The document discusses vehicle routing problems and algorithms. It defines the vehicle routing problem, describes common variants like the traveling salesman problem and vehicle routing problem with time windows. It also covers routing algorithms like nearest neighbor and Clarke and Wright savings heuristic. An example is provided to illustrate key concepts like computing an initial savings matrix and updating it during the serial savings algorithm.
Route optimization algorithm are the mathematical formula that solve routing problems..
Some types of routing:
1) Vehicle Routing Problem (VRP)
2) Traveling Salesman Problem (TSP)
3) Ant Colony Optimization (ACO)
This document discusses vehicle routing and scheduling models and algorithms. It introduces basic models like the Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), and Pickup and Delivery Problem with Time Windows (PDPTW). Construction heuristics like savings, insertion, and set covering algorithms are presented to find initial feasible solutions that can then be improved using local search methods. The document outlines practical considerations and recent variants like dynamic and stochastic routing problems.
The document discusses various formulations of the Vehicle Routing Problem with Backhauls (VRPB). It begins by providing background on the VRPB and its history. It then describes several common variants of the VRPB that have been studied in literature, including the Vehicle Routing Problem with Backhauls (VRPB), Mixed Vehicle Routing Problem with Backhauls (MVRPB), Multiple Depot Mixed Vehicle Routing Problem with Backhauls (MDMVRPB), Vehicle Routing Problem with Backhauls and Time Windows (VRPBTW), and others. For each variant, the document outlines key characteristics and constraints and references relevant literature and studies.
The document summarizes an optimization program that airlines can use to determine the right freight capacity, operating frequency, and fleet positioning to minimize costs and maximize profits. The program takes in data on routes, yields, demands, and costs. It then runs integer programming models and U-curve techniques to find the optimum solution. A case study on Yemenia airline shows how the program can determine the best aircraft types for its network and maximize profits on a multi-stop route from Sana'a to Singapore.
In this presentation, a new routing model was introduced in the form of integer linear programming by combining the concepts of time windows and multiple demands and by considering the two contradictory goals of minimizing travel: cost and maximizing demand coverage.
Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous FleetArian Razmi Farooji
The document summarizes a study comparing NSGA II and MOSA algorithms for solving a multi-depot vehicle routing problem with time-dependent travel times and a heterogeneous fleet. The problem involves routing vehicles from multiple depots to serve customers within time windows while minimizing costs and number of routes. NSGA II and MOSA were tested on randomly generated small, medium, and large problems. Results showed that on average, MOSA performed better than the model on small problems, while NSGA II performed comparably to the model.
This document summarizes a study on developing statistical models to predict the conditional probability of release (CPR) and expected quantity of release (EQR) from tank cars involved in accidents. The study used a large historical accident database to develop logistic regression models for the CPR and EQR of different tank car components. Variables like material thickness, presence of jackets/insulation, and accident characteristics were considered. The models can be used to analyze how tank car design features affect safety and guide future designs. Examples showed the models predicting the effects of thickness, head shields, and jackets on release risks. Future work includes finalizing calculators based on the models.
This document discusses multi-modal journey planning and describes a proposed solution approach. It summarizes the multi-modal journey planning problem, characteristics, previous work, and proposes a hybrid approach using a mathematical programming model combined with heuristic methods like Dijkstra's algorithm. The approach involves using the programming model to solve the multi-modal journey planning problem after applying Dijkstra's algorithm and graph techniques to pre-process the data.
Route optimization algorithm are the mathematical formula that solve routing problems..
Some types of routing:
1) Vehicle Routing Problem (VRP)
2) Traveling Salesman Problem (TSP)
3) Ant Colony Optimization (ACO)
This document discusses vehicle routing and scheduling models and algorithms. It introduces basic models like the Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), and Pickup and Delivery Problem with Time Windows (PDPTW). Construction heuristics like savings, insertion, and set covering algorithms are presented to find initial feasible solutions that can then be improved using local search methods. The document outlines practical considerations and recent variants like dynamic and stochastic routing problems.
The document discusses various formulations of the Vehicle Routing Problem with Backhauls (VRPB). It begins by providing background on the VRPB and its history. It then describes several common variants of the VRPB that have been studied in literature, including the Vehicle Routing Problem with Backhauls (VRPB), Mixed Vehicle Routing Problem with Backhauls (MVRPB), Multiple Depot Mixed Vehicle Routing Problem with Backhauls (MDMVRPB), Vehicle Routing Problem with Backhauls and Time Windows (VRPBTW), and others. For each variant, the document outlines key characteristics and constraints and references relevant literature and studies.
