Self-adaptive randomized constructive heuristics for the multi-item capacitated lot sizing problem. (November 2022)

• Record Type: Journal Article
• Authors: Lai, David; Li, Yijun; Demir, Emrah; Dellaert, Nico; Van Woensel, Tom
• Is Part Of: Computers & operations research. Volume 147(2022)
• Publication Date: 2022-11
• Subjects: Capacitated lot sizing problem -- Constructive heuristics -- Self-adaptive -- Perturbation strategy -- Period-by-period heuristic -- Lot elimination heuristic; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2022.105928
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: The Capacitated Lot-Sizing Problem (CLSP) and its variants are important and challenging optimization problems. Constructive heuristics are known to be the most intuitive and fastest methods for finding good feasible solutions for the CLSPs and therefore are often used as a subroutine in building more sophisticated exact or metaheuristic approaches. Classical constructive heuristics, such as period-by-period heuristics and lot elimination heuristics, are widely used by researchers. This paper introduces four perturbation strategies to the period-by-period and lot elimination heuristics to further improve the solution quality. We propose a new procedure to automatically adjust the parameters of the randomized period-by-period (RPP) heuristics. The procedure is proved to offer better solutions with reduced computation times by improving time-consuming parameter tuning phase. Combinations of the self-adaptive RPP heuristics with Tabu search and lot elimination heuristics are tested to be effective. Computational experiments provided high-quality solutions with a 0.88% average optimality gap on benchmark instances of 12 periods and 12 items, and an optimality gap within 1.2% for the instances with 24 periods and 24 items. Highlights: Perturbation strategies for randomized period-by-period/lot-elimination heuristics A self-adaptive procedure to avoid parameter tuning and premature convergence Effective heuristics in combinations with Tabu Search Best results in solution quality and time for large benchmark instances