The electric vehicle routing problem with time windows using genetic algorithm

Around the world there have recently been new and more powerful computing platforms created that can be used to work with computer science problems. Some of these problems that are dealt with are real problems of the industry; most are classified by complexity theory as hard problems. One such problem is the vehicle routing problem with time windows (VRPTW). The computational Grid is a platform which has recently ventured into the treatment of hard problems to find the best solution for these. This chapter presents a genetic algorithm for the vehicle routing problem with time windows. The algorithm iteratively applies a mutation operator, first of the intelligent type and second of the restricting type. The algorithm takes advantage of Grid computing to increase the exploration and exploitation of the solution space of the problem. The Grid performance is analyzed for a genetic algorithm and a measurement of the latencies that affect the algorithm is studied. The convenience of applying this new computing platform to the execution of algorithms specially designed for Grid computing is presented.

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Genetic Algorithm based approach for Vehicle Routing Problem with Time Windows

International Journal of Logistics Systems and Management ◽

10.1504/ijlsm.2008.017480 ◽

2008 ◽

Vol 4 (3) ◽

pp. 338 ◽

Cited By ~ 3

Author(s):

M. Rajmohan ◽

P. Shahabudeen

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Time Windows ◽

Routing Problem

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A Variable Neighborhood Search Branching for the Electric Vehicle Routing Problem with Time Windows

Electronic Notes in Discrete Mathematics ◽

10.1016/j.endm.2014.11.029 ◽

2015 ◽

Vol 47 ◽

pp. 221-228 ◽

Cited By ~ 66

Author(s):

Maurizio Bruglieri ◽

Ferdinando Pezzella ◽

Ornella Pisacane ◽

Stefano Suraci

Keyword(s):

Vehicle Routing ◽

Electric Vehicle ◽

Vehicle Routing Problem ◽

Variable Neighborhood Search ◽

Time Windows ◽

Neighborhood Search ◽

Routing Problem

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A Grid-Based Genetic Approach to Solving the Vehicle Routing Problem with Time Windows

Applied Sciences ◽

10.3390/app9183656 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3656 ◽

Cited By ~ 1

Author(s):

Marco Antonio Cruz-Chávez ◽

Abelardo Rodríguez-León ◽

Rafael Rivera-López ◽

Martín H. Cruz-Rosales

Keyword(s):

Genetic Algorithm ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Information Exchange ◽

Time Windows ◽

Optimization Procedure ◽

Virtual Private Network ◽

Routing Problem ◽

Private Network ◽

Grid Based

This paper describes one grid-based genetic algorithm approach to solve the vehicle routing problem with time windows in one experimental cluster MiniGrid. Clusters used in this approach are located in two Mexican cities (Cuernavaca and Jiutepec, Morelos) securely communicating with each other since they are configured as one virtual private network, and its use as a single set of processors instead of isolated groups allows one to increase the computing power to solve complex tasks. The genetic algorithm splits the population of candidate solutions in several segments, which are simultaneously mutated in each process generated by the MiniGrid. These mutated segments are used to build a new population combining the results produced by each process. In this paper, the MiniGrid configuration scheme is described, and both the communication latency and the speedup behavior are discussed. Experimental results show one information exchange reduction through the MiniGrid clusters as well as an improved behavior of the evolutionary algorithm. A statistical analysis of these results suggests that our approach is better as a combinatorial optimization procedure as compared with other methods.

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