The document summarizes an optimization program that airlines can use to determine the right freight capacity, operating frequency, and fleet positioning to minimize costs and maximize profits. The program takes in data on routes, yields, demands, and costs. It then runs integer programming models and U-curve techniques to find the optimum solution. A case study on Yemenia airline shows how the program can determine the best aircraft types for its network and maximize profits on a multi-stop route from Sana'a to Singapore.
In this presentation, a new routing model was introduced in the form of integer linear programming by combining the concepts of time windows and multiple demands and by considering the two contradictory goals of minimizing travel: cost and maximizing demand coverage.
Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous FleetArian Razmi Farooji
The document summarizes a study comparing NSGA II and MOSA algorithms for solving a multi-depot vehicle routing problem with time-dependent travel times and a heterogeneous fleet. The problem involves routing vehicles from multiple depots to serve customers within time windows while minimizing costs and number of routes. NSGA II and MOSA were tested on randomly generated small, medium, and large problems. Results showed that on average, MOSA performed better than the model on small problems, while NSGA II performed comparably to the model.
This document summarizes a study on developing statistical models to predict the conditional probability of release (CPR) and expected quantity of release (EQR) from tank cars involved in accidents. The study used a large historical accident database to develop logistic regression models for the CPR and EQR of different tank car components. Variables like material thickness, presence of jackets/insulation, and accident characteristics were considered. The models can be used to analyze how tank car design features affect safety and guide future designs. Examples showed the models predicting the effects of thickness, head shields, and jackets on release risks. Future work includes finalizing calculators based on the models.
This document discusses multi-modal journey planning and describes a proposed solution approach. It summarizes the multi-modal journey planning problem, characteristics, previous work, and proposes a hybrid approach using a mathematical programming model combined with heuristic methods like Dijkstra's algorithm. The approach involves using the programming model to solve the multi-modal journey planning problem after applying Dijkstra's algorithm and graph techniques to pre-process the data.
Building Understanding of Modern Last-mile Delivery SystemsAlan Erera
1. Same-day delivery systems that provide instant delivery are becoming more common and changing consumer expectations and retail logistics.
2. Very short delivery times require new approaches for efficient last-mile delivery logistics.
3. Understanding modern last-mile delivery systems can be helped by quantitative models, but more study is still needed as these systems evolve rapidly.
The document summarizes research examining whether air transport demand and airfare price transmission are perfectly reversible or exhibit imperfect reversibility with respect to income, jet fuel prices, and airfares. Econometric analysis of US data from 1979-2012 finds evidence of asymmetry and hysteresis, indicating imperfect reversibility. Specifically, the magnitude and direction of responses differ depending on whether factors are rising or falling. This has implications for demand forecasting, policymaking, and airline revenue management. Future work could examine heterogeneity across travel markets.
Lec 13A Signalized Intersections (Transportation Engineering Dr.Lina Shbeeb)Hossam Shafiq I
This document provides an overview of signalized intersection analysis and design for a transportation engineering course. It defines key terms related to signal timing, describes assumptions and methods for calculating traffic delay under uniform and random arrival conditions, and discusses optimizing signal timing for various performance measures. Sample calculations are provided to determine optimal cycle length and green time allocation using flow ratio-based methods. Level of service criteria are also defined based on average vehicle delay.
Signalized Intersections (Transportation Engineering)Hossam Shafiq I
This document provides an overview of signalized intersection analysis and optimization for a transportation engineering course. It defines key terms related to signal timing, describes methods for calculating vehicle delay under uniform and random traffic arrivals, and approaches for optimizing cycle length, green time allocation, and level of service. Examples are provided to illustrate calculations for critical lane group volume-to-capacity ratio, total lost time, optimal signal timing, green time distribution, and intersection level of service.
Operation Research involves determining an optimum solution to a distribution problem. In order to suitably plan and assign vehicles to different routes, organizations can use the listing method described in the North-West corner rule. Research methods such as PERT and CPM are useful for timely completion of a project.
Modular Multi-Objective Genetic Algorithm for Large Scale Bi-level ProblemsStefano Costanzo
The document discusses using a genetic algorithm to optimize air traffic congestion through peak and off-peak pricing. It models the problem as a bi-level optimization with a central planner setting prices and airlines minimizing costs. The genetic algorithm was able to find pricing solutions that reduced total flight delays while maintaining revenue neutrality for air navigation service providers. Future work includes further analyzing cost distributions across airlines and applying decentralized peak load pricing with individual air navigation service providers setting prices.
This document discusses facility location decisions and methods for analyzing location strategies. It begins with an overview of what can be located, such as plants, warehouses, retail outlets, and key questions to consider around location. Common methods for solving single and multiple facility location problems are then presented, including the center-of-gravity (COG) method and optimization approaches. The document concludes with examples of applying COG and discussing other techniques like simulation and weighted checklists for analyzing retail location decisions.
