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.
FOLLOWING CAR ALGORITHM WITH MULTI AGENT RANDOMIZED SYSTEMijcsit
We present a new Following Car Algorithm in Microscopic Urban Traffic Models which integrates some real-life factors that need to be considered, such as the effect of random distributions in the car speed,acceleration, entry of lane… Our architecture is based on Multi-Agent Randomized Systems (MARS) developed in earlier publications
Options on Quantum Money: Quantum Path- Integral With Serial ShocksAM Publications,India
The author previously developed a numerical multivariate path-integral algorithm, PATHINT, which has been applied to several classical physics systems, including statistical mechanics of neocortical interactions, options in financial markets, and other nonlinear systems including chaotic systems. A new quantum version, qPATHINT, has the ability to take into account nonlinear and time-dependent modifications of an evolving system. qPATHINT is shown to be useful to study some aspects of serial changes to systems. Applications to options on quantum money and blockchains in financial markets are discussed.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
Exploring Queuing Theory to Minimize Traffic Congestion Problem in Calabar-Hi...Premier Publishers
Traffic congestion has been a serious problem that drivers are facing especially in Calabar – highway by IBB road intersection. In this paper, emphasis is placed on model formation and derivation of some parameters that will help to facilitate the flow of vehicles in this intersection to reduce traffic congestion. The channel considered in this research is multiple queue single servers. We derived variance waiting time of vehicles in the queue and in the system, expected number of vehicles in the queue and in the system waiting for service, expected waiting time of vehicles in the queue and in the system. We also determine the time each vehicle spends in the queue waiting for service and the mean queue length for all the channels in each section. The result shows fair traffic congestion in Calabar – highway by IBB road intersection especially in the morning and evening hours for all the locations.
P REDICTION F OR S HORT -T ERM T RAFFIC F LOW B ASED O N O PTIMIZED W...ijcsit
Short term traffic forecasting has been a very impo
rtant consideration in many areas of transportation
research for more than 3 decades. Short-term traffi
c forecasting based on data driven methods is one o
f the
most dynamic and developing research arenas with en
ormous published literature. In order to improve
forecasting model accuracy of wavelet neural networ
k, an adaptive particle swarm optimization algorith
m
based on cloud theory was proposed, not only to hel
p improve search performance, but also speed up
individual optimizing ability. And the inertia weig
ht adaptively changes depending on X-conditional cl
oud
generator which has the stable tendency and randomn
ess property .Then the adaptive particle swarm
optimization algorithm based on cloud theory was us
ed to optimize the weights and thresholds of wavele
t
BP neural network, Instead of traditional gradient
descent method . At last, wavelet BP neural network
was
trained to search for the optimal solution. Based o
n above theory, an improved wavelet neural network
model based on modified particle swarm optimization
algorithm was proposed and the availability of the
modified prediction method was proved by predicting
the time series of real traffic flow. At last, the
computer simulations have shown that the nonlinear
fitting and accuracy of the modified prediction
methods are better than other prediction methods.
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.
FOLLOWING CAR ALGORITHM WITH MULTI AGENT RANDOMIZED SYSTEMijcsit
We present a new Following Car Algorithm in Microscopic Urban Traffic Models which integrates some real-life factors that need to be considered, such as the effect of random distributions in the car speed,acceleration, entry of lane… Our architecture is based on Multi-Agent Randomized Systems (MARS) developed in earlier publications
Options on Quantum Money: Quantum Path- Integral With Serial ShocksAM Publications,India
The author previously developed a numerical multivariate path-integral algorithm, PATHINT, which has been applied to several classical physics systems, including statistical mechanics of neocortical interactions, options in financial markets, and other nonlinear systems including chaotic systems. A new quantum version, qPATHINT, has the ability to take into account nonlinear and time-dependent modifications of an evolving system. qPATHINT is shown to be useful to study some aspects of serial changes to systems. Applications to options on quantum money and blockchains in financial markets are discussed.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
Exploring Queuing Theory to Minimize Traffic Congestion Problem in Calabar-Hi...Premier Publishers
Traffic congestion has been a serious problem that drivers are facing especially in Calabar – highway by IBB road intersection. In this paper, emphasis is placed on model formation and derivation of some parameters that will help to facilitate the flow of vehicles in this intersection to reduce traffic congestion. The channel considered in this research is multiple queue single servers. We derived variance waiting time of vehicles in the queue and in the system, expected number of vehicles in the queue and in the system waiting for service, expected waiting time of vehicles in the queue and in the system. We also determine the time each vehicle spends in the queue waiting for service and the mean queue length for all the channels in each section. The result shows fair traffic congestion in Calabar – highway by IBB road intersection especially in the morning and evening hours for all the locations.
P REDICTION F OR S HORT -T ERM T RAFFIC F LOW B ASED O N O PTIMIZED W...ijcsit
Short term traffic forecasting has been a very impo
rtant consideration in many areas of transportation
research for more than 3 decades. Short-term traffi
c forecasting based on data driven methods is one o
f the
most dynamic and developing research arenas with en
ormous published literature. In order to improve
forecasting model accuracy of wavelet neural networ
k, an adaptive particle swarm optimization algorith
m
based on cloud theory was proposed, not only to hel
p improve search performance, but also speed up
individual optimizing ability. And the inertia weig
ht adaptively changes depending on X-conditional cl
oud
generator which has the stable tendency and randomn
ess property .Then the adaptive particle swarm
optimization algorithm based on cloud theory was us
ed to optimize the weights and thresholds of wavele
t
BP neural network, Instead of traditional gradient
descent method . At last, wavelet BP neural network
was
trained to search for the optimal solution. Based o
n above theory, an improved wavelet neural network
model based on modified particle swarm optimization
algorithm was proposed and the availability of the
modified prediction method was proved by predicting
the time series of real traffic flow. At last, the
computer simulations have shown that the nonlinear
fitting and accuracy of the modified prediction
methods are better than other prediction methods.
