Heuristic solution methods for the selective disassembly sequencing problem under sequence-dependent costs. (March 2021)

• Record Type: Journal Article
• Authors: Jeunet, Jully; Della Croce, Federico; Salassa, Fabio
• Is Part Of: Computers & operations research. Volume 127(2021)
• Publication Date: 2021-03
• Subjects: Disassembly -- Product recovery -- Maintenance -- Greedy algorithms -- Matheuristics; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2020.105151
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: We address the selective disassembly sequencing problem under sequence dependent costs. We develop a randomized greedy algorithm and a matheuristic. Simulation results show the superiority of our heuristics over existing solution methods. Abstract: The first Waste Framework Directive issued by the European Union dates back to the seventies but was drastically amended in the last decade to reduce environmental impacts of waste by encouraging reuse, recycling and remanufacturing. Product recovery starts with disassembly which results in high labor costs. Disassembly supports environmentally conscious choices like replacement of defective parts to extend the life span of products, removal of suitable components for reuse and extraction of hazardous substances to decontaminate materials for reprocessing. Besides, selective disassembly also accommodates maintenance and repairs. Optimizing the cost of disassembly is crucial to make this process an economically viable option. Due to change tools and parts reorientation, disassembly costs are sequence-dependent. Therefore minimizing the disassembly cost involves the search for an adequate sequence of disassembly tasks. Consequently, this paper addresses the disassembly sequencing problem for selective and sequential disassembly under sequence-dependent costs. As optimal formulations fail to handle real-world cases, we develop a randomized greedy algorithm (needing a very few number of parameters to be set and proving to be robust with respect to their value) and a matheuristic to solve efficiently medium to large-sized instances.