Tractor-trailers are used all across America to transport cargo, but are not designed with fuel efficiency in mind. Therefor, there is an incentive for companies to invest in making their tractor-trailers more aerodynamic in order to save on fuel costs. I go into the testing and methodology of how my team and I decided to tackle the problem of reducing the coefficient of drag on tractor-trailers by implementing air channeling devices (ACDs). Then, I cover the results from our experiments and our ACD recommendation.
Traffic Concepts (Transportation Engineering)Hossam Shafiq I
This document discusses key traffic concepts such as flow rate, spacing, headway, speed, and density. It defines these terms and explores the relationships between volume, speed, and density. Flow rate, spacing, headway, time mean speed and space mean speed are defined. Speed-density and flow-density relationships are presented, showing how traffic flow transitions from uncongested to congested states. Examples of traffic patterns by time of day and location are shown.
Multi-objective Genetic Algorithm Applied to Conceptual Design of Single-stag...Masahiro Kanazaki
"Multi-objective Genetic Algorithm Applied to Conceptual Design of Single-stage Rocket Using Hybrid Propulsion System" presented at The Eighth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems (CJK-OSM).
8 capacity-analysis ( Transportation and Traffic Engineering Dr. Sheriff El-B...Hossam Shafiq I
This document discusses concepts related to transportation capacity analysis including:
- Definitions of level of service (LOS) categories A through F and their characteristics.
- How capacity is defined as the maximum hourly rate of vehicles that can pass a point under prevailing conditions.
- Procedures from the Highway Capacity Manual (HCM) for calculating capacity for basic freeway sections and the impacts of factors like lane width, lateral clearance, and free flow speed.
- The relationships between capacity, LOS, and transportation design and how capacity analysis can inform design.
Multiple Vehicle Motion Planning: An Infinite Diminsion Newton Optimization M...AJHaeusler
In this invited talk at the LARSyS Summer School 2014, we describe a numerical algorithm for multiple vehicle motion planning that addresses explicitly temporal and spatial specifications, as well as energy-related constraints. As a motivating example, we cite the case where a group of vehicles is tasked to reach a number of target points at the same time (simultaneous arrival problem) and avoid inter-vehicle as well as vehicle/obstacle collision, subject to the constraint that the overall energy required for vehicle motion be minimized.
The methodology adopted builds on a numerical method for solving optimal control problems that is known as the PRojection Operator based Newton method for Trajectory Optimization (PRONTO)—a method that avoids the transcription phase typical in direct methods for numerical optimal control and that employs an infinite dimension Newton method to achieve second order convergence of the trajectory optimization problem.
With the theoretical set-up adopted, the vehicle dynamics are taken explicitly into account at the planning level. Thus, in contrast to some of the planning methods available in the literature, the method proposed allows for the direct incorporation of dynamical constraints imposed by the physical characteristics of the vehicles, motion actuators, and even energy sources (e.g. batteries). Should the problem to be solved be feasible, the method yields energy-optimal trajectories without the need to separate the steps of path planning and trajectory generation, as is customary in many of the motion planning methods described in the literature. Restrictive system properties such as differential flatness are not required.
A feasible solution algorithm for a primitive vehicle routing problemCem Recai Çırak
This paper proposes a heuristic algorithm to solve the vehicle routing problem (VRP). The algorithm uses a 2-phase approach: 1) customers are clustered based on their location and demand, and 2) routes are determined to service the customers in each cluster. The algorithm was tested on problems with up to 10,000 customers and showed good computation time but suboptimal solutions. Increasing the vehicle capacity improved the algorithm's performance. The paper concludes that adding an improvement method like 2-opt could further enhance the solution quality.
The document discusses modernizing the carriage stores depot at the Carriage Repair Workshop in Lower Parel, Western Railway through implementing an automated storage and retrieval system. It describes the existing conditions and issues with storage, outlines the proposed project involving installing an ASRS, drive-in pallet systems and conveyors, and discusses the process for obtaining approval and sanction for the project.
A presentation given at the SAE COMVEC conference this year during the CFD expert panel. Focuses on the new adjoint solver that is part of the automotive CFD suite, Elements, from Streamline Solutions.
Lecture 05 Roundabout (Traffic Engineering هندسة المرور & Dr. Usama Shahdah) Hossam Shafiq I
This document summarizes key points from a traffic engineering course lecture on roundabouts. It discusses conflict points, geometric characteristics of roundabouts like entry width and exit width. It covers calculating volumes using passenger car equivalents and peak hour factors. An example is provided on calculating entry, exit, and circulating volumes. Finally, it discusses methods for determining roundabout capacity like the HCM 2000 and Kimber methods, and using software like Synchro to analyze roundabouts.
