Variable neighborhood descent heuristic for solving reverse logistics multi-item dynamic lot-sizing problems. (February 2017)

• Record Type: Journal Article
• Authors: Sifaleras, Angelo; Konstantaras, Ioannis
• Is Part Of: Computers & operations research. Volume 78(2017)
• Page Start: 385
• Page End: 392
• Publication Date: 2017-02
• Subjects: Inventory -- Variable Neighborhood Search -- Mathematical Programming -- Lot Sizing -- Reverse Logistics; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2015.10.004
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: The multi-product dynamic lot sizing problem with product returns and recovery is an important problem that appears in reverse logistics and is known to be NP-hard. In this paper we propose an efficient variable neighborhood descent heuristic algorithm for solving this problem. Furthermore, we present a new benchmark set with the largest instances in the literature. The computational results demonstrate that our approach outperforms the state-of-the-art Gurobi optimizer. Abstract : Highlights: Tackle the multi-product dynamic lot sizing problem with product returns and recovery. Investigate for the first time a VND approach for this NP-hard problem. Present a new benchmark set with the currently largest instances in the literature. Propose a self-contained solver without any link to other callable library API. The proposed VND heuristic outperforms the state-of-the-art Gurobi optimizer.