Exploring Feasible and Infeasible Regions in the Vehicle Routing Problem with Time Windows Using a Multi-objective Particle Swarm Optimization Approach

被引：0
|
作者：
Castro, Juan P. [1 ]
Landa-Silva, Dario [1 ,2 ]
Moreno Perez, Jose A. [3 ]
机构：
[1] Univ Nottingham, Automated Scheduling Optimisat & Planning Res Grp, Nottingham NG7 2RD, England
[2] Univ Nottingham, Optimisat & Planning Res Grp, Automated Scheduling, Nottingham NG7 2RD, England
[3] Univ Laguna, Escuela Tecn Super Engn Informat, Grp Intelligent Comp, Dpto Estadist I O & Comp, Tenerife, Spain
来源：
NICSO 2008: NATURE INSPIRED COOPERATIVE STRATEGIES FOR OPTIMIZATION | 2009年 / 236卷
关键词：
Particle Swarm Optimization; PSO; Multi-Objective; Vehicle Routing Problem with Time Windows; VRPTW;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper investigates the ability of a discrete particle swarm optimization algorithm (DPSO) to evolve solutions from infeasibility to feasibility for the Vehicle Routing Problem with Time Windows (VRPTW). The proposed algorithm incorporates some principles from multi-objective optimization to allow particles to conduct a dynamic trade-off between objectives in order to reach feasibility. The main contribution of this paper is to demonstrate that without incorporating tailored heuristics or operators to tackle infeasibility, it is possible to evolve very poor infeasible route-plans to very good feasible ones using swarm intelligence.
引用
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页码：103 / +
页数：3
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