The document provides an overview of Kota Stores Depot which was established in 1962 to supply materials for workshop manufacturing. It discusses the depot's increased workload due to higher production targets. Key activities include supplying components and centralized scrap disposal. Challenges include higher volume of materials and scrap. The depot has implemented mechanization of material handling, better infrastructure, and IT tools to improve efficiency. Initiatives such as palletization and modernization projects have enhanced productivity and inventory management.
This presentation discusses transportation model optimization techniques. It introduces transportation models and methods for solving transportation problems to reach an optimal solution. Specifically, it covers what optimization is, transportation models and their applications, phases of solution such as obtaining initial and optimal solutions, and methods like the North-West Corner rule. It provides an example problem demonstrating solutions using different methods and showing the Vogel's Approximation Method achieves the lowest transportation cost. The presentation concludes that this technique can optimize scheduling, production, investment and other problems by minimizing transportation or distribution costs.
The document discusses inventory management problems and inventory control systems. It begins by introducing independent demand systems like continuous review, periodic review, economic order quantity and dependent demand systems. It then discusses the objectives of inventory control systems as providing service at minimum total cost. Key concepts covered include types of inventory like cycle and pipeline inventory, inventory performance metrics like costs and service levels, and inventory policies like periodic review and economic order quantity. An example of determining the optimal inventory policy for parkas at a outdoor equipment company is also provided.
Building Understanding of Modern Last-mile Delivery SystemsAlan Erera
1. Same-day delivery systems that provide instant delivery are becoming more common and changing consumer expectations and retail logistics.
2. Very short delivery times require new approaches for efficient last-mile delivery logistics.
3. Understanding modern last-mile delivery systems can be helped by quantitative models, but more study is still needed as these systems evolve rapidly.
The document summarizes research examining whether air transport demand and airfare price transmission are perfectly reversible or exhibit imperfect reversibility with respect to income, jet fuel prices, and airfares. Econometric analysis of US data from 1979-2012 finds evidence of asymmetry and hysteresis, indicating imperfect reversibility. Specifically, the magnitude and direction of responses differ depending on whether factors are rising or falling. This has implications for demand forecasting, policymaking, and airline revenue management. Future work could examine heterogeneity across travel markets.
Lec 13A Signalized Intersections (Transportation Engineering Dr.Lina Shbeeb)Hossam Shafiq I
This document provides an overview of signalized intersection analysis and design for a transportation engineering course. It defines key terms related to signal timing, describes assumptions and methods for calculating traffic delay under uniform and random arrival conditions, and discusses optimizing signal timing for various performance measures. Sample calculations are provided to determine optimal cycle length and green time allocation using flow ratio-based methods. Level of service criteria are also defined based on average vehicle delay.
Signalized Intersections (Transportation Engineering)Hossam Shafiq I
This document provides an overview of signalized intersection analysis and optimization for a transportation engineering course. It defines key terms related to signal timing, describes methods for calculating vehicle delay under uniform and random traffic arrivals, and approaches for optimizing cycle length, green time allocation, and level of service. Examples are provided to illustrate calculations for critical lane group volume-to-capacity ratio, total lost time, optimal signal timing, green time distribution, and intersection level of service.
Operation Research involves determining an optimum solution to a distribution problem. In order to suitably plan and assign vehicles to different routes, organizations can use the listing method described in the North-West corner rule. Research methods such as PERT and CPM are useful for timely completion of a project.
Modular Multi-Objective Genetic Algorithm for Large Scale Bi-level ProblemsStefano Costanzo
The document discusses using a genetic algorithm to optimize air traffic congestion through peak and off-peak pricing. It models the problem as a bi-level optimization with a central planner setting prices and airlines minimizing costs. The genetic algorithm was able to find pricing solutions that reduced total flight delays while maintaining revenue neutrality for air navigation service providers. Future work includes further analyzing cost distributions across airlines and applying decentralized peak load pricing with individual air navigation service providers setting prices.
This document discusses facility location decisions and methods for analyzing location strategies. It begins with an overview of what can be located, such as plants, warehouses, retail outlets, and key questions to consider around location. Common methods for solving single and multiple facility location problems are then presented, including the center-of-gravity (COG) method and optimization approaches. The document concludes with examples of applying COG and discussing other techniques like simulation and weighted checklists for analyzing retail location decisions.
Tractor-trailers are used all across America to transport cargo, but are not designed with fuel efficiency in mind. Therefor, there is an incentive for companies to invest in making their tractor-trailers more aerodynamic in order to save on fuel costs. I go into the testing and methodology of how my team and I decided to tackle the problem of reducing the coefficient of drag on tractor-trailers by implementing air channeling devices (ACDs). Then, I cover the results from our experiments and our ACD recommendation.