A New Method to Solving Generalized Fuzzy Transportation Problem-Harmonic Mea...AI Publications
Transportation Problem is one of the models in the Linear Programming problem. The objective of this paper is to transport the item from the origin to the destination such that the transport cost should be minimized, and we should minimize the time of transportation. To achieve this, a new approach using harmonic mean method is proposed in this paper. In this proposed method transportation costs are represented by generalized trapezoidal fuzzy numbers. Further comparative studies of the new technique with other existing algorithms are established by means of sample problems.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared
Time of arrival based localization in wireless sensor networks a non linear ...sipij
In this paper, we aim to obtain the location information of a sensor node deployed in a Wireless Sensor Network (WSN). Here, Time of Arrival based localization technique is considered. We calculate the position information of an unknown sensor node using the non- linear techniques. The performances of the techniques are compared with the Cramer Rao Lower bound (CRLB). Non-linear Least Squares and the Maximum Likelihood are the non-linear techniques that have been used to estimate the position of the unknown sensor node. Each of these non-linear techniques are iterative approaches, namely, Newton
Raphson estimate, Gauss Newton Estimate and the Steepest Descent estimate for comparison. Based on the
results of the simulation, the approaches have been compared. From the simulation study, Localization
based on Maximum Likelihood approach is having higher localization accuracy.
Genetic Algorithm For The Travelling Salesman Problem using Enhanced Sequenti...CSCJournals
Traveling Salesman Problem (TSP) is one of the most important combinatorial optimization problems. There are many researches to improve the genetic algorithm for solving TSP. The Sequential Constructive crossover (SCX) is one of the most efficient crossover operators for solving optimization problems. In this paper, we propose a new crossover operator, named Enhanced Sequential Constructive crossover operator (ESCX), which modifies and improves the criteria of SCX operator in construction of offspring. ESCX considers, in addition to the real cost of the traversed cities, an estimation cost of the remaining tour, and it selects the next node to build the offspring based on that evaluation. The experimental results comparing the proposed crossover operator to the SCX operator on some benchmark TSPLIB instances show the effectiveness of our proposed operator.
Routing in Wireless Mesh Networks: Two Soft Computing Based Approachesijmnct
Due to dynamic network conditions, routing is the most critical part in WMNs and needs to be optimised.
The routing strategies developed for WMNs must be efficient to make it an operationally self configurable
network. Thus we need to resort to near shortest path evaluation. This lays down the requirement of some
soft computing approaches such that a near shortest path is available in an affordable computing time. This
paper proposes a Fuzzy Logic based integrated cost measure in terms of delay, throughput and jitter.
Based upon this distance (cost) between two adjacent nodes we evaluate minimal shortest path that updates
routing tables. We apply two recent soft computing approaches namely Big Bang Big Crunch (BB-BC) and
Biogeography Based Optimization (BBO) approaches to enumerate shortest or near short paths. BB-BC
theory is related with the evolution of the universe whereas BBO is inspired by dynamical equilibrium in
the number of species on an island. Both the algorithms have low computational time and high convergence
speed. Simulation results show that the proposed routing algorithms find the optimal shortest path taking
into account three most important parameters of network dynamics. It has been further observed that for
the shortest path problem BB-BC outperforms BBO in terms of speed and percent error between the
evaluated minimal path and the actual shortest path.
Multi-Index Bi-Criterion Transportation Problem: A Fuzzy ApproachIJAEMSJORNAL
This paper represents a non linear bi-criterion generalized multi-index transportation problem (BGMTP) is considered. The generalized transportation problem (GTP) arises in many real-life applications. It has the form of a classical transportation problem, with the additional assumption that the quantities of goods change during the transportation process. Here the fuzzy constraints are used in the demand and in the budget. An efficient new solution procedure is developed keeping the budget as the first priority. All efficient time-cost trade-off pairs are obtained. D1-distance is calculated to each trade-off pair from the ideal solution. Finally optimum solution is reached by using D1-distance.
Transportation Problem (TP) is an important network structured linear programming problem that arises in several contexts and has deservedly received a great deal of attention in the literature. The central concept in this problem is to find the least total transportation cost of a commodity in order to satisfy demands at destinations using available supplies at origins in a crisp environment. In real life situations, the decision maker may not be sure about the precise values of the coefficients belonging to the transportation problem. The aim of this paper is to introduce a formulation of TP involving Triangular fuzzy numbers for the transportation costs and values of supplies and demands. We propose a two-step method for solving fuzzy transportation problem where all of the parameters are represented by non-negative triangular fuzzy numbers i.e., an Interval Transportation Problems (TPIn) and a Classical Transport Problem (TP). Since the proposed approach is based on classical approach it is very easy to understand and to apply on real life transportation problems for the decision makers. To illustrate the proposed approach two application examples are solved. The results show that the proposed method is simpler and computationally more efficient than existing methods in the literature.
A Generic Agent Model Towards Comparing Resource Allocation Approaches to On-...daoudalaa
A talk at OptLearnMAS-21
The 12th Workshop on Optimization and Learning in Multiagent Systems held at AAMAS 2021
abstract:
Allocation problems are major issues in managing On-Demand Transport (ODT) systems. They have been studied for decades, and a variety of solutions were proposed. The approaches to these problems can be classified as centralized and decentralized. Each has its pros and cons in practice.
In this work, we aim to provide a generic model for the problem of online on-demand transport (ODT) with autonomous vehicles and a multi-agent model dedicated to resource allocation and scheduling in vehicle fleets. This generic model supports the processing of different allocation mechanisms, and considers autonomous vehicles that communicate via peer-to-peer radio channels to meet passenger requirements and satisfy trip requests in an online ODT system.
