Adaptive GVNS Heuristics for Solving the Pollution Location Inventory Routing Problem

被引：1

作者：

Karakostas, Panagiotis ^{[1
]}

Sifaleras, Angelo ^{[2
]}

Georgiadis, Michael C. ^{[1
]}

机构：

[1] Aristotle Univ Thessaloniki, Dept Chem Engn, Univ Campus, Thessaloniki 54124, Greece

[2] Univ Macedonia, Sch Informat Sci, Dept Appl Informat, 156 Egnatia Str, Thessaloniki 54636, Greece

来源：

LEARNING AND INTELLIGENT OPTIMIZATION, LION | 2020年 / 11968卷

关键词：

Adaptive General Variable Neighborhood Search; Intelligent optimization methods; Pollution Location Inventory Routing Problem; Green logistics; VARIABLE NEIGHBORHOOD SEARCH;

D O I：

10.1007/978-3-030-38629-0_13

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This work proposes Adaptive General Variable Neighborhood Search metaheuristic algorithms for the efficient solution of Pollution Location Inventory Routing Problems (PLIRPs). A comparative computational study, between the proposed methods and their corresponding classic General Variable Neighborhood Search versions, illustrates the effectiveness of the intelligent mechanism used for automating the re-ordering of the local search operators in the improvement step of each optimization method. Results on 20 PLIRP benchmark instances show the efficiency of the proposed metaheuristics.

引用

页码：157 / 170

页数：14

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