End-of-life product disassembly with priority-based extraction of dangerous parts

Michela Dalle Mura, Francesco Pistolesi (), Gino Dini and Beatrice Lazzerini
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Michela Dalle Mura: University of Pisa
Francesco Pistolesi: University of Pisa
Gino Dini: University of Pisa
Beatrice Lazzerini: University of Pisa

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 3, No 13, 837-854

Abstract: Abstract The amount of electronic waste generated in the world is impressive. The USA alone yearly throw away 9.4 million tons of electronic devices: only 12.5% is recycled. One way to reduce this massive impact on the environment is to disassemble these devices with the aim of reusing and recycling as many parts as possible. Disassembling end-of-life products is a complex industrial process that may pose workers at risk because some parts of the product may contain dangerous materials. It is thus crucial to design efficient, sustainable and secure disassembly lines. This paper presents a multi-objective formulation of the Disassembly Line Balancing Problem (DLBP) which promotes efficiency and includes a new objective that increases the level of safety. The efficiency is guaranteed by balancing the idle times of the workstations, and by maximizing the profit and the level of feasibility of a disassembly sequence, which means disassembling the product as much as possible. Safety is maximized by extracting each dangerous part with a priority that is higher the more dangerous the part is. The most dangerous parts can thus be quickly removed from the product, thereby eliminating the exposure to the greatest risks. The disassembly continues with the execution of the tasks that remove the parts that are gradually less dangerous. Along with the DLBP formulation, this paper presents a genetic algorithm purposely designed to solve the problem. Two real-world case studies are discussed which entail the disassembly of a TV monitor and an air conditioner.

Keywords: Combinatorial optimization; Disassembly line balancing; Electronic waste; Evolutionary computation; Genetic algorithm; Metaheuristic; Multi-objective optimization; Product reuse and recycling; Risk; Safety; Selective disassembly; Sustainability (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10845-020-01592-z

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