traffic model that uses fuzzy logic to simulate traffic streams at signalized intersections. The model represents vehicle parameters like position and velocity as fuzzy numbers. It combines aspects of cellular automata models and fuzzy calculus. Compared to traditional cellular automata models, the fuzzy microscopic model requires fewer simulation runs, stores less data, and estimates output distributions in a single run. Future work could explore a stochastic cellular automata model with fuzzy decision rules to analyze more complex traffic situations.
Quantum inspired evolutionary algorithm for solving multiple travelling sales...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The document discusses fuzzy logic and its application in traffic modeling. It provides background on fuzzy set theory and fuzzy logic. It then summarizes research using fuzzy logic models for car-following behavior. One study developed a fuzzy logic car-following model using relative speed and distance as inputs and acceleration as the output. The model was validated using real-world driving data. Other research applied fuzzy rule-based models and inference systems to lane changing decisions under heavy traffic conditions.
In solving real life transportation problem we often face the state of uncertainty as well as hesitation due to various uncontrollable factors. To deal with uncertainty and hesitation many authors have suggested the intuitionistic fuzzy representation for the data. So, in this paper, we consider a transportation problem having uncertainty and hesitation in supply, demand and costs. We formulate the problem and utilize triangular intuitionistic fuzzy numbers (TrIFNs) to deal with uncertainty and hesitation. We propose a new method called PSK method for finding the intuitionistic fuzzy optimal solution for fully intuitionistic fuzzy transportation problem in single stage. Also the new multiplication operation on TrIFN is proposed to find the optimal object value in terms of TrIFN. The main advantage of this method is computationally very simple, easy to understand and also the optimum objective value obtained by our method is physically meaningful. Finally the effectiveness of the proposed method is illustrated by means of a numerical example which is followed by graphical representation of the finding.
ENTROPY-COST RATIO MAXIMIZATION MODEL FOR EFFICIENT STOCK PORTFOLIO SELECTION...cscpconf
This paper introduces a new stock portfolio selection model in non-stochastic environment.Following the principle of maximum entropy, a new entropy-cost ratio function is introduced as
the objective function. The uncertain returns, risks and ividends of the securities are considered as interval numbers. Along with the objective function, eight different types of constraints are used in the model to convert it into a pragmatic one. Three different models have been proposed by defining the future inancial market optimistically, pessimistically and in hecombined form to model the portfolio selection problem. To illustrate the effectiveness and tractability of the proposed models, these are tested on a set of data from Bombay Stock Exchange (BSE). The solution has been done by genetic algorithm.
This document summarizes a research paper that proposes using a genetic algorithm to solve the travelling salesman problem (TSP). It begins by defining the TSP and explaining that it is NP-hard. The document then reviews various existing approaches that have used genetic algorithms and other metaheuristics to solve TSP. It proposes a genetic algorithm with tournament selection, two-point crossover, and interchange mutation operators. The algorithm is tested on sample problems with 15 cities and is shown to find optimal or near-optimal solutions. In conclusion, the document argues that genetic algorithms can efficiently find good solutions to TSP, especially when combined with knowledge from heuristic methods.
A Fuzzy Mean-Variance-Skewness Portfolioselection Problem.inventionjournals
A fuzzy number is a normal and convex fuzzy subsetof the real line. In this paper, based on membership function, we redefine the concepts of mean and variance for fuzzy numbers. Furthermore, we propose the concept of skewness and prove some desirable properties. A fuzzy mean-variance-skewness portfolio se-lection model is formulated and two variations are given, which are transformed to nonlinear optimization models with polynomial ob-jective and constraint functions such that they can be solved analytically. Finally, we present some numerical examples to demonstrate the effectiveness of the proposed models
Vehicle routing problem is a NP-hard problem, with the expansion of problem solving more difficult.
This paper proposes a hybrid behavior based on ant colony algorithm to solve the problem, ant to different
objectives in the first place as the path selection according to the analysis of the impact on the algorithm, then
define the ant behavior and design four concrete ant behavior by selecting different ways of ant behavior to
form different improved algorithm. Finally, experimental results show that the improved algorithm can solve
vehicle routing problems quickly and effectively.
Two Phase Algorithm for Solving VRPTW ProblemWaqas Tariq
Vehicle Routing Problem with Time Windows (VRPTW) is a well known NP hard combinatorial scheduling optimization problem in which minimum number of routes have to be determined to serve all the customers within their specified time windows. Different analytic and heuristic approaches have been tried to solve such problems. In this paper we propose a two phase method which utilizes Genetic algorithms as well as random search incorporating simulated annealing concepts to solve VRPTW problem in various scenarios.
Comparison between the genetic algorithms optimization and particle swarm opt...IAEME Publication
The document compares the genetic algorithms optimization and particle swarm optimization methods for designing close range photogrammetry networks. It presents the genetic algorithm and particle swarm optimization as two popular meta-heuristic algorithms inspired by natural evolution and collective animal behavior, respectively. The document develops mathematical models representing the genetic algorithm and particle swarm optimization for close range photogrammetry network design and evaluates them in a test field to reinforce the theoretical aspects.
Critical Paths Identification on Fuzzy Network Projectiosrjce
In this paper, a new approach for identifying fuzzy critical path is presented, based on converting the
fuzzy network project into deterministic network project, by transforming the parameters set of the fuzzy
activities into the time probability density function PDF of each fuzzy time activity. A case study is considered as
a numerical tested problem to demonstrate our approach.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of
decision problems under uncertainty. The exact approaches for computing decision based on possibilistic
networks are limited by the size of the possibility distributions. Generally, these approaches are based on
possibilistic propagation algorithms. An important step in the computation of the decision is the
transformation of the DAG (Direct Acyclic Graph) into a secondary structure, known as the junction trees
(JT). This transformation is known to be costly and represents a difficult problem. We propose in this paper
a new approximate approach for the computation of decision under uncertainty within possibilistic
networks. The computing of the optimal optimistic decision no longer goes through the junction tree
construction step. Instead, it is performed by calculating the degree of normalization in the moral graph
resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying
its preferences.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
On Intuitionistic Fuzzy Transportation Problem Using Pentagonal Intuitionisti...YogeshIJTSRD
In this paper a new method is proposed for finding an optimal solution for Pentagonal intuitionistic fuzzy transportation problems, in which the cost values are Pentagonal intuitionistic fuzzy numbers. The procedure is illustrated with a numerical example. P. Parimala | P. Kamalaveni "On Intuitionistic Fuzzy Transportation Problem Using Pentagonal Intuitionistic Fuzzy Numbers Solved by Modi Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41094.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/41094/on-intuitionistic-fuzzy-transportation-problem-using-pentagonal-intuitionistic-fuzzy-numbers-solved-by-modi-method/p-parimala
The document describes the development of a mobile game to help students learn Boolean logic and the Quine-McCluskey algorithm. The game allows users to minimize Boolean expressions by solving Karnaugh maps of varying difficulty. The authors implemented the Quine-McCluskey algorithm in Swift to generate optimal solutions and check user answers. They discuss challenges like the algorithm's exponential time complexity and cases with no essential prime implicants. The prototype lets users set the problem size and difficulty to generate random Karnaugh maps to solve.
Flavours of Physics Challenge: Transfer Learning approachAlexander Rakhlin
Presentation for "Heavy Flavour Data Mining workshop", February 18-19, University of Zurich. I discuss the solution that won Physics Prize of Flavours of Physics challenge organized by CERN, Yandex, Intel at Kaggle.
