A biased-randomized iterated local search for the vehicle routing problem with optional backhauls

Julio C. Londoño (), Rafael D. Tordecilla (), Leandro do C. Martins () and Angel A. Juan ()
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Julio C. Londoño: Universidad del Valle
Rafael D. Tordecilla: Universitat Oberta de Catalunya
Leandro do C. Martins: Universitat Oberta de Catalunya
Angel A. Juan: Universitat Oberta de Catalunya

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 29, issue 2, No 7, 387-416

Abstract: Abstract The vehicle routing problem with backhauls integrates decisions on product delivery with decisions on the collection of returnable items. In this paper, we analyze a scenario in which collection of items is optional—but subject to a penalty cost. Both transportation costs and penalties associated with non-collecting decisions are considered. A mixed-integer linear model is proposed and solved for small instances. Also, a metaheuristic algorithm combining biased randomization techniques with iterated local search is introduced for larger instances. Our approach yields cost savings and is competitive when compared to other state-of-the-art approaches.

Keywords: Vehicle routing problem with optional backhauls; Returnable transport items; Biased randomization; Iterated local search; 90B06; 90C11; 90C59 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11750-020-00558-x

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