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.
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The second author has been funded by the University of Macedonia Research Committee as part of the “Principal Research 2019” funding scheme (ID 81307).
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Karakostas, P., Sifaleras, A., Georgiadis, M.C. (2020). Adaptive GVNS Heuristics for Solving the Pollution Location Inventory Routing Problem. In: Matsatsinis, N., Marinakis, Y., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2019. Lecture Notes in Computer Science(), vol 11968. Springer, Cham. https://doi.org/10.1007/978-3-030-38629-0_13
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