A heuristic approach for optimizing travel planning using genetics algorithmeSAT Journals
Abstract In today’s fast-paced society, everyone is caught up in the hustle and bustle of life which has resulted in ineffective Planning of their very important vacation tour. Either they spend much time on deciding what to do next, or will take many unnecessary, unfocused and inefficient steps. The main purpose of our project is to develop a Travel Planner that will allow the customer to plan the entire tour so that he visits many places in less time. The concept would be implemented using Genetics Algorithm of Artificial Intelligence which would be used as a search algorithm to find the nearest optimal travel path. Moreover, In order to reduce the running time of GA, Parallelization of Genetics Algorithm would be demonstrated using Hadoop Framework. Key Words: Genetics Algorithm, TSP, Hadoop, and MapReduce etc…
A heuristic approach for optimizing travel planning using genetics algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Developing effective meta heuristics for a probabilisticHari Rajagopalan
This document summarizes an article that evaluates four meta-heuristics (evolutionary algorithm, tabu search, simulated annealing, and hybridized hill-climbing) for solving a probabilistic location model called the maximum expected coverage location problem (MEXCLP). The MEXCLP aims to locate a limited number of ambulances to maximize expected coverage of demand points within a response time threshold. The article uses statistical experimental design to objectively analyze the performance of the four meta-heuristics on test problems of varying sizes. The results show that on average tabu search and simulated annealing find high quality solutions in the least amount of time, especially for large problems requiring dynamic redeployment, though all four methods produced good results
UNDERSTANDING NEGATIVE SAMPLING IN KNOWLEDGE GRAPH EMBEDDINGijaia
This document summarizes and categorizes existing approaches for negative sampling in knowledge graph embedding. It divides negative sampling methods into three categories: 1) static distribution-based approaches like uniform and Bernoulli sampling that sample negatives from fixed distributions, 2) dynamic distribution-based approaches that sample from adaptive distributions, and 3) custom cluster-based approaches that group entities for targeted negative sampling. The document analyzes representative approaches within each category and discusses their characteristics and limitations to provide guidance on negative sampling in knowledge graph embedding.
The document discusses several algorithms for finding the shortest path in a graph: Dijkstra's algorithm, Floyd-Warshall algorithm, Bellman-Ford algorithm, and genetic algorithms. It provides details on how Dijkstra's and Floyd-Warshall algorithms work, including pseudocode. Examples are given for both algorithms. Applications of the different algorithms are also discussed.
The paper presents a quantified modal logic for spatial qualification that uses qualitative reasoning to infer an agent's possible presence at a location. It defines predicates for presence, occupancy of regions, and spatial relations. Axioms state that presence facts persist and an agent can remain at a location. Reachability between locations within a time interval allows determining if an agent could be somewhere. The logic is compared to modal logics S4 and S5.
Fuzzy Retrial Queues with Priority using DSW Algorithmijceronline
In this paper we study the priority queueing model under fuzzy environment.It optimize a fuzzy priority queueing model (preemptive priority, non-preemptive priority) in which arrival rate ,service rate,retrial rate are fuzzy numbers.Approximate method of Extension namely DSW (Dong, Shah and Wong) algorithm is used to define membership functions of the performance measures of priority queuing system . DSW algorithm is based on the cut representation of fuzzy sets in a standard interval analysis. Numerical example is also illustrated to check the validity of the model.
IRJET-Debarred Objects Recognition by PFL OperatorIRJET Journal
This document discusses a method for recognizing debarred objects like pistols, knives, and handguns in x-ray luggage scans using Partial Fuzzy Logic (PFL). PFL is used to estimate the degree of similarity between scanned objects and prohibited items. The method involves segmenting objects from x-ray images, then applying PFL to aggregate information and determine if an object matches prohibited items based on weighted criteria. PFL lies between logical "and" and "or" to provide a parameterized aggregation. The document tests the method on sample x-ray images to recognize knives and other banned objects.
The philosophy of fuzzy logic was formed by introducing the membership degree of a linguistic value or variable instead of divalent membership of 0 or 1. Membership degree is obtained by mapping the variable on the graphical shape of fuzzy numbers. Because of simplicity and convenience, triangular membership numbers (TFN) are widely used in different kinds of fuzzy analysis problems. This paper suggests a simple method using statistical data and frequency chart for constructing non-isosceles TFN when we are using direct rating for evaluating a variable in a predefined scale. In this method, the relevancy between assessment uncertainties and statistical parameters such as mean value and the standard deviation is established in a way that presents an exclusive form of triangle number for each set of data. The proposed method with regard to the graphical shape of the frequency chart distributes the standard deviation around the mean value and forms the TFN with the membership degree of 1 for mean value. In the last section of the paper modification of the proposed method is presented through a practical case study.
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge ...ijcsa
This document summarizes a research paper that introduces a text mining-based method for answering biological queries and testing hypotheses. The proposed approach analyzes hypotheses stated as natural language questions and measures their statistical significance based on existing literature. It computes a p-value to determine whether to accept or reject each hypothesis. The method also generates a network of related biological entities to provide context and suggest new hypotheses for further investigation. The goal is to help researchers quantitatively evaluate assumptions and guide relevant discovery of new biological knowledge.
A novel defence scheme against selfish Node attack in manetijcsa
This document proposes a new intrusion detection system (IDS) algorithm to defend against selfish node attacks in mobile ad hoc networks (MANETs). Selfish nodes flood the network with false information and drop packets from other nodes. The proposed IDS identifies selfish node behavior and blocks their activities. Simulation results show the IDS enhances network performance from negligible to 92% and prevents infection from attacks. The IDS is integrated with the AODV routing protocol to detect and eliminate selfish nodes within its transmission range.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.
Vehicle routing problem is a NP-hard problem, with the expansion of problem solving more difficult.
This paper proposes a hybrid behavior based on ant colony algorithm to solve the problem, ant to different
objectives in the first place as the path selection according to the analysis of the impact on the algorithm, then
define the ant behavior and design four concrete ant behavior by selecting different ways of ant behavior to
form different improved algorithm. Finally, experimental results show that the improved algorithm can solve
vehicle routing problems quickly and effectively.
Two Phase Algorithm for Solving VRPTW ProblemWaqas Tariq
Vehicle Routing Problem with Time Windows (VRPTW) is a well known NP hard combinatorial scheduling optimization problem in which minimum number of routes have to be determined to serve all the customers within their specified time windows. Different analytic and heuristic approaches have been tried to solve such problems. In this paper we propose a two phase method which utilizes Genetic algorithms as well as random search incorporating simulated annealing concepts to solve VRPTW problem in various scenarios.
Comparison between the genetic algorithms optimization and particle swarm opt...IAEME Publication
The document compares the genetic algorithms optimization and particle swarm optimization methods for designing close range photogrammetry networks. It presents the genetic algorithm and particle swarm optimization as two popular meta-heuristic algorithms inspired by natural evolution and collective animal behavior, respectively. The document develops mathematical models representing the genetic algorithm and particle swarm optimization for close range photogrammetry network design and evaluates them in a test field to reinforce the theoretical aspects.
Critical Paths Identification on Fuzzy Network Projectiosrjce
In this paper, a new approach for identifying fuzzy critical path is presented, based on converting the
fuzzy network project into deterministic network project, by transforming the parameters set of the fuzzy
activities into the time probability density function PDF of each fuzzy time activity. A case study is considered as
a numerical tested problem to demonstrate our approach.
A NEW APPROACH IN DYNAMIC TRAVELING SALESMAN PROBLEM: A HYBRID OF ANT COLONY ...ijmpict
Nowadays swarm intelligence-based algorithms are being used widely to optimize the dynamic traveling salesman problem (DTSP). In this paper, we have used mixed method of Ant Colony Optimization (AOC) and gradient descent to optimize DTSP which differs with ACO algorithm in evaporation rate and innovative data. This approach prevents premature convergence and scape from local optimum spots and also makes it possible to find better solutions for algorithm. In this paper, we’re going to offer gradient descent and ACO algorithm which in comparison to some former methods it shows that algorithm has significantly improved routes optimization.
Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of
decision problems under uncertainty. The exact approaches for computing decision based on possibilistic
networks are limited by the size of the possibility distributions. Generally, these approaches are based on
possibilistic propagation algorithms. An important step in the computation of the decision is the
transformation of the DAG (Direct Acyclic Graph) into a secondary structure, known as the junction trees
(JT). This transformation is known to be costly and represents a difficult problem. We propose in this paper
a new approximate approach for the computation of decision under uncertainty within possibilistic
networks. The computing of the optimal optimistic decision no longer goes through the junction tree
construction step. Instead, it is performed by calculating the degree of normalization in the moral graph
resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying
its preferences.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
On Intuitionistic Fuzzy Transportation Problem Using Pentagonal Intuitionisti...YogeshIJTSRD
In this paper a new method is proposed for finding an optimal solution for Pentagonal intuitionistic fuzzy transportation problems, in which the cost values are Pentagonal intuitionistic fuzzy numbers. The procedure is illustrated with a numerical example. P. Parimala | P. Kamalaveni "On Intuitionistic Fuzzy Transportation Problem Using Pentagonal Intuitionistic Fuzzy Numbers Solved by Modi Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41094.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathematics/41094/on-intuitionistic-fuzzy-transportation-problem-using-pentagonal-intuitionistic-fuzzy-numbers-solved-by-modi-method/p-parimala
The document describes the development of a mobile game to help students learn Boolean logic and the Quine-McCluskey algorithm. The game allows users to minimize Boolean expressions by solving Karnaugh maps of varying difficulty. The authors implemented the Quine-McCluskey algorithm in Swift to generate optimal solutions and check user answers. They discuss challenges like the algorithm's exponential time complexity and cases with no essential prime implicants. The prototype lets users set the problem size and difficulty to generate random Karnaugh maps to solve.
Flavours of Physics Challenge: Transfer Learning approachAlexander Rakhlin
Presentation for "Heavy Flavour Data Mining workshop", February 18-19, University of Zurich. I discuss the solution that won Physics Prize of Flavours of Physics challenge organized by CERN, Yandex, Intel at Kaggle.
A heuristic approach for optimizing travel planning using genetics algorithmeSAT Journals
Abstract In today’s fast-paced society, everyone is caught up in the hustle and bustle of life which has resulted in ineffective Planning of their very important vacation tour. Either they spend much time on deciding what to do next, or will take many unnecessary, unfocused and inefficient steps. The main purpose of our project is to develop a Travel Planner that will allow the customer to plan the entire tour so that he visits many places in less time. The concept would be implemented using Genetics Algorithm of Artificial Intelligence which would be used as a search algorithm to find the nearest optimal travel path. Moreover, In order to reduce the running time of GA, Parallelization of Genetics Algorithm would be demonstrated using Hadoop Framework. Key Words: Genetics Algorithm, TSP, Hadoop, and MapReduce etc…
A heuristic approach for optimizing travel planning using genetics algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Developing effective meta heuristics for a probabilisticHari Rajagopalan
This document summarizes an article that evaluates four meta-heuristics (evolutionary algorithm, tabu search, simulated annealing, and hybridized hill-climbing) for solving a probabilistic location model called the maximum expected coverage location problem (MEXCLP). The MEXCLP aims to locate a limited number of ambulances to maximize expected coverage of demand points within a response time threshold. The article uses statistical experimental design to objectively analyze the performance of the four meta-heuristics on test problems of varying sizes. The results show that on average tabu search and simulated annealing find high quality solutions in the least amount of time, especially for large problems requiring dynamic redeployment, though all four methods produced good results
UNDERSTANDING NEGATIVE SAMPLING IN KNOWLEDGE GRAPH EMBEDDINGijaia
This document summarizes and categorizes existing approaches for negative sampling in knowledge graph embedding. It divides negative sampling methods into three categories: 1) static distribution-based approaches like uniform and Bernoulli sampling that sample negatives from fixed distributions, 2) dynamic distribution-based approaches that sample from adaptive distributions, and 3) custom cluster-based approaches that group entities for targeted negative sampling. The document analyzes representative approaches within each category and discusses their characteristics and limitations to provide guidance on negative sampling in knowledge graph embedding.
The document discusses several algorithms for finding the shortest path in a graph: Dijkstra's algorithm, Floyd-Warshall algorithm, Bellman-Ford algorithm, and genetic algorithms. It provides details on how Dijkstra's and Floyd-Warshall algorithms work, including pseudocode. Examples are given for both algorithms. Applications of the different algorithms are also discussed.
The paper presents a quantified modal logic for spatial qualification that uses qualitative reasoning to infer an agent's possible presence at a location. It defines predicates for presence, occupancy of regions, and spatial relations. Axioms state that presence facts persist and an agent can remain at a location. Reachability between locations within a time interval allows determining if an agent could be somewhere. The logic is compared to modal logics S4 and S5.
Fuzzy Retrial Queues with Priority using DSW Algorithmijceronline
In this paper we study the priority queueing model under fuzzy environment.It optimize a fuzzy priority queueing model (preemptive priority, non-preemptive priority) in which arrival rate ,service rate,retrial rate are fuzzy numbers.Approximate method of Extension namely DSW (Dong, Shah and Wong) algorithm is used to define membership functions of the performance measures of priority queuing system . DSW algorithm is based on the cut representation of fuzzy sets in a standard interval analysis. Numerical example is also illustrated to check the validity of the model.
IRJET-Debarred Objects Recognition by PFL OperatorIRJET Journal
This document discusses a method for recognizing debarred objects like pistols, knives, and handguns in x-ray luggage scans using Partial Fuzzy Logic (PFL). PFL is used to estimate the degree of similarity between scanned objects and prohibited items. The method involves segmenting objects from x-ray images, then applying PFL to aggregate information and determine if an object matches prohibited items based on weighted criteria. PFL lies between logical "and" and "or" to provide a parameterized aggregation. The document tests the method on sample x-ray images to recognize knives and other banned objects.
The philosophy of fuzzy logic was formed by introducing the membership degree of a linguistic value or variable instead of divalent membership of 0 or 1. Membership degree is obtained by mapping the variable on the graphical shape of fuzzy numbers. Because of simplicity and convenience, triangular membership numbers (TFN) are widely used in different kinds of fuzzy analysis problems. This paper suggests a simple method using statistical data and frequency chart for constructing non-isosceles TFN when we are using direct rating for evaluating a variable in a predefined scale. In this method, the relevancy between assessment uncertainties and statistical parameters such as mean value and the standard deviation is established in a way that presents an exclusive form of triangle number for each set of data. The proposed method with regard to the graphical shape of the frequency chart distributes the standard deviation around the mean value and forms the TFN with the membership degree of 1 for mean value. In the last section of the paper modification of the proposed method is presented through a practical case study.
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge ...ijcsa
This document summarizes a research paper that introduces a text mining-based method for answering biological queries and testing hypotheses. The proposed approach analyzes hypotheses stated as natural language questions and measures their statistical significance based on existing literature. It computes a p-value to determine whether to accept or reject each hypothesis. The method also generates a network of related biological entities to provide context and suggest new hypotheses for further investigation. The goal is to help researchers quantitatively evaluate assumptions and guide relevant discovery of new biological knowledge.
A novel defence scheme against selfish Node attack in manetijcsa
This document proposes a new intrusion detection system (IDS) algorithm to defend against selfish node attacks in mobile ad hoc networks (MANETs). Selfish nodes flood the network with false information and drop packets from other nodes. The proposed IDS identifies selfish node behavior and blocks their activities. Simulation results show the IDS enhances network performance from negligible to 92% and prevents infection from attacks. The IDS is integrated with the AODV routing protocol to detect and eliminate selfish nodes within its transmission range.
Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.
Expert system of single magnetic lens using JESS in Focused Ion Beamijcsa
This work shows expert system of symmetrical single magnetic lens used in focused ion beam optical system. Java expert system shell(JESS) programming is proposed to build the intelligent agent "MOPTION"for getting an optimum magnetic flux density , and calculate the ion optical trajectory. The combination of such rule based engine and SIMION 8.1 has configured the reconstruction process and compiled the data retrieved by the proposed expert system agent to implement the pole-pieces reconstruction for lens design. The pole pieces reconstruction has been resulted in 3D graph , and under the infinite magnification conditions of the optical path, aberration (spherical / chromatic and total) disks diameters have been obtained and got the values (0.03,0.13 and 0.133) micron (μm) respectively.
NTRUSION D ETECTION S YSTEMS IN M OBILE A D H OC N ETWORKS : S TATE OF ...ijcsa
Mobile Ad Hoc Networks (MANETs) are more vulnerable
to different attacks. Prevention methods as
cryptographic techniques alone are not sufficient t
o make them secure; therefore, efficient intrusion
detection must be deployed and elaborated to facili
tate the identification of attacks. An Intrusion De
tection
System (IDS) aims to detect malicious and selfish n
odes in a network. The intrusion detection methods
used
normally for wired networks can no longer adequate
when adapted directly to a wireless ad-hoc network,
so existing techniques of intrusion detection have
to be changed and new techniques have to be determi
ned
to work efficiency and effectively in this new netw
ork architecture of MANETs. In this paper we give a
survey of different architectures and methods of in
trusion detection systems (IDSs) for MANETs
accordingly to the recent literature.