Traffic Concepts (Transportation Engineering)Hossam Shafiq I
This document discusses key traffic concepts such as flow rate, spacing, headway, speed, and density. It defines these terms and explores the relationships between volume, speed, and density. Flow rate, spacing, headway, time mean speed and space mean speed are defined. Speed-density and flow-density relationships are presented, showing how traffic flow transitions from uncongested to congested states. Examples of traffic patterns by time of day and location are shown.
Multi-objective Genetic Algorithm Applied to Conceptual Design of Single-stag...Masahiro Kanazaki
"Multi-objective Genetic Algorithm Applied to Conceptual Design of Single-stage Rocket Using Hybrid Propulsion System" presented at The Eighth China-Japan-Korea Joint Symposium on Optimization of Structural and Mechanical Systems (CJK-OSM).
8 capacity-analysis ( Transportation and Traffic Engineering Dr. Sheriff El-B...Hossam Shafiq I
This document discusses concepts related to transportation capacity analysis including:
- Definitions of level of service (LOS) categories A through F and their characteristics.
- How capacity is defined as the maximum hourly rate of vehicles that can pass a point under prevailing conditions.
- Procedures from the Highway Capacity Manual (HCM) for calculating capacity for basic freeway sections and the impacts of factors like lane width, lateral clearance, and free flow speed.
- The relationships between capacity, LOS, and transportation design and how capacity analysis can inform design.
Multiple Vehicle Motion Planning: An Infinite Diminsion Newton Optimization M...AJHaeusler
In this invited talk at the LARSyS Summer School 2014, we describe a numerical algorithm for multiple vehicle motion planning that addresses explicitly temporal and spatial specifications, as well as energy-related constraints. As a motivating example, we cite the case where a group of vehicles is tasked to reach a number of target points at the same time (simultaneous arrival problem) and avoid inter-vehicle as well as vehicle/obstacle collision, subject to the constraint that the overall energy required for vehicle motion be minimized.
The methodology adopted builds on a numerical method for solving optimal control problems that is known as the PRojection Operator based Newton method for Trajectory Optimization (PRONTO)—a method that avoids the transcription phase typical in direct methods for numerical optimal control and that employs an infinite dimension Newton method to achieve second order convergence of the trajectory optimization problem.
With the theoretical set-up adopted, the vehicle dynamics are taken explicitly into account at the planning level. Thus, in contrast to some of the planning methods available in the literature, the method proposed allows for the direct incorporation of dynamical constraints imposed by the physical characteristics of the vehicles, motion actuators, and even energy sources (e.g. batteries). Should the problem to be solved be feasible, the method yields energy-optimal trajectories without the need to separate the steps of path planning and trajectory generation, as is customary in many of the motion planning methods described in the literature. Restrictive system properties such as differential flatness are not required.
A feasible solution algorithm for a primitive vehicle routing problemCem Recai Çırak
This paper proposes a heuristic algorithm to solve the vehicle routing problem (VRP). The algorithm uses a 2-phase approach: 1) customers are clustered based on their location and demand, and 2) routes are determined to service the customers in each cluster. The algorithm was tested on problems with up to 10,000 customers and showed good computation time but suboptimal solutions. Increasing the vehicle capacity improved the algorithm's performance. The paper concludes that adding an improvement method like 2-opt could further enhance the solution quality.
The document discusses modernizing the carriage stores depot at the Carriage Repair Workshop in Lower Parel, Western Railway through implementing an automated storage and retrieval system. It describes the existing conditions and issues with storage, outlines the proposed project involving installing an ASRS, drive-in pallet systems and conveyors, and discusses the process for obtaining approval and sanction for the project.
A presentation given at the SAE COMVEC conference this year during the CFD expert panel. Focuses on the new adjoint solver that is part of the automotive CFD suite, Elements, from Streamline Solutions.
Lecture 05 Roundabout (Traffic Engineering هندسة المرور & Dr. Usama Shahdah) Hossam Shafiq I
This document summarizes key points from a traffic engineering course lecture on roundabouts. It discusses conflict points, geometric characteristics of roundabouts like entry width and exit width. It covers calculating volumes using passenger car equivalents and peak hour factors. An example is provided on calculating entry, exit, and circulating volumes. Finally, it discusses methods for determining roundabout capacity like the HCM 2000 and Kimber methods, and using software like Synchro to analyze roundabouts.
The document provides an overview of Kota Stores Depot which was established in 1962 to supply materials for workshop manufacturing. It discusses the depot's increased workload due to higher production targets. Key activities include supplying components and centralized scrap disposal. Challenges include higher volume of materials and scrap. The depot has implemented mechanization of material handling, better infrastructure, and IT tools to improve efficiency. Initiatives such as palletization and modernization projects have enhanced productivity and inventory management.
