Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/590
Title: Adaptive GVNS Heuristics for Solving the Pollution Location Inventory Routing Problem
Authors: Karakostas, Panagiotis
Sifaleras, Angelo
Georgiadis, Michael C.
Editors: Matsatsinis, Nikolaos F.
Marinakis, Yannis
Pardalos, Panos
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Mathematics::Applied Mathematics
FRASCATI::Natural sciences::Computer and information sciences
Keywords: Adaptive General Variable Neighborhood Search
Intelligent optimization methods
Pollution Location Inventory Routing Problem
Green logistics
Issue Date: 2020
Publisher: Springer
Volume: 11968
First Page: 157
Last Page: 170
Volume Title: Learning and Intelligent Optimization. LION 2019
Part of Series: Lecture Notes in Computer Science
Part of Series: Lecture Notes in Computer Science
Abstract: 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.
URI: https://doi.org/10.1007/978-3-030-38629-0_13
https://ruomo.lib.uom.gr/handle/7000/590
ISBN: 978-3-030-38628-3
978-3-030-38629-0
ISSN: 0302-9743
1611-3349
Other Identifiers: 10.1007/978-3-030-38629-0_13
Appears in Collections:Department of Applied Informatics

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