A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem

Can B. Kalayci (), Olcay Polat () and Surendra M. Gupta ()
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Can B. Kalayci: Pamukkale University
Olcay Polat: Pamukkale University
Surendra M. Gupta: Northeastern University

Annals of Operations Research, 2016, vol. 242, issue 2, No 8, 354 pages

Abstract: Abstract For remanufacturing or recycling companies, a reverse supply chain is of prime importance since it facilitates in recovering parts and materials from end-of-life products. In reverse supply chains, selective separation of desired parts and materials from returned products is achieved by means of disassembly which is a process of systematic separation of an assembly into its components, subassemblies or other groupings. Due to its high productivity and suitability for automation, disassembly line is the most efficient layout for product recovery operations. A disassembly line must be balanced to optimize the use of resources (viz., labor, money and time). In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that requires the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence dependent time increments. A hybrid algorithm that combines a genetic algorithm with a variable neighborhood search method (VNSGA) is proposed to solve the SDDLBP. The performance of VNSGA was thoroughly investigated using numerous data instances that have been gathered and adapted from the disassembly and the assembly line balancing literature. Using the data instances, the performance of VNSGA was compared with the best known metaheuristic methods reported in the literature. The tests demonstrated the superiority of the proposed method among all the methods considered.

Keywords: Reverse supply chain; Disassembly; Assembly; Sequence-dependent disassembly line balancing; Metaheuristics; Hybrid genetic algorithm; Variable neighborhood search (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (16)

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DOI: 10.1007/s10479-014-1641-3

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