We validate this model's genericity by applying several allocation mechanisms (mathematical programming, greedy heuristic, distributed constraint optimization, and auctions) and compare their performance on synthetic scenarios in a real-world city map
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONSijscmcj
The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BY DOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition - O(n3), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.
Algorithm Finding Maximum Concurrent Multicommodity Linear Flow with Limited ...IJCNCJournal
Graphs and extended networks are is powerful mathematical tools applied in many fields as transportation,
communication, informatics, economy, … Algorithms to find Maximum Concurrent Multicommodity Flow
with Limited Cost on extended traffic networks are introduced in the works we did. However, with those
algorithms, capacities of two-sided lines are shared fully for two directions. This work studies the more
general and practical case, where flows are limited to use two-sided lines with a single parameter called
regulating coefficient. The algorithm is presented in the programming language Java. The algorithm is
coded in programming language Java with extended network database in database management system
MySQL and offers exact results.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Modelling dynamic patterns using mobile datacsandit
Understanding, modeling and simulating human mobility among urban regions is very challengeable
effort. It is very important in rescue situations for many kinds of events, either in the indoor events like
evacuation of buildings or outdoor ones like public assemblies, community evacuation, in exigency
situations there are several incidents could be happened, the overcrowding causes injuries and death
cases, which are emerged during emergency situations, as well as it serves urban planning and smart
cities. The aim of this study is to explore the characteristics of human mobility patterns, and model them
mathematically depending on inter-event time and traveled distances (displacements) parameters by using
CDRs (Call Detailed Records) during Armada festival in France. However, the results of the numerical
simulation endorse the other studies findings in that the most of real systems patterns are almost follows an
exponential distribution. In the future the mobility patterns could be classified according (work or off)
days, and the radius of gyration could be considered as effective parameter in modelling human mobility
Robust Watermarking through Dual Band IWT and Chinese Remainder TheoremjournalBEEI
CRT was a widely used algorithm in the development of watermarking methods. The algorithm produced good image quality but it had low robustness against compression and filtering. This paper proposed a new watermarking scheme through dual band IWT to improve the robustness and preserving the image quality. The high frequency sub band was used to index the embedding location on the low frequency sub band. In robustness test, the CRT method resulted average NC value of 0.7129, 0.4846, and 0.6768 while the proposed method had higher NC value of 0.7902, 0.7473, and 0.8163 in corresponding Gaussian filter, JPEG, and JPEG2000 compression test. Meanwhile the both CRT and proposed method had similar average SSIM value of 0.9979 and 0.9960 respectively in term of image quality. The result showed that the proposed method was able to improve the robustness and maintaining the image quality.
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.
A study and implementation of the transit route network design problem for a ...csandit
The design of public transportation networks presup
poses solving optimization problems,
involving various parameters such as the proper mat
hematical description of networks, the
algorithmic approach to apply, and also the conside
ration of real-world, practical
characteristics such as the types of vehicles in th
e network, the frequencies of routes, demand,
possible limitations of route capacities, travel de
cisions made by passengers, the environmental
footprint of the system, the available bus technolo
gies, besides others. The current paper
presents the progress of the work that aims to stud
y the design of a municipal public
transportation system that employs middleware techn
ologies and geographic information
services in order to produce practical, realistic r
esults. The system employs novel optimization
approaches such as the particle swarm algorithms an
d also considers various environmental
parameters such as the use of electric vehicles and
the emissions of conventional ones.
A STUDY AND IMPLEMENTATION OF THE TRANSIT ROUTE NETWORK DESIGN PROBLEM FOR A ...cscpconf
The design of public transportation networks presupposes solving optimization problems,
involving various parameters such as the proper mathematical description of networks, the
algorithmic approach to apply, and also the consideration of real-world, practical
characteristics such as the types of vehicles in the network, the frequencies of routes, demand,
possible limitations of route capacities, travel decisions made by passengers, the environmental
footprint of the system, the available bus technologies, besides others. The current paper
presents the progress of the work that aims to study the design of a municipal public
transportation system that employs middleware technologies and geographic information
services in order to produce practical, realistic results. The system employs novel optimization
approaches such as the particle swarm algorithms and also considers various environmental
parameters such as the use of electric vehicles and the emissions of conventional ones.
Vehicle route scheduling and transportation cost minimization in a latex indu...IJRES Journal
The vehicle route scheduling problem is concerned with the determination of routes and schedules for a fleet of vehicles to satisfy the demands of a set of customers. The goal of vehicle routing is to schedule multiple suppliers from various places. Vehicle routing has existed since the advent of the Industrial age, when large-scale production became possible. As the complexity and scale of the manufacturing world increased, the task of optimizing vehicle routing grew. The vehicle routing problem is a combinatorial optimization and integer programming problem seeking to service a number of customers with a fleet of vehicles. Often the context is that of delivering goods located at a central depot to customers who have placed orders for such goods or vice-versa. Implicit is the goal of minimizing the cost of distributing the goods. Many methods have been developed for searching for good solutions to the problem, however even for the smallest problems, finding global minimum for the cost function is computationally complex. The paper presents an optimization algorithm using Particle Swarm Optimization (PSO) for the vehicle routing that would enable the logistic manager of a latex industry to minimize the transportation cost and maximize the collection using minimum number of vehicles.