This presentation discusses transportation model optimization techniques. It introduces transportation models and methods for solving transportation problems to reach an optimal solution. Specifically, it covers what optimization is, transportation models and their applications, phases of solution such as obtaining initial and optimal solutions, and methods like the North-West Corner rule. It provides an example problem demonstrating solutions using different methods and showing the Vogel's Approximation Method achieves the lowest transportation cost. The presentation concludes that this technique can optimize scheduling, production, investment and other problems by minimizing transportation or distribution costs.
The document discusses inventory management problems and inventory control systems. It begins by introducing independent demand systems like continuous review, periodic review, economic order quantity and dependent demand systems. It then discusses the objectives of inventory control systems as providing service at minimum total cost. Key concepts covered include types of inventory like cycle and pipeline inventory, inventory performance metrics like costs and service levels, and inventory policies like periodic review and economic order quantity. An example of determining the optimal inventory policy for parkas at a outdoor equipment company is also provided.
This document contains questions and answers related to physical distribution management. It discusses topics like transportation problems, vehicle routing, warehouse location, and inventory management. Analytical solutions to optimization problems have limitations and need to be used cautiously. Logistics involves activities like packaging, transportation, warehousing and inventory control across the supply chain. Customer service and cost control are important considerations for warehouse and in-transit inventories.
20151216 convergence of quasi dynamic assignment models Luuk Brederode
This document discusses convergence in strategic transport models. It defines convergence as reaching equilibrium between demand and supply models. Demand models predict demand given travel costs, while supply models predict costs given demand. Iterative methods are needed to find the equilibrium point where these intersect. The document compares different iterative methods, such as the method of repeated approximations and method of successive averages, which improve convergence by enforcing contraction. Non-contraction maps may not converge, so averaging schemes are proposed to enforce contraction. The best method depends on the level and speed of convergence achieved without overly complex implementation.
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.
This document discusses vehicle routing applications and the vehicle routing problem (VRP). It defines vehicle routing as designing and assigning routes to vehicles according to an objective function. The VRP involves figuring out optimal routes from a depot to destinations with constraints like costs, vehicle limitations, and time windows. Common types of VRPs include static and dynamic, with static having predetermined data and dynamic having changing data. Common VRPs discussed are capacitated VRP, VRP with time windows, and pickup and delivery VRP. The document notes the VRP is difficult to solve due to its complexity and discusses manual and software-based approaches to solving VRPs.
The document discusses path control algorithms and simulation results for heavy vehicle collision avoidance. It outlines three prioritized use cases: rear-end collision avoidance involving a single lane change, and two types of run-off-road prevention. It presents a 4 degree-of-freedom nonlinear dynamic model for heavy vehicles. Path planning and feedforward/feedback control algorithms are described to track a critical path. Simulation results demonstrate rear-end collision avoidance through automated braking and steering. Model verification involves comparing simulation results to test data from a proving ground.
Optimizing Logistics Chains with Simulation ModellingAusenco
The document discusses how simulation modeling can optimize logistics chains. It begins by noting that supply chain logistics are often overlooked but are actually vital components with significant costs. Simulation modeling is the best way to predict how logistics systems will perform as they experience variations that static calculations cannot represent. The presentation will discuss how simulation modeling can significantly improve the capital and operating costs of projects. It provides examples of how simulation modeling has been used to optimize various mining and industrial logistics systems. The key benefits of simulation modeling include validating capital investments, improving throughput, reducing costs, and making better risk-optimized decisions.
An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Pr...ijtsrd
An aggrandized solution is designed for the vehicles to reduce the total cost of distribution by which it can supply the goods to the customers with its known capacity can be named as a vehicle routing problem. In variable neighborhood search method, mainly an efficient vehicle routing can be achieved by calculating the distance matrix value based on the customers location or the path where the customers resides. The main objective of the paper is to reduce the total distance travelled to deliver the goods to the customers. The proposed algorithm is a hierarchy based enhanced agglomerative clustering algorithm technique which is used in the data mining scenario effectively. The proposed algorithm decreases the total distance assigning to each route and the important thing need to consider is that, this enhanced clustering algorithm can reduce the total distance when compared to the previously proposed variable neighborhood search method. V. Praveen | V. Hemalatha | M. Poovizhi"An Enhanced Agglomerative Clustering Algorithm for Solving Vehicle Routing Problem" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4701.pdf http://www.ijtsrd.com/computer-science/other/4701/an-enhanced-agglomerative-clustering-algorithm-for-solving-vehicle-routing-problem/v-praveen
The document discusses a feasibility study conducted for potential rail transit service in Santa Cruz County, California. It considers several scenarios for routes and service levels. Ridership forecasts and cost estimates are provided for each scenario. Public feedback was gathered through surveys and outreach activities, with key concerns relating to costs, noise, and service to Watsonville. The study recommends advancing some scenarios for further analysis and environmental review to identify a preferred alternative for implementation.
