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 M. |
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 |
Files in This Item:
File | Description | Size | Format | |
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Adaptive_GVNS_Heuristics_for_Solving_the_Pollution_Location_Inventory_Routing_Problem.pdf | 203,33 kB | Adobe PDF | View/Open |
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