A learning-based iterated local search algorithm for the asymmetrical prize collecting vehicle routing problem

Author

Dong, Naiqun ; Xu, Jianyou

Author_Institution

Polytech. Sch., Shenyang Ligong Univ., Shenyang, China

Volume

2

fYear

2012

fDate

20-21 Oct. 2012

Firstpage

29

Lastpage

32

Abstract

This paper proposes a learning-based iterated local search algorithm for the asymmetrical prize collecting vehicle routing problem, which is a new variant of VRP where the objective is a linear combination of three objects: minimization of total distance, minimization of vehicles used, and maximization of customers served. Some benchmark problem instances are taken as the experiment data and the computational results show that our approach can yield about 4.05% average duality gap compared to the lower bound.

Keywords

duality (mathematics); iterative methods; learning (artificial intelligence); minimisation; search problems; transportation; vehicles; VRP; asymmetrical prize collecting vehicle routing problem; customers served maximization; duality gap; learning-based iterated local search algorithm; linear combination; total distance minimization; vehicles used minimization; Classification algorithms; Heuristic algorithms; Operations research; Routing; Search problems; Transforms; Vehicles; iterated local search; prize collecting; vehicle routing problem;

fLanguage

English

Publisher

ieee

Conference_Titel

System Science, Engineering Design and Manufacturing Informatization (ICSEM), 2012 3rd International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4673-0914-1

Type

conf

DOI

10.1109/ICSSEM.2012.6340799

Filename

6340799