Front End Data Cleaning And Transformation In Standard Printed Form Using Neu...ijcsa
Front end of data collection and loading into database manually may cause potential errors in data sets and a very time consuming process. Scanning of a data document in the form of an image and recognition of corresponding information in that image can be considered as a possible solution of this challenge. This paper presents an automated solution for the problem of data cleansing and recognition of user written data to transform into standard printed format with the help of artificial neural networks. Three different neural models namely direct, correlation based and hierarchical have been developed to handle this issue. In a very hostile input environment, the solution is developed to justify the proposed logic.
A Novel Framework and Policies for On-line Block of Cores Allotment for Multi...ijcsa
Computer industry has widely accepted that future performance increases must largely come from increasing the number of processing cores on a die. This has led to NoC processors. Task scheduling is one of the most challenging problems facing parallel programmers today which is known to be NP-complete. A good principle is space-sharing of cores and to schedule multiple DAGs simultaneously on NoC processor. Hence the need to find optimal number of cores for a DAG for a particular scheduling method and further which region of cores on NoC, to be allotted for a DAG . In this work, a method is proposed to find near-optimal minimal block of cores for a DAG on a NoC processor. Further, a time efficient framework and three on-line block allotment policies to the submitted DAGs are experimented. The objectives of the policies, is to improve the NoC throughput. The policies are experimented on a simulator and found to deliver better performance than the policies found in literature..
A SURVEY OF S ENTIMENT CLASSIFICATION TECHNIQUES USED FOR I NDIAN REGIONA...ijcsa
Sentiment Analysis is a natural language processing
task that extracts sentiment from various text for
ms
and classifies them according to positive, negative
or neutral polarity. It analyzes emotions, feeling
s, and
the attitude of a speaker or a writer towards a con
text. This paper gives comparative study of various
sentiment classification techniques and also discus
ses in detail two main categories of sentiment
classification techniques these are machine based a
nd lexicon based. The paper also presents challenge
s
associated with sentiment analysis along with lexic
al resources available.
Parsing of xml file to make secure transaction in mobile commerceijcsa
This document summarizes research on parsing XML files to enable secure mobile commerce transactions. It discusses how parsing XML reduces its size, allowing data to be transmitted more quickly and securely during mobile transactions. The document reviews different XML parsing techniques, including DOM and SAX parsers. It also analyzes how different mobile operating systems, such as Android, Apple iOS, and Symbian, handle XML parsing. The goal of the research is to develop an efficient XML parsing method using J2ME to provide stronger security for mobile commerce transactions by reducing transmission delays and errors.
Multimodal authentication is one of the prime concepts in current applications of real scenario. Various
approaches have been proposed in this aspect. In this paper, an intuitive strategy is proposed as a
framework for providing more secure key in biometric security aspect. Initially the features will be
extracted through PCA by SVD from the chosen biometric patterns, then using LU factorization technique
key components will be extracted, then selected with different key sizes and then combined the selected key
components using convolution kernel method (Exponential Kronecker Product - eKP) as Context-Sensitive
Exponent Associative Memory model (CSEAM). In the similar way, the verification process will be done
and then verified with the measure MSE. This model would give better outcome when compared with SVD
factorization[1] as feature selection. The process will be computed for different key sizes and the results
will be presented.
AN ONTOLOGY FOR EXPLORING KNOWLEDGE IN COMPUTER NETWORKSijcsa
This document presents an ontology for exploring knowledge in computer networks that was developed using OWL format. Over 500 concepts related to various aspects of computer networks like scope, scale, topology, etc. were identified and classified. Relationships between concepts were analyzed and over 550 relationships between 33 types of relationships were identified. The ontology was implemented using the Protege tool and can help users search for concepts in the computer networks domain on semantic web applications.
Proposed Agent Based Black hole Node Detection Algorithm for Ad-Hoc Wireless...ijcsa
A Mobile ad-hoc network (MANET) is a latest and eme
rging Research topic among researchers. The
reason behind the popularity of MANET is flexibilit
y and independence of network infrastructure. MANET
has some unique characteristic like dynamic network
topology, limited power and limited bandwidth for
communication. MANET has more challenge compare to
any other conventional network. However the
dynamical network topology of MANETs, infrastructur
e-less property and lack of certificate authority m
ake
the security problems of MANETs need to pay more at
tention. This paper represents review of layer wise
security attacks. It also discussed the issues and
challenges of mobile ad hoc network. On the importa
nce of
security issues, this paper proposed intrusion dete
ction framework for detecting network layer threats
such
as black hole attack.
A ROBUST MISSING VALUE IMPUTATION METHOD MIFOIMPUTE FOR INCOMPLETE MOLECULAR ...ijcsa
This document presents a new method called MiFoImpute for imputing missing values in molecular descriptor datasets. MiFoImpute uses an iterative random forest approach. It is compared to 10 other imputation methods on two molecular descriptor datasets with varying percentages of artificially introduced missing values (10-30%). Experimental results show that MiFoImpute has competitive or better performance than other methods according to NRMSE and NMAE error metrics. It exhibits robustness to increasing levels of missing data and computational efficiency compared to some other methods.
The advancement in mobile technology and wireless network increase the using of mobile device in database
driven application, these application require high reliability and availability due to nature inheritance of
mobile environment, transaction is the center component in database systems, In this paper we present
useful work done in mobile transaction, we show the mobile database environment and overview a lot of
proposed model of mobile transaction and show many techniques used to enhance transaction execution.
USING ARTIFICIAL NEURAL NETWORK IN DIAGNOSIS OF THYROID DISEASE: A CASE STUDYijcsa
Nowadays, one of the main issues to create challenges in medicine sciences by developing technology is the
disease diagnosis with high accuracy. In the recent decades, Artificial Neural Networks (ANNs) are considered as the best solutions to achieve this goal and involve in widespread researches to diagnose the diseases. In this paper, we consider a Multi-layer Perceptron (MLP) ANN using back propagation learning algorithm to classify Thyroid disease. It consists of an input layer with 5 neurons, a hidden layer with 6 neurons and an output layer with just 1 neuron. The suitable selection of activation function and the number of neurons in the hidden layer and also the number of layers are achieved using test and error method. Our simulation results indicate that the performed optimization in MLP ANNs can be reached the accuracy level to 98.6%.
Modeling Call Holding Times Of Public Safety Networkijcsa
This document presents a study of modeling call holding times (CHT) for an IP-based public safety network in Mongolia. The study analyzes CHT data collected over a week from the network. Standard probability distributions like gamma, lognormal and Weibull are initially fitted to the CHT data but are not ideal fits. The document then proposes modeling CHT using a mixture of lognormal distributions. An expectation maximization algorithm is used to estimate the parameters for this mixture model. Results show the mixture of lognormal distributions provides a reasonable fit for modeling CHT in the public safety network.
Taking advantage of state of the art underwater vehicles and current networking capabilities, the visionary
double objective of this work is to “open to people connected to the Internet, an access to ocean depths
anytime, anywhere.” Today, these people can just perceive the changing surface of the sea from the shores,
but ignore almost everything on what is hidden. If they could explore seabed and become knowledgeable,
they would get involved in finding alternative solutions for our vital terrestrial problems – pollution,
climate changes, destruction of biodiversity and exhaustion of Earth resources. The second objective is to
assist professionals of underwater world in performing their tasks by augmenting the perception of the
scene and offering automated actions such as wildlife monitoring and counting. The introduction of Mixed
Reality and Internet in aquatic activities constitutes a technological breakthrough when compared with the
status of existing related technologies. Through Internet, anyone, anywhere, at any moment will be
naturally able to dive in real-time using a Remote Operated Vehicle (ROV) in the most remarkable sites
around the world. The heart of this work is focused on Mixed Reality. The main challenge is to reach real
time display of digital video stream to web users, by mixing 3D entities (objects or pre-processed
underwater terrain surfaces), with 2D videos of live images collected in real time by a teleoperated ROV.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
Inside Story Opens the Vaults on Corporate Reputation - May 2013Catherine Anderson
After more than 13 years' measuring corporate reputations we're pleased to share some of our research findings and insights focusing on a case study with global insurance company QBE. We show some of the key events which can negatively impact your corporate reputation, and how organisations can negate this with a good relationship with business journalists.
