Decomposition-based multi-objective evolutionary algorithm for vehicle routing problem with stochastic demands

被引:0
作者
Sen Bong Gee
Willson Amalraj Arokiasami
Jing Jiang
Kay Chen Tan
机构
[1] National University of Singapore,
来源
Soft Computing | 2016年 / 20卷
关键词
Vehicle routing problem; Multi-objective optimization ; Evolutionary algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
Vehicle routing problem with stochastic demands (VRPSD) is a famous and challenging optimization problem which is similar to many real world problems. To resemble the real world scenario, total traveling distance, total driver remuneration, the number of vehicles used and the difference between driver remuneration are considered and formulated in the multi-objective optimization perspective. This paper aims to solve multi-objective VRPSD under the constraints of available time window and vehicle capacity using decomposition-based multi-objective evolutionary algorithm (MOEA/D) with diversity-loss-based selection method incorporates with local search and multi-mode mutation heuristics. We have also compared the optimization performance of the decomposition-based approach with the domination-based approach to study the difference between these two well-known evolutionary multi-objective algorithm frameworks. The simulation results have showed that the decomposition-based approach with diversity-loss-based selection method is able to maintain diverse output solutions.
引用
收藏
页码:3443 / 3453
页数:10
相关论文
共 96 条
[1]  
Ahmed F(2013)Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms Soft Comput 17 1283-1299
[2]  
Deb K(2015)A decomposition-based evolutionary algorithm for many objective optimization Evol Comput IEEE Trans 19 445-460
[3]  
Asafuddoula M(1992)A vehicle routing problem with stochastic demand Oper Res 40 574-585
[4]  
Ray T(2010)Multi-objective and prioritized berth allocation in container ports Ann Oper Res 180 63-103
[5]  
Sarker R(2002)A fast and elitist multiobjective genetic algorithm: NSGA-II IEEE Trans Evol Comput 6 182-197
[6]  
Bertsimas DJ(2005)Evaluating the Evol Comput J 13 501-525
[7]  
Cheong C(2014)-domination based multiobjective evolutionary algorithm for a quick computation of pareto-optimal solutions Evol Comput IEEE Trans 18 577-601
[8]  
Tan KC(1986)An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints Eur J Oper Res 23 228-235
[9]  
Liu D(2011)Stochastic vehicle routing with modified savings algorithm Adv Eng Softw 42 760-771
[10]  
Lin C(2014)Jmetal: a java framework for multi-objective optimization Evol Comput IEEE Trans 99 1-1