A New Method to Solving Generalized Fuzzy Transportation Problem-Harmonic Mea...AI Publications
Transportation Problem is one of the models in the Linear Programming problem. The objective of this paper is to transport the item from the origin to the destination such that the transport cost should be minimized, and we should minimize the time of transportation. To achieve this, a new approach using harmonic mean method is proposed in this paper. In this proposed method transportation costs are represented by generalized trapezoidal fuzzy numbers. Further comparative studies of the new technique with other existing algorithms are established by means of sample problems.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared
Time of arrival based localization in wireless sensor networks a non linear ...sipij
In this paper, we aim to obtain the location information of a sensor node deployed in a Wireless Sensor Network (WSN). Here, Time of Arrival based localization technique is considered. We calculate the position information of an unknown sensor node using the non- linear techniques. The performances of the techniques are compared with the Cramer Rao Lower bound (CRLB). Non-linear Least Squares and the Maximum Likelihood are the non-linear techniques that have been used to estimate the position of the unknown sensor node. Each of these non-linear techniques are iterative approaches, namely, Newton
Raphson estimate, Gauss Newton Estimate and the Steepest Descent estimate for comparison. Based on the
results of the simulation, the approaches have been compared. From the simulation study, Localization
based on Maximum Likelihood approach is having higher localization accuracy.
Genetic Algorithm For The Travelling Salesman Problem using Enhanced Sequenti...CSCJournals
Traveling Salesman Problem (TSP) is one of the most important combinatorial optimization problems. There are many researches to improve the genetic algorithm for solving TSP. The Sequential Constructive crossover (SCX) is one of the most efficient crossover operators for solving optimization problems. In this paper, we propose a new crossover operator, named Enhanced Sequential Constructive crossover operator (ESCX), which modifies and improves the criteria of SCX operator in construction of offspring. ESCX considers, in addition to the real cost of the traversed cities, an estimation cost of the remaining tour, and it selects the next node to build the offspring based on that evaluation. The experimental results comparing the proposed crossover operator to the SCX operator on some benchmark TSPLIB instances show the effectiveness of our proposed operator.
Routing in Wireless Mesh Networks: Two Soft Computing Based Approachesijmnct
Due to dynamic network conditions, routing is the most critical part in WMNs and needs to be optimised.
The routing strategies developed for WMNs must be efficient to make it an operationally self configurable
network. Thus we need to resort to near shortest path evaluation. This lays down the requirement of some
soft computing approaches such that a near shortest path is available in an affordable computing time. This
paper proposes a Fuzzy Logic based integrated cost measure in terms of delay, throughput and jitter.
Based upon this distance (cost) between two adjacent nodes we evaluate minimal shortest path that updates
routing tables. We apply two recent soft computing approaches namely Big Bang Big Crunch (BB-BC) and
Biogeography Based Optimization (BBO) approaches to enumerate shortest or near short paths. BB-BC
theory is related with the evolution of the universe whereas BBO is inspired by dynamical equilibrium in
the number of species on an island. Both the algorithms have low computational time and high convergence
speed. Simulation results show that the proposed routing algorithms find the optimal shortest path taking
into account three most important parameters of network dynamics. It has been further observed that for
the shortest path problem BB-BC outperforms BBO in terms of speed and percent error between the
evaluated minimal path and the actual shortest path.
Multi-Index Bi-Criterion Transportation Problem: A Fuzzy ApproachIJAEMSJORNAL
This paper represents a non linear bi-criterion generalized multi-index transportation problem (BGMTP) is considered. The generalized transportation problem (GTP) arises in many real-life applications. It has the form of a classical transportation problem, with the additional assumption that the quantities of goods change during the transportation process. Here the fuzzy constraints are used in the demand and in the budget. An efficient new solution procedure is developed keeping the budget as the first priority. All efficient time-cost trade-off pairs are obtained. D1-distance is calculated to each trade-off pair from the ideal solution. Finally optimum solution is reached by using D1-distance.
Transportation Problem (TP) is an important network structured linear programming problem that arises in several contexts and has deservedly received a great deal of attention in the literature. The central concept in this problem is to find the least total transportation cost of a commodity in order to satisfy demands at destinations using available supplies at origins in a crisp environment. In real life situations, the decision maker may not be sure about the precise values of the coefficients belonging to the transportation problem. The aim of this paper is to introduce a formulation of TP involving Triangular fuzzy numbers for the transportation costs and values of supplies and demands. We propose a two-step method for solving fuzzy transportation problem where all of the parameters are represented by non-negative triangular fuzzy numbers i.e., an Interval Transportation Problems (TPIn) and a Classical Transport Problem (TP). Since the proposed approach is based on classical approach it is very easy to understand and to apply on real life transportation problems for the decision makers. To illustrate the proposed approach two application examples are solved. The results show that the proposed method is simpler and computationally more efficient than existing methods in the literature.
A Generic Agent Model Towards Comparing Resource Allocation Approaches to On-...daoudalaa
A talk at OptLearnMAS-21
The 12th Workshop on Optimization and Learning in Multiagent Systems held at AAMAS 2021
abstract:
Allocation problems are major issues in managing On-Demand Transport (ODT) systems. They have been studied for decades, and a variety of solutions were proposed. The approaches to these problems can be classified as centralized and decentralized. Each has its pros and cons in practice.
In this work, we aim to provide a generic model for the problem of online on-demand transport (ODT) with autonomous vehicles and a multi-agent model dedicated to resource allocation and scheduling in vehicle fleets. This generic model supports the processing of different allocation mechanisms, and considers autonomous vehicles that communicate via peer-to-peer radio channels to meet passenger requirements and satisfy trip requests in an online ODT system.
We validate this model's genericity by applying several allocation mechanisms (mathematical programming, greedy heuristic, distributed constraint optimization, and auctions) and compare their performance on synthetic scenarios in a real-world city map
SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONSijscmcj
The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BY DOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition - O(n3), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.