Idiopathic Subglottic and Tracheal Stenosis - A Survey of the Patient ExperienceCatherine Anderson
This document describes a survey of 160 patients with either acquired subglottic stenosis (AS, n=28) or idiopathic subglottic and tracheal stenosis (ISTS, n=132). The key findings were:
1) ISTS patients experienced longer delays in diagnosis compared to AS patients, with 58% of ISTS patients not receiving a diagnosis for over 18 months.
2) The most common treatments for both groups were balloon dilation and laser dilation, while tracheal resection was performed in 36% of both groups.
3) Patient satisfaction was significantly higher after tracheal resection (76% satisfaction) compared to other treatment modalities (39% satisfaction).
The document describes a new multi-vehicle trajectory generator (MTG) that can generate realistic vehicle encounter scenarios by encoding real encounter data into an interpretable latent space representation. The MTG uses a bi-directional encoder with GRU modules to extract temporal features from encounter trajectories, and a multi-branch decoder to separately generate multiple vehicle trajectories while sharing hidden states. A new disentanglement metric is proposed to evaluate deep generative models based on the variance of latent codes under different noise levels, without requiring classifiers. The MTG is shown to generate more rational vehicle encounters than baseline models and allow controlling encounters through the disentangled latent codes.
Solving real world delivery problem using improved max-min ant system with lo...ijaia
This paper presents a solution to real-world delive
ry problems (RWDPs) for home delivery services wher
e
a large number of roads exist in cities and the tra
ffic on the roads rapidly changes with time. The
methodology for finding the shortest-travel-time to
ur includes a hybrid meta-heuristic that combines a
nt
colony optimization (ACO) with Dijkstra’s algorithm
, a search technique that uses both real-time traff
ic
and predicted traffic, and a way to use a real-worl
d road map and measured traffic in Japan. We
previously proposed a hybrid ACO for RWDPs that use
d a MAX-MIN Ant System (MMAS) and proposed a
method to improve the search rate of MMAS. Since tr
affic on roads changes with time, the search rate i
s
important in RWDPs. In the current work, we combine
the hybrid ACO method with the improved MMAS.
Experimental results using a map of central Tokyo a
nd historical traffic data indicate that the propos
ed
method can find a better solution than conventional
methods.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
This document discusses using machine learning algorithms to predict traffic flow and reduce congestion at intersections. It compares linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor models on a UK road traffic dataset. All models performed well according to evaluation metrics, indicating they are suitable for an adaptive traffic light system. The system was implemented using a random forest regressor model and simulations showed it reduced traffic congestion by 30.8%, justifying its effectiveness.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
The document summarizes research on solving the optimal components assignment problem for a multistate network using fuzzy optimization. It discusses how the problem can be formulated as a fuzzy linear program by defining fuzzy membership functions for the objectives of maximizing reliability, minimizing total lead time, and minimizing total cost. The paper then proposes using a genetic algorithm combined with fuzzy linear programming to find component assignments that maximize the fuzzy objective membership degree.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
This document provides a review of fuzzy microscopic traffic flow models. It discusses how fuzzy logic can be used to model traffic flow and driver behavior by introducing uncertainty into variables like speed and headway. It describes fuzzy cellular automata models that represent traffic as vehicles characterized by fuzzy numbers for position and velocity. It also covers fuzzy logic car-following models that use linguistic terms and rules to model car-following behavior, and fuzzy route choice models that calculate possibility indexes to determine the most likely route. The goal of these fuzzy models is to more realistically simulate traffic flow and account for the imprecise nature of traffic data.
This document provides an overview of a student's assignment reviewing fuzzy microscopic traffic flow models. It discusses how fuzzy logic can be used to introduce uncertainty into traffic simulation models to better reflect real-world conditions. It reviews different types of fuzzy microscopic models, including fuzzy cellular models that use fuzzy numbers to represent vehicle parameters and transitions between time steps, and fuzzy logic car-following models that use fuzzy reasoning and linguistic terms to describe driver behavior. The goal is to understand how these fuzzy microscopic models work.
Route optimization problem using vehicle routing problem (VRP) and time window constraint is explained as finding paths for a finite count of vehicles to provide service to a huge number of customers and hence, optimizing the
path in a given duration of the time window. The vehicles in the loop have restricted intake of capacity. This path initiates from the depot, delivers the
goods, and stops at the depot. Each customer is to serve exactly once. If the arrival of the vehicle is before the time window “opens” or when the time window “closes,” there will be waiting for cost and late cost. The challenge involved over here is to scheduling visits to customers who are only
available during specific time windows. Ant colony optimization (ACO) algorithm is a meta-heuristic algorithm stimulated by the growing behaviour of real ants. In this paper, we combine the ACO algorithm with graph network henceforth increasing the number of vehicles in a particular depot for increasing the efficiency for timely delivery of the goods in a particular
time width. This problem is solved by, an efficient technique known as the ACO+graph algorithm.
Algorithms And Optimization Techniques For Solving TSPCarrie Romero
The document discusses three algorithms - simulated annealing, ant colony optimization, and genetic algorithm - for solving the traveling salesman problem (TSP). It analyzes each algorithm's approach, parameters used, and results of experiments on 15 and 50 randomly generated cities. Simulated annealing had average distances of 4.1341 and 20.1316 units for 15 and 50 cities respectively. Ant colony optimization yielded average distances of 3.9102 units for 15 cities, running faster than simulated annealing. Genetic algorithm was tested on 15 cities in Brazil.
Applied research. Optimization of the Shuttle ServicesRAMON RIOS
Application of the queuing theory to find out the root causes of the long waiting times in the company X. The verification of the outcome was with promodel simulation software. Networking using the shortest route to optimize even better. Forecasting to predict increasing in the population and get our life cycle. Gantt chart to calculate the total days and to track the gantt chart for any delays.
A Combined Method for Capacitated Periodic Vehicle Routing Problem with Stric...rahulmonikasharma
The paper develops a model for the optimal management of periodic deliveries of a given commodity with known capacity called Capacitated Periodic Vehicle Routing Problem (CPVRP). Due to the large number of customers, it is necessary to incorporate strict time windows, and pick-up and delivery in the periodic planning.. The goal is to schedule the deliveries according to feasible combinations of delivery days and to determine the the routing policies of the vehicles. The objective is to minimize the sum of the costs of all routes over the planning horizon. We model the problem as a large-scale linear mixed integer program and we propose a combined approach to solve the problem.
Heptagonal Fuzzy Numbers by Max Min MethodYogeshIJTSRD
In this paper, we propose another methodology for the arrangement of fuzzy transportation problem under a fuzzy environment in which transportation costs are taken as fuzzy Heptagonal numbers. The fuzzy numbers and fuzzy values are predominantly used in various fields. Here, we are converting fuzzy Heptagonal numbers into crisp value by using range technique and then solved by the MAX MIN method for the transportation problem. M. Revathi | K. Nithya "Heptagonal Fuzzy Numbers by Max-Min Method" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38280.pdf Paper URL: https://www.ijtsrd.com/mathemetics/applied-mathamatics/38280/heptagonal-fuzzy-numbers-by-maxmin-method/m-revathi
The document summarizes a proposed fuzzy logic-based joint space path planning system for a 3 degree-of-freedom robot manipulator. The system is composed of three separate fuzzy logic units that each control one of the manipulator joints. The inputs and outputs of each fuzzy block control the change in joint position for each time step. Simulation results show the robot is able to reach the goal configuration successfully using this approach. The fuzzy logic method is able to meet real-time requirements for robot motion planning without requiring an exact model of the robot.
Understanding the effect of Financing Variability Using Chance- Constrained A...IRJET Journal
1) The document discusses using chance-constrained programming (CCP) to model the impact of financing variability on time-cost tradeoffs in construction projects. CCP allows incorporating the probability of events into an optimization model.
2) Previous studies have used linear programming and other approaches to address time-cost tradeoffs but have not considered uncertainties like financing variability.