Future of Heavy Duty Vehicles CO2 Emissions Legislation and Fuel Consumption ...JMDSAE
By Dimitrios Savvidis
The talk will be covering :
Latest developments on CO2 legislation in Europe
Overview of GHG emissions in the transport sector in Europe
New simulation tool – VECTO
Future steps
This document provides an overview of logistics and supply chain operational planning. It discusses key concepts like transportation modes, routing heuristics, strategic vs operational decision making, and information technology enablers. Specific examples are given around Walmart's large distribution network and optimization approaches like bin packing and milk runs. References are also provided to supplemental reading materials.
Reinforcement Learning for EVRP with V2GPrasant Misra
This document proposes using reinforcement learning to optimize routing for electric vehicle fleets performing last-mile deliveries. The approach models the electric vehicle routing problem as a Markov decision process to learn optimal routing policies. The model considers constraints like vehicle capacity, customer time windows, and optional energy delivery to the grid. The reinforcement learning algorithm trains a neural network to select the best vehicle-to-node assignments that minimize trip costs while satisfying constraints. An example illustrates how the algorithm may route two vehicles over time to service customers and an optional energy delivery.
Route optimization for collection of municipal solid waste in Katpadi, VelloreHarshit Shahi
The project aims to reduce the total distance travelled by the fleet of vehicles for collection of municipal solid waste by planning new collection routes using Vehicle Routing Problem Solver (part of Network Analyst extension) in ArcGIS.
Presentation by Clare Linton at UTSG January 2015.
www.city.ac.uk/utsg-2015/programme
www.engineering.leeds.ac.uk/dtc-low-carbon-technologies/student-profiles/ClareLinton.shtml
Mission and operations planning (M&OP) is based on the commercial supply chain S&OP process and modified for use in planning and managing disaster response and humanitarian supply chains.
The document presents a proposed method for annotating transportation mode from tourist GPS trajectory data under environmental constraints. The method uses both tourist features like speed from GPS data as well as environmental constraints like bus routes and train lines to estimate transportation mode. It aims to reduce inconsistencies between estimations from GPS data alone and what is possible given real-world constraints. An experiment applying the method to GPS data from tourist trips in Kyoto showed higher accuracy, around 90%, compared to using GPS data alone or other existing methods. The authors discuss areas for further improving the method, like automatically determining parameters and incorporating more environmental constraints.
This document discusses transportation models and methods for solving transportation problems:
1. It defines a transportation problem as minimizing the cost of distributing a product from multiple sources to multiple destinations. Special methods are needed to solve transportation problems rather than the standard simplex method.
2. The Northwest Corner Rule and Row Minima Method are described as approaches that allocate shipments starting from the upper left cell and lowest cost cell in the first row, respectively, to exhaust supplies and demands in a certain order.
3. The aim of transportation models is to find an optimal transportation schedule that minimizes transportation costs. Various solution methods like the Northwest Corner Rule, Row Minima Method, and Least Cost Method are discussed.
This document discusses optimizing inbound logistics costs for an automobile manufacturer through a case study at TAFE. It presents the existing decentralized warehouse system and associated high costs. The objectives are to introduce a centralized warehouse, optimize carrier selection using packaging heuristics, and implement barcode technology to minimize errors. The methodology involves selecting an optimal warehouse location and layout, estimating costs for the new system, and developing a transportation model from warehouse to plant. Introducing a centralized warehouse is expected to optimize total inbound logistics costs.
Similar to PPT9 - Planning and Managing Long Haul Freight Transportation (20)
The document discusses machine vision and image matching. It begins with definitions of image matching as the process of geometrically positioning two images so their pixels represent the same physical areas. It describes extracting local invariant features from images using methods like Scale Invariant Feature Transform (SIFT) to find correspondences between images for tasks like object recognition and panorama creation despite variations in lighting, viewpoint and scale. SIFT extracts key points from images and represents each with a 128-element feature vector for robust matching between images.
1. Unsupervised learning digunakan untuk pengelompokkan data tanpa label melalui clustering.
2. K-means clustering dan hierarchical clustering adalah dua pendekatan utama clustering.
3. Pemilihan parameter seperti jumlah cluster pada k-means mempengaruhi akurasi hasil clustering.
1. The document discusses machine vision techniques including image filtering in the frequency domain and wavelet transforms. It provides details on Fourier transforms, common filters like low pass and high pass, and compares Fourier and wavelet transforms.
