Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/733
Title: | Variable neighborhood search-based solution methods for the pollution location-inventory-routing problem |
Authors: | Karakostas, Panagiotis Sifaleras, Angelo Georgiadis, Michael C. |
Type: | Article |
Subjects: | FRASCATI::Natural sciences::Mathematics::Applied Mathematics FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | Variable Neighborhood Search Facility Location Vehicle Routing Problem Inventory Management |
Issue Date: | 2022 |
Publisher: | Springer |
Source: | Optimization Letters |
Volume: | 16 |
First Page: | 211 |
Last Page: | 235 |
Abstract: | This work presents efficient solution approaches for a new complex NP-hard combinatorial optimization problem, the Pollution Location Inventory Routing problem (PLIRP), which considers both economic and environmental issues. A mixed-integer linear programming (MILP) model is proposed and first, small problem instances are solved using the CPLEX solver. Due to its computational complexity, General Variable Neighborhood Search (GVNS)-based metaheuristic algorithms are developed for the solution of medium and large instances. The proposed approaches are tested on 30 new randomly generated PLIRP instances. Parameter estimation has been performed for determining the most suitable perturbation strength. An extended numerical analysis illustrates the effectiveness and efficiency of the underlying methods, leading to high-quality solutions with limited computational effort. Furthermore, the impact of holding cost variations to the total cost is studied. |
URI: | https://doi.org/10.1007/s11590-020-01630-y https://ruomo.lib.uom.gr/handle/7000/733 |
ISSN: | 1862-4472 1862-4480 |
Other Identifiers: | 10.1007/s11590-020-01630-y |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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VNS_based_solution_methods_for_the_pollution_location_inventory_routing_problem.pdf | 539,42 kB | Adobe PDF | View/Open |
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