3) The study aims to develop a new mathematical model that comprehensively addresses precedence constraints, financing variability, and time-cost optimization for construction projects. CCP is used to quantify the effect of cost uncertainty from variable financing.
The location-routing problem is a relatively new branch of logistics system. Its objective is to determine a suitable location for constructing distribution warehouses and proper transportation routing from warehouse to the customer. In this study, the location-routing problem is investigated with considering fuzzy servicing time window for each customer. Another important issue in this regard is the existence of congested times during the service time and distributing goods to the customer. This caused a delay in providing service for customer and imposed additional costs to distribution system. Thus we have provided a mathematical model for designing optimal distributing system. Since the vehicle location-routing problem is Np-hard, thus a solution method using genetic meta-heuristic algorithm was developed and the optimal sequence of servicing for the vehicle and optimal location for the warehouses were determined through an example.
This document proposes an improved hybrid behavior ant colony algorithm to solve vehicle routing problems. It defines four types of ant behaviors - random, greedy, pheromone-based, and a hybrid behavior considering factors like distance, saving value, and vehicle load. The algorithm allows ants to select behaviors and routes probabilistically based on these factors. Simulation experiments on a 31-city dataset show the hybrid behavior outperforms basic ant colony and other variants, finding better solutions on average. The results demonstrate this improved algorithm can effectively solve vehicle routing problems.
Prediction of passenger train using fuzzy time series and percentage change m...journalBEEI
In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.
This paper evokes the vehicle routing problem (VRP) which aims to determine the minimum total cost
pathways for a fleet of heterogeneous vehicles to deliver a set of customers' orders. The inability of
optimization algorithms alone to fully satisfy the needs of logistic managers become obvious in
transportation field due to the spatial nature of such problems. In this context, we couple a geographical
information system (GIS) with a metaheuristic to handle the VRP efficiently then generate a geographical
solution instead of the numerical solution. A real-case instance in a Tunisian region is studied in order to
test the proposed approach.
Rides Request Demand Forecast- OLA BikeIRJET Journal
The document presents a study that develops a model to forecast demand for Ola bike rides in Bangalore, India using ride request data from Ola. The study uses clustering and machine learning techniques like XGBoost to predict demand for rides by time period and location. This will help Ola better understand demand patterns and maximize the efficiency of their bike fleet to meet rider needs. The model is trained on attributes from ride requests including booking time, pickup and drop off locations.
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Understanding Inductive Bias in Machine LearningSUTEJAS
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The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
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The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
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Integrating fuzzy and ant colony system for
1. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.5, October 2014
Integrating Fuzzy and Ant Colony System for
Fuzzy Vehicle Routing Problem with Time
Windows
Sandhya and V.Katiyar
Department of Computer Science and Engineering, Maharishi Markandeswar University,
Ambala, Haryana,India.
Abstract
In this paper fuzzy VRPTW with an uncertain travel time is considered. Credibility theory is used to model
the problem and specifies a preference index at which it is desired that the travel times to reach the
customers fall into their time windows. We propose the integration of fuzzy and ant colony system based
evolutionary algorithm to solve the problem while preserving the constraints. Computational results for
certain benchmark problems having short and long time horizons are presented to show the effectiveness of
the algorithm. Comparison between different preferences indexes have been obtained to help the user in
making suitable decisions
Keywords:
VRPTW, Fuzzy sets, Credibility theory, ant colony system, uncertainty, stochastic simulation.
1.Introduction
Transportation is one important component of logistics. Efficient utilization of vehicles directly
affects the logistic cost as 40% to 50% money is spent on transportation. Moreover proper
utilization of vehicles is also important from environmental view point. The use of automated
route planning and scheduling can lead to huge savings in transportation cost ranging from 5% to
20% [25]. So efficient routing of fleets is a crucial issue for companies and this can be formulated
as vehicle routing problem.Vehicle Routing Problem was first introduced by Danting and
Rameser [26] in 1959. Till now many variants of the problem have been proposed [2]. Vehicle
Routing Problem with Time Windows is one flavor of VRP. In VRPTW a least cost route from
central depot to all customers is to be designed in such a way that all customers are visited by
homogenous vehicles once and only once while preserving the capacity and time window
constraints. In this problem it is assumed that the time to travel from one customer to another is
equivalent to distance between the customers. Moreover in these types of problems, all
parameters are assumed to deterministic. In real life scenarios these assumptions donot hold
because of varying road conditions, link failures, rush hours, congestion etc. which lead to
uncertainties in data. Most of the algorithms developed for deterministic problems do not work in
these situations. In this paper VRPTW with fuzzy travel time is considered. This uncertainty is
handled using fuzzy logic. The fuzzy travel time is represented by triangular fuzzy number.
DOI:10.5121/ijcsa.2014.4506 73
2. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.5, October 2014
Credibility theory is used to model the problem. Improved Ant Colony System (IACS) [4] is then
used to find the most efficient route for the problem. The main goal of this paper is to develop an
algorithm that can provide the user with a route having minimum distance with uncertain travel
time at a desired preference index. The rest of the paper is organized as follows.Literature review
is presented in section 2. Section 3 discusses the essential basics of fuzzy theory. A model based
on credibility theory is then proposed in section 4. Section 5 presents improved ACS that is used
to solve the model. Results based on experimentations are discussed in section 6. Finally section
7 presents the conclusions and scope for future work.
74
2.Literature Review
VRP is concerned with finding the minimum set of routes, starting and ending at the central
depot, for homogenous vehicles to serve number of customers with demands for a goods such that
capacity constraint is preserved.Latest taxonomy on VRP can be found in [2]. The VRPTW
considers the time window for each customer in which service has to be provided. A vehicle can
arrive before the starting time of the window but it cannot arrive after the closing time of window.
A taxonomy can be found on [5,6].VRPTW is NP-hard problem. Many exact and metaheuristics
algorithms have been proposed for solving VRPTW [7,8,9,10]. A categorized bibliography of
metaheuristics and their extensions can be found in [1].But these became infeasible for dynamic
problems.In [11] a mixed integer linear programming approach and a nearest neighbor, branch
and cut algorithm is used to solve the problem for time dependent VRPTW.However this model
does not follow FIFO property.Ichoua et al [12] proposed a tabu search approach to solve
TDVRPTW, where customers are characterized by soft time windows. The model presented
satisfies the FIFO property.In another approach Donati et al. [13] use multi ant colony and local
search improvement approach to update the slack or the feasible time delays. The travel times are
analyzed by discretizing the time space thus satisfying the FIFO property. However these models
fail when uncertainties arises in various parameters.A detailed summary for various uncertain
parameters for VRPTW can be found in [19].Because of lack of data or due to extreme
complexity of the problem it requires subjective judgment. Fuzzy set theory provides meaningful
methodologies to handle uncertainty, vagueness and ambiguity.In [14] triangular fuzzy numbers
are used to represent fuzzy travel time and a route construction method is proposed to solve the
problem.A fuzzy optimization model using imperialist competitive algorithm is presented for
fuzzy vehicle routing problem with time window in [15].Cao Erbao et al [16] use fuzzy
credibility theory to model the vehicle routing problem with fuzzy demand. It uses integration of
stochastic simulation and differential evolution algorithms to solve the same model. However it
requires a lot of parameters to be taken care of.Yongshuang Zheng and Baodinf Liu [17] present
an integration of fuzzy simulation and genetic algorithms to design a hybrid intelligent algorithm
for solving fuzzy vehicle routing model. In [18] two new types of credibility programming
models including fuzzy chance constraint programming and fuzzy chance-constraint goal
programming are presented to model fuzzy VRP with fuzzy travel time. In [3] fuzzy concepts and
genetic algorithm are used for the solution multi-objective VRP. Finally J.Brito et al [20]
proposes a GRASP meta heuristic to solve the VRPTW in which travel time is uncertain. A
chance constraint model is build using credibility approach to solve the problem. However the
proposed algorithm appears to be inefficient because in GRASP metaheuristic each restart is
independent of the previous one.On the other hand the stochastic element of ACO allows to build
3. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.5, October 2014
variety of different solutions.In this paper VRPTW with fuzzy travel time is considered.
