A spatial parallel heuristic approach for solving very large-scale vehicle routing problems

被引：15

作者：

Tu, Wei ^{[1
,2
]}

Li, Qingquan ^{[1
,2
]}

Li, Qiuping ^{[3
]}

Zhu, Jiasong ^{[1
,2
]}

Zhou, Baoding ^{[1
,2
]}

Chen, Biyu ^{[4
]}

机构：

[1] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Coll Civil Engn, Shenzhen, Guangdong, Peoples R China

[2] Natl Adm Surveying Mapping & GeoInformat, Key Lab Geoenvironm Monitoring Coastal Zone, Shenzhen, Guangdong, Peoples R China

[3] Sun Yat Sen Univ, Sch Geog & Planning, Ctr Integrated Geog Informat Anal, Guangzhou, Guangdong, Peoples R China

[4] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Hubei, Peoples R China

来源：

TRANSACTIONS IN GIS | 2017年 / 21卷 / 06期

基金：

美国国家科学基金会;

关键词：

Local search; logistics; parallel computing; spatial partition; vehicle routing problems; TABU SEARCH; WASTE COLLECTION; OPTIMIZATION; GIS; ALGORITHM; SYSTEM;

D O I：

10.1111/tgis.12267

中图分类号：

P9 [自然地理学]; K9 [地理];

学科分类号：

0705 ; 070501 ;

摘要：

The vehicle routing problem (VRP) is one of the most prominent problems in spatial optimization because of its broad applications in both the public and private sectors. This article presents a novel spatial parallel heuristic approach for solving large-scale VRPs with capacity constraints. A spatial partitioning strategy is devised to divide a region of interest into a set of small spatial cells to allow the use of a parallel local search with a spatial neighbor reduction strategy. An additional local search and perturbation mechanism around the border area of spatial cells is used to improve route segments across spatial cells to overcome the border effect. The results of one man-made VRP benchmark and three real-world super-large-scale VRP instances with tens of thousands of nodes verify that the presented spatial parallel heuristic approach achieves a comparable solution with much less computing time.

引用

页码：1130 / 1147

页数：18

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