Algorithm Finding Maximum Concurrent Multicommodity Linear Flow with Limited ...IJCNCJournal
Graphs and extended networks are is powerful mathematical tools applied in many fields as transportation,
communication, informatics, economy, … Algorithms to find Maximum Concurrent Multicommodity Flow
with Limited Cost on extended traffic networks are introduced in the works we did. However, with those
algorithms, capacities of two-sided lines are shared fully for two directions. This work studies the more
general and practical case, where flows are limited to use two-sided lines with a single parameter called
regulating coefficient. The algorithm is presented in the programming language Java. The algorithm is
coded in programming language Java with extended network database in database management system
MySQL and offers exact results.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Modelling dynamic patterns using mobile datacsandit
Understanding, modeling and simulating human mobility among urban regions is very challengeable
effort. It is very important in rescue situations for many kinds of events, either in the indoor events like
evacuation of buildings or outdoor ones like public assemblies, community evacuation, in exigency
situations there are several incidents could be happened, the overcrowding causes injuries and death
cases, which are emerged during emergency situations, as well as it serves urban planning and smart
cities. The aim of this study is to explore the characteristics of human mobility patterns, and model them
mathematically depending on inter-event time and traveled distances (displacements) parameters by using
CDRs (Call Detailed Records) during Armada festival in France. However, the results of the numerical
simulation endorse the other studies findings in that the most of real systems patterns are almost follows an
exponential distribution. In the future the mobility patterns could be classified according (work or off)
days, and the radius of gyration could be considered as effective parameter in modelling human mobility
Robust Watermarking through Dual Band IWT and Chinese Remainder TheoremjournalBEEI
CRT was a widely used algorithm in the development of watermarking methods. The algorithm produced good image quality but it had low robustness against compression and filtering. This paper proposed a new watermarking scheme through dual band IWT to improve the robustness and preserving the image quality. The high frequency sub band was used to index the embedding location on the low frequency sub band. In robustness test, the CRT method resulted average NC value of 0.7129, 0.4846, and 0.6768 while the proposed method had higher NC value of 0.7902, 0.7473, and 0.8163 in corresponding Gaussian filter, JPEG, and JPEG2000 compression test. Meanwhile the both CRT and proposed method had similar average SSIM value of 0.9979 and 0.9960 respectively in term of image quality. The result showed that the proposed method was able to improve the robustness and maintaining the image quality.
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.
A study and implementation of the transit route network design problem for a ...csandit
The design of public transportation networks presup
poses solving optimization problems,
involving various parameters such as the proper mat
hematical description of networks, the
algorithmic approach to apply, and also the conside
ration of real-world, practical
characteristics such as the types of vehicles in th
e network, the frequencies of routes, demand,
possible limitations of route capacities, travel de
cisions made by passengers, the environmental
footprint of the system, the available bus technolo
gies, besides others. The current paper
presents the progress of the work that aims to stud
y the design of a municipal public
transportation system that employs middleware techn
ologies and geographic information
services in order to produce practical, realistic r
esults. The system employs novel optimization
approaches such as the particle swarm algorithms an
d also considers various environmental
parameters such as the use of electric vehicles and
the emissions of conventional ones.
A STUDY AND IMPLEMENTATION OF THE TRANSIT ROUTE NETWORK DESIGN PROBLEM FOR A ...cscpconf
The design of public transportation networks presupposes solving optimization problems,
involving various parameters such as the proper mathematical description of networks, the
algorithmic approach to apply, and also the consideration of real-world, practical
characteristics such as the types of vehicles in the network, the frequencies of routes, demand,
possible limitations of route capacities, travel decisions made by passengers, the environmental
footprint of the system, the available bus technologies, besides others. The current paper
presents the progress of the work that aims to study the design of a municipal public
transportation system that employs middleware technologies and geographic information
services in order to produce practical, realistic results. The system employs novel optimization
approaches such as the particle swarm algorithms and also considers various environmental
parameters such as the use of electric vehicles and the emissions of conventional ones.
Vehicle route scheduling and transportation cost minimization in a latex indu...IJRES Journal
The vehicle route scheduling problem is concerned with the determination of routes and schedules for a fleet of vehicles to satisfy the demands of a set of customers. The goal of vehicle routing is to schedule multiple suppliers from various places. Vehicle routing has existed since the advent of the Industrial age, when large-scale production became possible. As the complexity and scale of the manufacturing world increased, the task of optimizing vehicle routing grew. The vehicle routing problem is a combinatorial optimization and integer programming problem seeking to service a number of customers with a fleet of vehicles. Often the context is that of delivering goods located at a central depot to customers who have placed orders for such goods or vice-versa. Implicit is the goal of minimizing the cost of distributing the goods. Many methods have been developed for searching for good solutions to the problem, however even for the smallest problems, finding global minimum for the cost function is computationally complex. The paper presents an optimization algorithm using Particle Swarm Optimization (PSO) for the vehicle routing that would enable the logistic manager of a latex industry to minimize the transportation cost and maximize the collection using minimum number of vehicles.
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...ijsrd.com
Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.
Comparative study of optimization algorithms on convolutional network for aut...IJECEIAES
The last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications.
Deep learning, and in particular convolutional networks have become a fundamental
tool in the solution of problems related to environment identification, path planning,
vehicle behavior, and motion control. In this paper, we perform a comparative study of
the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the
development of an intelligent sensor. This sensor, part of our research in reactive
systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The
optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated
adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The
training of the deep model is evaluated in terms of convergence, accuracy, recall, and
F1-score metrics. Preliminary results show a better performance of the deep network
when using the SGD function as an optimizer, while the Ftrl function presents the
poorest performances.
With the widespread of smart mobile devices and the
availability of many applications that provide maps, many programs
have spread to find the closest and fastest routes between
two points on the map. While the exactness and effectiveness of
best path depend on the traffic circumstances, the system needs to
add more parameters such as real traffic density and velocity in
road. In addition, because of the restricted resources of phone devices,
it is not reasonable to be used to calculate the exact optimal
solutions by some familiar deterministic algorithms, which are
usually used to find the shortest path with a map of reasonable
node number. To resolve this issue, this paper put forward to use
the genetic algorithm to reduce the computational time. The proposed
system use the genetic algorithm to find the shortest path
time with miscellaneous situations of real traffic conditions. The
genetic algorithm is clearly demonstrate excellent result when applied
on many types of map, especially when the number of nodes
increased.