Credibility theory is used to build a chance constraint programming model for the problem. An
improved ant colony [4] metaheuristic is then used to obtain the optimal routes .Moreover using
this approach a decision maker can also evaluate different planning scenarios to choose the best
alternative with desired confidence level.
75
3.Fuzzy Credibility Theory
In this section, some basic concepts and results about fuzzy measure theory are summarized. The
term fuzzy logic was introduced by Lofti A. Zadeh [21]. In contrast to conventional logic, fuzzy
logic allows the intermediate values to be defined between conventional evaluations like true or
false. Fuzzy numbers are the numbers that possess fuzzy properties. In this paper we have
represented the fuzzy travel time between two customers as the triangular fuzzy number. A
triangular fuzzy number is represented by triplet TFN=(a, b, c) where b is the mean value (i.e.
mode) and and the left and right extremes of its spread. Its membership function is
=
4. 0
≤ ≤
≤ ≤
0
(1)
Zadeh [23] proposed the concept of possibility measure for fuzzy variables as a counterpart of
probability theory in crisp sets.Concept of the fuzzy set, fuzzy variable, possibility measure,
necessity theory are available in [22].Liu [24] proposed the credibility as the average of
possibility and necessity. Possibilityof an event is measured by most favorable cases only in
contrast to probability of an event where all favorable cases are measured. Let be nonempty set
and P () be the power set. Each element in P is called an event. Also denotes an empty set. In
order to present an axiomatic definition of possibility, it is necessary to assign a number Pos{A}
to each event A, which indicates the possibility that A will occur [16].
Axiom 3.1: Pos() =1 (Normality Axiom)
Axiom 3.2:Pos()=0 (Non negativity Axiom)
Axiom 3.3: For each
Pos !
#$ %= '()
#$ (Maximality Axiom)
Figure 2.1 shows the possibility of fuzzy event {X = x0}
Pos(X=x0)
x
5. International Journal on Computational Sciences Applications (IJCSA) Vol.4, No.5, October 2014
76
Figure 2.1 Possibility of fuzzy event
The explicit expression for possibility Pos(X=x0) is given by:
1 +
.
≤ + ≤
0+
Pos* ≤ + = ,
(2)
Necessity for an event A is defined as the impossibility of complement of that event Aci.e
Nec{A}=1-Pos {Ac}. Fig 2.2 shows the necessity of fuzzy event {X=x0}
1
Nec{X=x0}=1-Pos(X=x0 )
xo x
Figure 2.2 Necessity of fuzzy event
The explicit expression for possibility Nec(X=x0) is given by:
Nec* ≤ + = ,
1 +
.
≤ + ≤
0+
(3)
The credibility for event A is Cr{A}=1/2(Pos{A}+Nec{A}).}). The credibility measure signifies
the credibility how the solution satisfies the constraints. The explicit expression for credibility in
case of triangular fuzzy measure is:
Cr * ≤ + =
6. 1 +
./0
/ ≤ + ≤
.
/ ≤ + ≤
0+
(4)
4.Chance Constraint Model for Fuzzy VRPTW
We define a Fuzzy VRPTW as follows:
Given a set of n geographically distributed customers requiring services within a specific time
period from set of k homogenous vehicles stationed at a central depot with known demands of
customers having uncertain travel time between customers but lying within known ranges with
7. International Journal on Computational Sciences Applications (IJCSA) Vol.4, No.5, October 2014
most likely values being known then the objective of fuzzy VRPTW is to find the route with
minimum distance with specified confidence level that will meet all customers’ time windows
requirements assuming that:
1. Each vehicle starts and ends its tour at central depot indexed by 0.
2. Each vehicle has fixed identical capacity.
3. Each customer is to be visited once and only once by only one vehicle.
4. Each customer has a predefined time window in which it has to be served [ei , li].
5. The demand of the customer is fixed and is assumedthat it will not exceed the vehicle
77
capacity.
6. The travel time between each customer is not fixed and it is assumed to be expressed as
triangular fuzzy number 234 5
=(26$, 26/, 268).
Let D[i] be the departure time of vehicle v from customer i, then arrival time A[j]at j will be the
summation of departure time from previous customer i and the fuzzy travel time from node i to j.
A[j] = D[i] +234 9
(5)
The time to begin service will be maximum of arrival time or the opening time.
TS[j] =max (A[j], ej) (6)
Because of fuzzy travel time, arrival time and time to begin service at next customer will also be
fuzzy. However the service time S[i] and opening of the time windows e[i] are crisp numbers that
are special case of triangular fuzzy numbers where the three defining numbers are equal. We
obtain the credibility that the time to begin service at next customer does not exceed its close time
to be
Cr(TS[j]
≤
lj) =
8. 0:;6 =?@$
ABCD6@E
/∗CD 6@GCD6@E
:;6 ≥ =?@$IJ6 =?@/
AB/∗CD6@G0CD6@K
/∗CL6@KCD6@G
:;6 ≥ =?@/IJ6 =?@8
1:;6 ≥ =?@8
(7)
R6
As we know that if the travel time to next customer is smaller than its closing time then the
chances of serving that customer by that vehicle will grow. That is greater the difference between
the closing time and the maximum travel time greater is the chance to serve that customer and if
this difference is small that customer may not be served. Therefore, at some preference index Cr*,
the solution r verifies the fuzzy constraint of service times within the corresponding time window
if: Cr(TS[r]
lj). Cr*.The goal is to determine the value of MN∗ which will result in a route
having minimum corresponding ≤
distance. For this stochastic simulation is done. Thus the objective of
chance constraint model of fuzzy VRPTW using credibility theory is as follows:
Q Q #+
6
R
= O:I P P #+
P 6
SQ
#$ (8)
9. International Journal on Computational Sciences Applications (IJCSA) Vol.4, No.5, October 2014
78
subject to:
Q R
,6#+
RQ
P P 6
#$ =1 (9)
Q R6
#$ =1 and P +
P +6
Q R
#$ =1 (10)
Q R6
#+ - P 6 Q
P 6
R
#+ =0 (11)
R
P J ≤ TQ
#$ (12)
Q 234 5
#+ R
P P 6
+ )6 + V2
R6
#+ ≤ (13)
R6
P P MN=N@ ≤ 6
#+
RW
#$ MN∗ (14)
Following indices and notations are considered:
• i=0,1,2,…,n are the customer indexes with 0 denoting base station.
• k=1,2,…,m are the vehicles.
• di is the demand of the customer.
• Qk is the capacity of the vehicle.
• cij cost of moving from node i to j, expressed in terms of distance from customer i to j.
• sij is the service time at customer i.
• [ei, li] is the time window of customer i.
• tij is the fuzzy travel time between i and j.
• wtiis the waiting time at customer i.
• TS[i] is the fuzzy time to begin service.
Q is introduced such that 6
A binary variable 6
Q =1 if vehicle k travels directly from customer i to
customer j and otherwise 0. The objective function (8) seeks to minimize the total traveled
distance whereas constraint (9), specifies that every customer is visited by one vehicle and splitting
of deliveries are forbidden.Eq (10), states that each tour starts and ends at depot indexed 0.
Capacity constraint is preserved in Eq (12) and it ensure that demand of each customer does not
exceeds vehicle capacity.Eq. (13), preserves the constraints and ensuring that sum of fuzzy
travelling time to the customer, service time and waiting time are less than the closing time of
customer’s window. Eq (14), preserves time to begin service for route r is within a specified
preference index.
5.Proposed Solution Approach
In this section, first, stochastic simulation will be used to calculate the total distance. Then ant
colony heuristic is used to obtain the least cost route plan with the best value of dispatcher
preference index Cr*.
10. International Journal on Computational Sciences Applications (IJCSA) Vol.4, No.5, October 2014
79
a.Stochastic Simulation for additional distance
In this paper, as travel time between each customer is uncertain and they are represented as
triangular fuzzy numbers, so algorithms for deterministic problems cannot be applied for the fuzzy
VRPTW. Moreover real values of the travel time will be known after reaching the customer.
However uncertain travel time can be considered deterministic by stochastic simulation. We
summarize the algorithm as follows:
Step 1: Simulate actual travel time for each customer by following process:
Step 1.1: Generate a random number x within the left and right boundaries of a triangular
fuzzy travel time between each customer and calculate its membership u(x).
