A Bi-level Evolutionary Algorithm for Multi-objective Vehicle Routing Problems with Time Window Constraints

2015 ◽  
pp. 27-38 ◽  
Author(s):  
Abhishek Gupta ◽  
Yew-Soon Ong ◽  
Allan N. Zhang ◽  
Puay Sew Tan
Keyword(s):  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Time Window ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Multi Objective ◽  
Time Window Constraints

Multi Depot Probabilistic Vehicle Routing Problems with a Time Window

2013 ◽  
pp. 857-879
Author(s):  
Sutapa Samanta ◽  
Manoj K. Jha
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Vehicle Routing Problems ◽  
Problem Size ◽  
Routing Problems ◽  
Routing Problem ◽  
Logistics Systems ◽  
Time Window Constraints ◽  
Limiting Case ◽  
Crossover And Mutation

Vehicle Routing Problems (VRPs) are prevalent in all large pick up and delivery logistics systems and are critical to city logistics operations. Of notable significance are three key extensions to classical VRPs: (1) multi-depot scenario; (2) probabilistic demand; and (3) time-window constraints, which are considered simultaneously with VRPs in this paper. The issue then becomes a Multi Depot Probabilistic Vehicle Routing Problem with a Time Window (MDPVRPTW). The underlying complexities of MDPVRPTW are analyzed and a heuristic approach is presented to solve the problem. Genetic algorithms (GAs) are found to be capable of providing an efficient solution to the so-called MDPVRPTW. Within the GAs, two modification operators namely, crossover and mutation, are designed specially to solve the MDPVRPTW. Three numerical examples with 14, 25, and 51 nodes are presented to test the efficiency of the algorithm as the problem size grows. The proposed algorithms perform satisfactorily and the limiting case solutions are in agreement with the constraints. Additional work is needed to test the robustness and efficiency of the algorithms as the problem size grows.

Multi Depot Probabilistic Vehicle Routing Problems with a Time Window

2013 ◽  
pp. 251-273
Author(s):  
Sutapa Samanta ◽  
Manoj K. Jha
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Vehicle Routing Problems ◽  
Problem Size ◽  
Routing Problems ◽  
Routing Problem ◽  
Logistics Systems ◽  
Time Window Constraints ◽  
Limiting Case ◽  
Crossover And Mutation

Vehicle Routing Problems (VRPs) are prevalent in all large pick up and delivery logistics systems and are critical to city logistics operations. Of notable significance are three key extensions to classical VRPs: (1) multi-depot scenario; (2) probabilistic demand; and (3) time-window constraints, which are considered simultaneously with VRPs in this paper. The issue then becomes a Multi Depot Probabilistic Vehicle Routing Problem with a Time Window (MDPVRPTW). The underlying complexities of MDPVRPTW are analyzed and a heuristic approach is presented to solve the problem. Genetic algorithms (GAs) are found to be capable of providing an efficient solution to the so-called MDPVRPTW. Within the GAs, two modification operators namely, crossover and mutation, are designed specially to solve the MDPVRPTW. Three numerical examples with 14, 25, and 51 nodes are presented to test the efficiency of the algorithm as the problem size grows. The proposed algorithms perform satisfactorily and the limiting case solutions are in agreement with the constraints. Additional work is needed to test the robustness and efficiency of the algorithms as the problem size grows.

Multi Depot Probabilistic Vehicle Routing Problems with a Time Window

2011 ◽  
Vol 2 (2) ◽  
pp. 40-64 ◽  
Author(s):  
Sutapa Samanta ◽  
Manoj K. Jha
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Vehicle Routing Problems ◽  
Problem Size ◽  
Routing Problems ◽  
Routing Problem ◽  
Logistics Systems ◽  
Time Window Constraints ◽  
Limiting Case ◽  
Crossover And Mutation

Vehicle Routing Problems (VRPs) are prevalent in all large pick up and delivery logistics systems and are critical to city logistics operations. Of notable significance are three key extensions to classical VRPs: (1) multi-depot scenario; (2) probabilistic demand; and (3) time-window constraints, which are considered simultaneously with VRPs in this paper. The issue then becomes a Multi Depot Probabilistic Vehicle Routing Problem with a Time Window (MDPVRPTW). The underlying complexities of MDPVRPTW are analyzed and a heuristic approach is presented to solve the problem. Genetic algorithms (GAs) are found to be capable of providing an efficient solution to the so-called MDPVRPTW. Within the GAs, two modification operators namely, crossover and mutation, are designed specially to solve the MDPVRPTW. Three numerical examples with 14, 25, and 51 nodes are presented to test the efficiency of the algorithm as the problem size grows. The proposed algorithms perform satisfactorily and the limiting case solutions are in agreement with the constraints. Additional work is needed to test the robustness and efficiency of the algorithms as the problem size grows.

An Intelligent Water Drop Algorithm for Solving Multi-Objective Vehicle Routing Problems With Mixed Time Windows

2019 ◽  
Vol 10 (1) ◽  
pp. 82-104 ◽  
Author(s):  
Tao Wang ◽  
Jing Ni ◽  
Yixuan Wang
Keyword(s):  
Vehicle Routing ◽  
Hybrid Algorithm ◽  
Water Drop ◽  
Time Windows ◽  
Continuous Optimization ◽  
Vehicle Routing Problems ◽  
Basic Algorithm ◽  
Routing Problems ◽  
Multi Objective ◽  
Minimum Number

This article proposes an Intelligent Water Drop Algorithm for solving Multi-Objective Vehicle Routing Problems by considering the constraints of vehicle volume, delivery mileage, and mixed time windows and minimizing the cost of distribution and the minimum number of vehicles. This article improves the basic Intelligent Water Drop Algorithm and show the improved intelligent water droplet genetic hybrid algorithm is an effective method for solving discrete problems. The authors present a practical example and show the applicability of the proposed algorithm. The authors compare the algorithms with the basic algorithm and show the improved intelligent droplet genetic hybrid algorithm has higher computing efficiency and continuous optimization capability.

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