2. Fourier transforms allow filtering images by manipulating the image's frequency spectrum but do not provide time information. Wavelet transforms analyze images based on frequency and time, providing advantages over Fourier transforms for non-stationary signals.
3. Common filters discussed are ideal, Butterworth, and Gaussian filters for both low pass and high pass. Examples show the effects of applying these filters to an image. Discrete wavelet transforms provide an efficient method to decompose signals into different frequency bands.
Warna dari sebuah obyek dipengaruhi oleh interaksi antara cahaya dan material obyek, serta sistem penglihatan manusia. Beberapa faktor yang mempengaruhi warna antara lain pemantulan, penyerapan, dan pembelokan cahaya oleh material obyek, serta sensitivitas reseptor mata manusia terhadap panjang gelombang cahaya. Representasi warna dalam ruang warna seperti RGB dan CIE XYZ memungkinkan standarisasi persepsi warna.
This document provides an overview of machine vision applications including content-based image retrieval and face recognition. It discusses how content-based image retrieval systems work by extracting image features, calculating distances between images, and returning similar images from a database based on a query image. Examples of content-based image retrieval systems and the features they use are described. The document also covers face detection and recognition techniques, including the use of eigenfaces which represent faces as locations in a lower-dimensional space.
This document provides an overview of image matching techniques. It defines image matching as geometrically aligning two images so corresponding pixels represent the same scene region. Key aspects covered include detecting invariant local features, describing features in a scale and rotation invariant way using SIFT, and matching features between images. SIFT is highlighted as an extraordinarily robust technique that can handle various geometric and illumination changes. Feature matching is used in many computer vision applications such as image alignment, 3D reconstruction, and object recognition.
This document discusses unsupervised machine learning techniques for clustering unlabeled data. It covers k-means clustering, which partitions data into k groups based on minimizing distance between points and cluster centroids. It also discusses agglomerative hierarchical clustering, which successively merges clusters based on their distance. As an example, it shows hierarchical clustering of texture images from five classes to group similar textures.
This document provides an overview of pattern recognition and supervised learning for machine vision. It discusses what pattern recognition is, examples of pattern recognition applications, the basic steps in a pattern recognition system including data acquisition, preprocessing, feature extraction, supervised/unsupervised learning, and post-processing. For supervised learning, it describes the process of inferring functions from labeled training data. It also provides an example of using multiple features and decision boundaries for texture classification of images.
This document provides an overview of texture analysis techniques in machine vision. It discusses both structural and statistical approaches to texture analysis. Structural approaches attempt to model textures as repeating patterns of texture elements, while statistical approaches characterize textures using measures computed from pixel intensities alone. Specific statistical techniques covered include local binary patterns (LBP), gray-level co-occurrence matrices (GLCM), Laws texture energy measures, Fourier power spectrum, and wavelet texture descriptors. The document also discusses how these various texture features can be used for texture segmentation.
This document discusses various shape features that can be used for machine vision and image segmentation. It covers thresholding techniques, identifying object boundaries using chain codes and Fourier descriptors, and describing regions using basic descriptors like area and perimeter or moment invariants. Segmentation is described as an important but difficult task, and thresholding, discontinuities and region similarity are presented as common segmentation approaches. Examples are provided to illustrate different shape feature extraction methods.
This document provides an overview of feature detection techniques in machine vision, including edge detection, the Canny edge detector, interest points, and the Harris corner detector. It describes how edge detection works by finding discontinuities in images using masks and correlation. It explains that the Canny edge detector is an optimal method that uses Gaussian smoothing and non-maximum suppression. Interest points are localized features useful for applications like image alignment, and the Harris corner detector computes gradients to find locations with dominant directions, identifying corners.
This document provides an overview of image filtering in the frequency domain and introduces the wavelet transform. It discusses Fourier transforms and how they can be used to filter images. Specifically, it describes:
1) How low-pass filters smooth images by removing high frequency components, while high-pass filters sharpen images by removing low frequencies.
2) Common low-pass filters like ideal, Butterworth, and Gaussian filters and how their transfer functions are defined.
3) Examples of filtering an image with different low-pass filters to smooth or remove noise.
4) The limitations of the Fourier transform in analyzing non-stationary signals and how the wavelet transform provides time-frequency localization.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
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PPT9 - Planning and Managing Long Haul Freight Transportation
1. Supply Chain Logistics
Topic :
Planning and Managing Short-Haul Freight Transportation
and
Planning and Managing Long Freight Transportation
2. Single Flow Routing and
Multiple Vehicle Roundtrip
Routing
Chapter 8
Materials are taken from Prof. Marc Goetschalckx Course Note with some modification
35. Clark-Wright Savings Facts
• The points that offer the greatest savings when combined
on the same route are those that are farthest from the
depot and that are closest to each other