Step 1.2: Generate a random number r ∈ 0,1.
Step 1.3: Compare r and u(x): if ru(x), use x as the actual travel time, otherwise generate x
and r again and compare them until they satisfy the condition ru(x).
Step 2: Calculate total distance by moving along the planned route.
Step 3: Repeat step 1 and 2 N times and calculate the average total distance.
b.Route Construction using Ant Colony Optimization
To obtain the best solution, enhance ant colony optimization of [4] is applied. The algorithm works
as under.
Step 1: Initialize the pheromone matrix Z6 = 1 6 [ and place O ants at depot and the set of
customers as unvisited.
Step 2: Repeat while all the customers are not marked as visited.
Step 2.1 Start an ant and mark depot as the current location.
Step 2.2 From the current location (i) choose the customer (j) to be visited by a pseudo-random
proportional rule given by:
? = N]O ^_Z6`a. _b6`cd ,:;N ≤ N+
e(2ℎgNV:)g
(15)
whereN ∈ 0,1@is a uniformly generated random number, and b6is the visibility of customer j
and defined by
b6 = 1
hM6 + V26ij + h6 − g6 m il@ (16)
Here M6 is the cost of travelling from node i to j which is distance from node i to j. In our case as
travel time are uncertain and their values will be known after reaching the customers so it may be
possible that vehicle arrives at acustomer but cannot serve it because of expiry of time window.
11. International Journal on Computational Sciences Applications (IJCSA) Vol.4, No.5, October 2014
80
AlsoV26 = n
g6 − 6:;g6 6
0(2ℎgNV:)g
is the waiting time at location j before service can be started
and6 − 6, 6 6, i.e. the difference between the latest arrival time ljand actual arrival time aj at
customer j is the measure of urgency of customer j to be served.
)(e ∈ oQis the customer selected according to the probability pq given by
pq = _rsB`t._usB`v
P _rsB`t._usB`v
B∈wx
(17)
yzis the set of customers which will be successfully visited from the current location by the same
vehicle without violating the following time and capacity constraints.
(i) Time constraints: 6 ≤ 6i.e. arrival at {|}customer must be up to the closing
time6 of that customer with
6 = O6$, g6$ + =6$ + 26$,6and
lj). Cr* (18)
Cr(TS[r]
≤
Q J6
(ii) Capacity constraints:h6
Q ≤ TQi (19)
Step 2.3 Mark (j) as the current location and update the set of unvisited customers~ →
~– ?. Also update capacity and current time of the ant k.
Step 2.4 From the current location again findoQ. If oQ = ~‚ then go to 2.1 else
repeat the 2.2.
Step 3: Out of Opaths find the best path to be followed with minimum total travelled distance and
further try for improving that route by applying local search.
Step 4: Update the pheromone matrix by the global pheromone updation rule:
…Q
Z6 = 1 − ƒZ6 + P ΔZ6
#$ (20)
whereƒ ∈ 0, 1is a constant that controls the speed of evaporation of pheromone. †is number of
routes in the current best solution. The deposited pheromone ΔZ6on the links is given byΔZ6 =
T/ˆ. Here T is the constant, ˆ is the tour length of current solution.
Step 5: Repeat the step 2, 3 for desired number of iteration and provide the best obtained solution.
6.Computational Experience
The proposed algorithm has been encoded in MATLAB 8.0. This study uses the dataset of J.Brito
et al [20] which was generated from the example of Zheng and Liu [17]. Two types of
12. International Journal on Computational Sciences Applications (IJCSA) Vol.4, No.5, October 2014
experimental conditions based on the time windows are generated. We assume that there are 18
customers with short time horizons and with long time horizons. Customer labeled 0 is assumed
as depot. In each experiment demand for each customer, distance and fuzzy travel time between
each customer is same as that of J.Brito et al. Start time and the closing time for each customer in
long time horizon is assumed to be same but for dataset with short time period the total opening
duration is assumed to be 100 for each customer. The relative parameters and their values that
were used during implementation are listed in table6.1 .
81
Table 6.1 Simulation Parameters
Sr. No. Simulation Parameter Parameter Value
1 Number of customers 18
2 Number of iterations 1000
3 Capacity of Vehicles(Q) 1000
4 Initial Pheromone value for all arcs 1
5 Α 2.5
6 Β .7
7 Γ .5
8 Ρ 0.2
9 q0 0.5
10 No. of Ants 11
11 Credibility .8
12 Depot Close time 5000
13 Service Time 15
Comparison of results obtained by our algorithm,J.Brito[20] and Zheng et al [17] on various
parameters are presented in table 6.2
13. International Journal on Computational Sciences Applications (IJCSA) Vol.4, No.5, October 2014
82
Table 6.2 Comparison Table
Factors Zheng et al[17] J.Brito et al[20] Our Approach
Meta Heuristics Genetic GRASP ANT COLONY SYSTEM
Total Iterations 10,000 - 1000
Time
Consumed
10 hours 1 minute 3 minutes
Vehicle used 4 3 3
Total Distance 457.5 365.5 373
Vehicle Routes R1:0-16-17-18-5-0
R2:0-10-12-13-14-15-8-
0
R3:0-1-2-3-0
R4:0-9-6-7-11-4-0
R1:0-17-18-16-15-
14-12-13-0
R2:0-2-1-3-4-6-8-0
R3:0-10-9-11-7-5-0
R1:0-1- 2-3-18-17-13– 8-0
R2:0-4-5-6-10-11-7-9-0
R3:0-16-15-14-12-0
Loads of
Vehicles
590,815,440,640 795,930,760 955,840,550
Robust Less More More
One can observe that our algorithm produces effective results than Zheng et al [17]and
comparable results with GRASP. Moreover proposed algorithm is more robust than [17] in terms
of utilization of vehicles. To evaluate the importance of dispatcher preference Cr varied with the
interval of 0 to 1 with a step of 0.1. The average computational results of 10 times are given in fig
6.1
Influence of prefrence index
Fig 6.1 Influence of preference index
Aggregated Cost
Cr
14. International Journal on Computational Sciences Applications (I
JCSA) From above figure on can conclude that if decision maker is risk lover
of Cr whereas if he is risk adverse he will go for higher values with plan having higher cost.
Figure 6.2 shows the effect of fuzzy travel time on time windows for long duration problems with
a confidence level of 0.8.
Time
Missed Time Windows
customers
Arrival_Time Close_Time
Fig 6.2 Missed time windows for long horizon problems
One can note that if we are travelling with the same fuzzy s
served within their time windows. But
suffered as shown in fig 6.3. For example for customer 4 and 5 the closing time window is around
900 but the actual arrival time of the vehicle is
Time
Missed Time Windows at (.3)
Fig 6.3 Missed time w
Customers
A_T (.3) C_T
If we set the Cr=0.5 every customer can be served within it
IJCSA) Vol.4, No.5, October 2014
it will choose lower values
r with Cr=0.8
speed as given in [19] all customers are
for short horizon problems if Cr=0.4 some customers are
. around 1000.
windows for short problems with Cr=0.3
r=its time boundaries as shown in fig
83
] r=s fig6.4
15. International Journal on Computational Sciences Applications (I
IJCSA) Vol.4, No.5, October 2014
Missed Time Windows at (0.5)
Fig 6.4 Missed time windows for short problems wi
Time
7.Conclusions
with Cr=0.5
Deterministic assumptions of the VRPTW make it unsuitable for real world environment.
paper, VRPTW with fuzzy travel time is
to construct efficient and reliable routes for the problem.
for the problem using fuzzy credibility theory
fuzzy number. Additionally, stochastic simulation is done to get the fuzzy travel time and then ant
colony optimization algorithm is used to get the optimal solution for the problem in reasonable
time. We apply ly our solution approach to problems having short and long duration of time
windows. It was concluded that
problem and provides improved results on
approach.Further comparison between different confidence levels is done to show the influence
on total distance and it was concluded that higher values of preference index leads to higher cost.
This comparison helps a decision maker
different results based on cheapness and robustness
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We propose a chance constrained model
where fuzzy travel time is represented as triangular
tochastic the proposed approach performs well in each structure of the
[17] and comparable results with [20
20]
in choosing different values for confidence level to get
robustness.
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