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
Hybrid iterated local search algorithm for optimization route of airplane tr...IJECEIAES
The traveling salesman problem (TSP) is a very popular combinatorics problem. This problem has been widely applied to various real problems. The TSP problem has been classified as a Non-deterministic Polynomial Hard (NP-Hard), so a non-deterministic algorithm is needed to solve this problem. However, a non-deterministic algorithm can only produce a fairly good solution but does not guarantee an optimal solution. Therefore, there are still opportunities to develop new algorithms with better optimization results. This research develops a new algorithm by hybridizing three local search algorithms, namely, iterated local search (ILS) with simulated annealing (SA) and hill climbing (HC), to get a better optimization result. This algorithm aimed to solve TSP problems in the transportation sector, using a case study from the Traveling Salesman Challenge 2.0 (TSC 2.0). The test results show that the developed algorithm can optimize better by 15.7% on average and 11.4% based on the best results compared to previous studies using the TabuSA algorithm.
HOL, GDCT AND LDCT FOR PEDESTRIAN DETECTIONcsandit
In this paper, we present and analyze different approaches implemented here to resolve pedestrian detection problem. Histograms of Oriented Laplacian (HOL) is a descriptor of
characteristic, it aims to highlight objects in digital images, Discrete Cosine Transform DCT with its two version global (GDCT) and local (LDCT), it changes image's pixel into frequencies coefficients and then we use them as a characteristics in the process. We implemented
independently these methods and tried to combine it and used there outputs in a classifier, the new generated classifier has proved it efficiency in certain cases. The performance of those
methods and their combination is tested on most popular Dataset in pedestrian detection, which
are INRIA and Daimler.
HOL, GDCT AND LDCT FOR PEDESTRIAN DETECTIONcscpconf
In this paper, we present and analyze different approaches implemented here to resolve pedestrian detection problem. Histograms of Oriented Laplacian (HOL) is a descriptor of characteristic, it aims to highlight objects in digital images, Discrete Cosine Transform DCT
with its two version global (GDCT) and local (LDCT), it changes image's pixel into frequencies coefficients and then we use them as characteristics in the process. We implemented independently these methods and tried to combine it and used their outputs in a classifier, the newly generated classifier has proved its efficiency in certain cases. The performance of those methods and their combination is tested on most popular Dataset in pedestrian detection, which is INRIA and Daimler.
The well-known Vehicle Routing Problem (VRP) consist of assigning routeswith a set
ofcustomersto different vehicles, in order tominimize the cost of transport, usually starting from a central
warehouse and using a fleet of fixed vehicles. There are numerousapproaches for the resolution of this kind of
problems, being the metaheuristic techniques the most used, including the Genetic Algorithms (AG). The
number of approachesto the different parameters of an AG (selection, crossing, mutation...) in the literature is
such that it is not easy to take a resolution of a VRP problem directly. This paper aims to simplify this task by
analyzing the best known approaches with standard VRP data sets, and showing the parameter configurations
that offer the best results.
The International Journal of Engineering and Science (The IJES)theijes
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CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
The intensive development of traffic engineering and technologies that are integrated into
vehicles, roads and their surroundings, bring opportunities of real time transport mobility
modeling. Based on such model it is then possible to establish a predictive layer that is capable
of predicting short and long term traffic flow behavior. It is possible to create the real time
model of traffic mobility based on generated data. However, data may have different
geographical, temporal or other constraints, or failures. It is therefore appropriate to develop
tools that artificially create missing data, which can then be assimilated with real data. This
paper presents a mechanism describing strategies of generating artificial data using
microsimulations. It describes traffic microsimulation based on our solution of multiagent
framework over which a system for generating traffic data is built. The system generates data of
a structure corresponding to the data acquired in the real world.
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Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
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Gopinath Rebala
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Integration Of Gis And Optimization Routines For The Vehicle Routing Problem
1. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
DOI : 10.5121/ijccms.2013.2202 9
INTEGRATION OF GIS AND OPTIMIZATION
ROUTINES FOR THE VEHICLE ROUTING PROBLEM
Takwa Tlili1
, Sami Faiz2
and Saoussen Krichen3
1
LARODEC Laboratory, High Institute of Management, Tunisia
takwa.tlili@gmx.fr
2
LTSIRS Laboratory, National engineering school of Tunis, Tunisia
sami.faiz@insat.rnu.tn
3
LARODEC Laboratory, High Institute of Management, Tunisia
saoussen.krichen@isg.rnu.tn
ABSTRACT
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.
KEYWORDS
Vehicle routing problem; Dijkstra-based approach; Geographical information system.
1. INTRODUCTION
In industrial companies, applications that involve a distribution network are required to be
illustrated as a map. For this reason, the use of the GIS is recommended but this technique cannot
evoke the optimization aspect of the distribution problems. Also, the optimization is unable to
result a map. Vehicle Routing Problems (VRP) introduced by Dantzig et al. (1959), are the basic
problems in the vehicle routing class. They have been extensively studied since the sixties and
have received the greatest attention in the scientific literature (i.e. Ai et al, 2009, Cheang et al.,
2012, and Riera-Ledesma and Salazar-Gonzalez, 2012). In the VRP, all the customers
correspond to deliveries and the deterministic demands cannot be split. The vehicles are identical
and based at a single central depot, and only the capacity constraints for the vehicles are
imposed. The CVRP, considered as the standard version of the VRP (Chen et al. (2010), Szeto et
al. (2011) and Marinakis (2013)), is applied in different areas such as fuel consumption
optimization (Xiao et al., 2012) and school bus routing problems (Riera-Ledesma and Salazar-
Gonzalez, 2012). According to Toth et al. (2002), the largest problems that contain about 50
customers can be consistently solved by the most effective exact algorithms. Over this number of
customers the VRP may only be solved with heuristic methods. Basically, three main integration
strategies were proposed in the literature (Karimi and Houston, 1996, Faiz and Krichen, 2013) to
combine optimization routines in GIS. We can point out, the loose integration, the tight
integration and the full integration.
