Applying Genetic Algorithm for Min-Max Vehicle Routing Problem

Chun Yu Ren

doi:10.4028/www.scientific.net/AMM.97-98.640

Paper Titles

A New Genetic Algorithm for Continuous Berth and Quay Crane Allocation Problem
p.619

Research on Regional Logistics Spatial Evolution Based on Geographic Simulation System
p.623

An Investigation of the Planning and Designing of Road Network in Logistical Parks
p.629

Optimization of Container Terminal Unloading Operations Based Upon Multistage Flexible Flowshop Scheduling
p.633

Applying Genetic Algorithm for Min-Max Vehicle Routing Problem
p.640

Study on Construction Order of Logistics Park Based on Combined Model of Analytic Hierarchy Process and Gray Comprehensive Evaluation
p.644

Studies on Risk Pre-Warning and Emergency Treatment of Thermal Coal Supply Chain
p.648

The Research on Location of Multi-Level Network Distribution Centers
p.653

Developing a Competitive Strategy for the Port of Qingdao: An Application of the Analytic Hierarchy Process to Ports in Bohai Bay
p.659

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 97-98Applying Genetic Algorithm for Min-Max Vehicle...

Applying Genetic Algorithm for Min-Max Vehicle Routing Problem

Abstract:

The present study is focused on the Min-Max Vehicle Routing Problem (MMVRP). Genetic algorithm is used to get the optimization solution. First of all, use natural number coding so as to simplify the problem; apply insertion method so as to improve the feasibility; retain the best selection so as to guard the diversity of group; adopt 2- exchange mutation operator to strengthen the partial searching ability of chromosome. Secondly, the improved route crossover operation can avoid destroying good gene parts. Finally, the good performance of improved algorithm can be proved by experiment calculation and concrete examples

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 97-98)

Pages:

640-643

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.97-98.640

Citation:

Cite this paper

Online since:

September 2011

Authors:

Chun Yu Ren

Keywords:

2-Exchange Mutation Operator, Improved Route Crossover Operation, MMVRP, Natural Coding, Retain the Best Selection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Liu Xia. Research on Vehicle Routing Problem. PhD thesis of Huazhong University of Science and Technology, (2007), pp.24-44

Google Scholar

[2] Arkin E.M., Hassin R., Levin A. Approximations for minimum and min-max vehicle routing problems. Algorithms archive, Vol. 59(2006), pp.1-18

DOI: 10.1016/j.jalgor.2005.01.007

Google Scholar

[3] Ozdamar, L., Wei Yi. Greedy Neighborhood Search for Disaster Relief and Evacuation Logistics. Intelligent Systems, Vol. 23(2008), pp.14-23

DOI: 10.1109/mis.2008.7

Google Scholar

[4] J. Carlsson, D. Ge, A. Subramaniam, A. Wu, Y. Ye. Solving min-max multi-depot vehicle routing problem.Report, (2007).

DOI: 10.1090/fic/055/03

Google Scholar

[5] David Applegate; William Cook; Sanjeeb Dash; Andre Rohe. Solution of a min-max vehicle routing problem. INFORMS Journal on Computing, Vol. 14(2002), pp.132-143

DOI: 10.1287/ijoc.14.2.132.118

Google Scholar

[6] A. Corberan, E. Fernandez, M. Laguna, R. Marti. Heuristic solutions to the problem of routing school buses with multiple objectives, Journal of the Operational Research Society, Vol. 53(2002), pp.427-435

DOI: 10.1057/palgrave.jors.2601324

Google Scholar