The present paper is organized as follows: section 2 describes in details the Dijkstra-based PSO
approach for soving the VRP. Section 3 is a statement of a real-life VRP application in Ezzahra-
Tunisia as well as the resolution of the proposed instance.
2. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
10
2. THE DIJKSTRA-BASED PSO APPROACH
To solve the VRP using the dataset previously generated, we apply the Dijkstra-based PSO
(DPSO) approach. The PSO algorithm is applied to load the minimum number of vehicles and
the dijkstra algorithm is adapted to handle the routing process.
2.1. Particle swarm optimization concepts
Particle swarm optimization (PSO) is a population-based evolutionary algorithm simulating the
social behavior of bird flocking. The PSO initializes a population of random particles, every
particle is considered as a potential solution. Firstly, particles fly spontaneously through the
problem space at a random velocities. Next, velocities are updated based on the best previous
particle's experience and the best previous group's experience, i.e. the behavior inside a
population is a compromise between individual and collective memory. The PSO script is
described as follows.
Given a d -dimensional search space and a swarm of S particles ),1,=( Sp . To each
particle p in generation t corresponds a position-vector (1) and a velocity-vector (2).
),,,(= 21
t
pd
t
p
t
p
t
p xxxX (1)
),,,(= 21
t
pd
t
p
t
p
t
p vvvV (2)
where t
pnx represents the location and t
pnv is the flying velocity of particle p in generation t in
the n th dimension of the search space ),1,=( dn .
Particles memorize every reached position and save the one with the best fitness. This individual
best position is denoted by ),,,(= 21
t
pd
t
p
t
p
t
p bbbB . In each iteration, particles record the whole
best position's fitness until generation t . This global best position is referred to as
),,,(= 21
t
d
ttt
gggG . The new velocity and position are updated, respectively, by those
following formulas:
)()(= 11
22
11
11
1 −−−−−
−+−+ t
pn
t
n
t
pn
t
pn
t
pn
t
pn xgrcxbrcvwv (3)
t
pn
t
pn
t
pn vxx +−1
= (4)
TtdnSp ,1,=;,1,=;,1,= (5)
Where is the iteration counter ; is the inertia weight that controls the influence
of the precedent velocity on the current velocity; and are two random variables uniformly
distributed in [0, 1]. The acceleration coefficients, referred to as and , control how far a
particle will move in a single iteration.
The pseudo-code of PSO (Perez et al., 2007 and Van den Bergh et al., 2006) can be stated as
follows.
Algorithm 1: PSO algorithm
1: Initialize population ;
2: for t := 1 to T do
3: for s := 1 to S do
4: Evaluate Fitness (
t
pX ) ;
3. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
11
5: Select the individual best position
t
pB ;
6: Select the global best position
t
G ;
7: Update Velocity using Equation (3) ;
8: Update Positions using Equation (4) ;
9: end for
10: end for
2.2. Dijkstra algorithm concepts
Dijkstra's algorithm is a well known approach for solving shortest path problems. This algorithm
can be described as an iterative procedure which starts from a source node. In each iteration,
another vertex from the graph is added to the shortest-path. The pseudo-code of Dijkstra can be
stated as follows.
Algorithm 2: Dijkstra algorithm
1: Initialize the cost of each node to 1
2: Initialize the cost of the source to 0
3: while the queue of nodes is not empty
4: Select the unknown node with the lowest cost, node*
5: Mark node* as known
6: for each node n which is adjacent to node*
7: cost(n)= Min (old-cost(node*), cost(node*)+cost(node*, n))
3. THE VRP APPLIED TO EZZAHRA-TUNISIA
3.1 The studied area description
Ezzahra is a coastal town on the outskirts of Tunis located six kilometers south of the capital and
covers 750 hectares. It is bounded by the Mediterranean Sea and the municipalities of Rades,
Hammam Lif and El-Bou Mhel Bassatine. Administratively attached to the governorate of Ben
Arous, it is the seat of a delegation and a municipality of 31 792 inhabitants (2006) whereas the
city itself has a population of 6000 inhabitants.
3.2 The used GIS tool
We used Quantum GIS (QGIS) that is an Open Source GIS that runs on Linux, Unix, Mac OSX
and Windows. It is released under the GNU General Public License (GPL). QGIS aims to be an
easy-to-use GIS, providing common functions and features. The initial goal was to provide a GIS
data viewer. QGIS has reached the point in its evolution where it is being used by many for their
daily GIS data viewing needs. It also supports a number of raster and vector data formats.
Numerous features can be provided by this tool including the following:
• View and overlay vector and raster data in different formats and projections without
• Conversion to an internal or common format
• Explore spatial data with a friendly GUI and compose maps
• Create, edit, manage and export vector maps in several formats.
• Analyze data
4. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
12
• Publish maps on the Internet
• Extend QGIS functionality through plugins
3.3 The dataset
To generate the map of Ezzahra, we used QGIS 1.4 under Linux. The input is a dataset which
consists on the descriptive data (in .dbf format) and the spacial data (in .shp format). As shown in
figure 1 we present mainly roads, sea, islets and buildings of this town.
Figure 1. The Ezzahra (Tunisia) city map created by QGIS
In order to solve the VRP in this context, we enrich our data with a new layer called Customer
that contains nine clients and one depot. In figure 2 we can observe the disper-sion of customers
on the chosen area.
Figure 2. The dispersion of customers on Ezzahra (Tunisia) territory
The available database of the studied area contains a layer of roads that does not allow the
movement from customer to another. So, we create a new vector layer expressing the transport
5. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
13
network consisting on a set of arcs connecting the set of customers. This layer represents the
roads by which a vehicle can deliver items to a set of customers. Figure 3 shows the positions of
customers onto the transport network.
Figure 3. The transport network
3.4 SOLVING THE VRP APPLICATION
The VRP is an optimization problem in which objects, already stored in depots, are required to
be loaded into vehicles, then delivered to some geographically dispersed selling points all over
the Tunisian territory, in order to fulfill known customer requests.
The scenario of VRP is described below:
The choice of the cost saving vehicles: That depends on the total distance and the
quantity of freight.
The cargo loading: In a single depot, a set of orders has to be charged in each vehicle.
The cargo delivery: According to orders packed in the vehicle, this latter has to visit the
corresponding customers.
Before stating the mathematical formulation, we have to introduce some key terms of this
problem :
-A box: It is a parallelepiped object that contains an order or a part of an order. Each
box is characterized by a weight and a volume.
-An order: It corresponds to only one customer and each order is packed into one or
more boxes.
-An invoice: It includes the type of products, the corresponding quantity and the cus-tomer order.
-A vehicle: It is characterized by a cost, a capacity and a volume.
-A warehouse: It is a central depot that contains the orders already packed into boxes.
Thereby, the problem is about planning the loading of every vehicle then its round in an
6. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
14
optimal way that minimizes the total cost.
• Every order has to be carried by exactly one vehicle.
• The capacity of each vehicle should be respected.
• The total volume of orders packed in a vehicle should be in the volume interval of that
vehicle.
• Each vehicle should not exceed a prefixed distance and can not accept a path less than a
minimum threshold.
Our motivation behind using the QGIS is to use a real-case while performing dis-tances between
customers. There is an additional plugin integrable on QGIS called ROAD GRAPH (Figure 4)
that calculates the shortest path between two points on any polyline layer and plots this path over
the road network.
Figure 4. The ROAD GRAPH plugin
By means of ROAD GRAPH we denote the shortest paths between each pair of customers. For
example, in figure 5 the red line represents the shortest path of transport network between C1 and
C2.
Figure 5. The detection of the shortest path
In the same manner as shown in figure 5, we denoted each shortest distance between every
couple of customers. The result is a distance-matrix illustrated in figure 6. The matrix consists on
round distances in meter.
7. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
15
Figure 6. Matrix of real distances
06647547988151002215336177522390324419270359
66470117521903439812257561184911933109468
5479117503358460993276939130241304797757
88152190335801280597735479738988876086
100223439460912800531428318500917163385
153368122932759775314024163823430722804
177525756693935472831241606151667441173
2390311849130249738850038236151051621032
2441911933130479888917143076674516026161
270351094697757608633822804117210326160
987654321
C
C
C
C
C
C
C
C
C
D
CCCCCCCCCD
We tabulate in table 1 the weight w and the volume v of nine customer's order to deliver.
Table 1: Parameters setting of the orders
Order 1 2 3 4 5 6 7 8 9
w 220 150 100 500 300 150 250 500 770
v 150 450 420 520 170 50 150 300 400
In table 2, we report the parameters of vehicles. The two first columns represent the variable cost
per kilometer and the fixed cost. The two last columns record the capacity weight and volume of
vehicles.
Table 2: Parameters setting of the vehicles
Variable cost (DT/Km) Fixed cost (DT) Max. Weight (Kg) Max. volume (m
3
)
0.106 37.000 1800 2400
The objective of the VRP is to minimize the total cost. Thus, we should begin with computing
costs as follows.
costFixdcostVarcost ijij .).(= +× (6)
We lead to the cost matrix illustrated in figure 6.
Figure 7: Matrix of costs
037.7037.5837.9338.0638.6238.8839.5339.5839.869
37.70037.1237.2337.3637.8637.6138.2538.2438.168
37.5837.12037.3537.4837.9837.7338.3838.3838.037
37.9337.2337.35037.1337.6337.3738.0338.0437.806
38.0637.3637.4837.13037.5637.3037.9037.9737.675
38.6237.8637.9837.6337.56037.2537.4037.4537.244
38.8837.6137.7337.3737.3037.25037.6537.7037.433
39.5338.2538.3838.0337.9037.4037.65037.0537.222
39.5838.2438.3838.0437.9737.4537.7037.05037.271
39.8638.1638.0337.8037.6737.2437.4337.2237.270
987654321
C
C
C
C
C
C
C
C
C
D
CCCCCCCCCD
Our objective is to minimize the total cost. The solution of our application is modeled on two
parts. The loading of orders and the routing of vehicles.
8. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
16
Figure 8: The loading of the orders in the vehicles
Figure 7 illustrates the distribution of the orders on vehicles while respecting the capacity of
weight, the maximum volume and distance of each vehicle.
Figure 8 is the map that guides the driver of vehicle 1 to deliver the loaded orders to
corresponding customers. The yellow arrows form the shortest itinerary to distribute the cargo.
For the travel of the vehicle 1, the cost is equal to DT149.28 in the following representation we
can observe the path with the cost in each arc.
Figure 9 is the map of the second vehicle that begins from the depot point and travels through
the optimal itinerary in terms of cost. While observing the following figure, we obtain a cost of
travel equal to DT186.73 .
Figure 9: The routing map of vehicle 1
Figure 10: The routing map of vehicle 2
9. International Journal of Chaos, Control, Modelling and Simulation (IJCCMS) Vol.2, No.2, June 2013
17
3. CONCLUSIONS
We introduced in this paper the vehicle routing filed and its relation with GIS techniques. To
apply our elaborated approach in a real-case, we used GIS tool for purposes of precision. So we
presented in the third section a detailed description of the studied area, the GIS tool, the dataset
and the scenario of the routing process of a company. Finally, we solved our application using
the DPSO approach, then we reported the solution consisting in two vehicle maps which indicate
the optimal itinerary.
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