Disassembly line balancing with sequencing decisions: A mixed integer linear programming model and extensions

Edis, Emrah B.; Ilgın, Mehmet Ali; Edis, Rahime Sancar

doi:10.1016/j.jclepro.2019.117826

Cited by 23 publications

(5 citation statements)

References 42 publications

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“…Equation (12) indicates that each disassembly task can only be assigned to only one workstation. Equation ( 13) denotes that the disassembly precedence relations between two tasks should not be violated when disassembled at each workstation.…”

Section: Constrainsmentioning

confidence: 99%

“…There are roughly three categories of methods to solve the DLBP: exact methods, heuristic methods and metaheuristic algorithms. In the exact methods, mixed-integer linear programming [12][13][14][15][16], branch and bound [17,18] are mostly used to solve DLBP in the linear interactive and general optimizer (LINGO). Aiming at the low efficiency of solving large-scale DLBP problems, heuristic and metaheuristic algorithms are proposed.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Optimization of Two-Sided Disassembly Line Balance Considering Parallel Operation and Uncertain Using Multiobjective Flatworm Algorithm

et al. 2021

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The two-sided disassembly line is popular for its high-efficiency disassembly of large-volume end-of-life products. However, in the process of two-sided disassembly, some parts and components need to be disassembled in parallel, and the uncertainty of disassembly time lacks certain research. This paper constructs a fuzzy multiobjective two-sided disassembly line balance problem model based on parallel operation constraint, which aims to reduce the balance loss rate, smoothness index, and energy consumption of disassembly activities. A multiobjective flatworm algorithm based on the Pareto-dominance relationship is developed. To increase the diversity of feasible solutions in the evolution process and accelerate the convergence of Pareto-optimal solutions to prevent the random search of solution space, growth, splitting and regeneration mechanisms are embedded in the algorithm. The working mechanism and efficiency of the multiobjective flatworm algorithm are proved on a series of two-sided disassembly cases, and the excellent performance of the proposed model and algorithm are demonstrated by an actual automobile two-sided disassembly line.

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Section: Constrainsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Optimization of Two-Sided Disassembly Line Balance Considering Parallel Operation and Uncertain Using Multiobjective Flatworm Algorithm

et al. 2021

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“…The DLBP is a multi-objective NP-hard optimization problem, which has been studied from the aspects of economics [21][22][23][24], harmfulness [25], cost [26,27], efficiency [28][29][30][31], and energy consumption [32,33]. For example, Zhu et al considered the hazards of the disassembly operation in the DLBP.…”

Section: Study Motivationmentioning

confidence: 99%

Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards

Meng

Zhang

2020

Sustainability

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End-of-life (EOL) electromechanical products often have multiple failure characteristics and material hazard attributes. These factors create uncertain disassembly task sequences and affect the remanufacturing cost, environmental sustainability, and disassembly efficiency of the remanufacturing disassembly line system. To address this problem, a novel multi-constraint remanufacturing disassembly line balancing model (MC-RDLBM) is constructed in this article, which accounts for the failure characteristics of the parts and material hazard constraints. To assign the disassembly task reasonably, a disassembly priority decision-making model was presented to describe the relationship between the failure layer, the material hazards layer, and the economic feasibility layer. Furthermore, the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) optimization for the MC-RDLBM is improved. To increase the convergence speed of the algorithm, an initial population construction method is designed, which includes the component failure and material hazards. Moreover, a novel genetic algorithm evolution rule with a Pareto non-dominant relation and crowded distance constraint is established, which expands the search scope of the chromosome’s autonomous evolution and avoids local convergence. Furthermore, a Pareto grade-based evaluation strategy for non-dominant solutions is proposed to eliminate the invalid remanufacturing disassembly task sequences. Finally, a case study verified the effectiveness and feasibility of the proposed method.

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“…Most of those methodologies assume that all disassembly line parameters are deterministic. Mixed integer programming (Altekin, 2017;Altekin & Akkan, 2012;Altekin, Bayındır, & Gümüşkaya, 2016;Altekin, Kandiller, & Ozdemirel, 2008;Paksoy, Güngör, Özceylan, & Hancilar, 2013), branch and bound (Li, Cil, Mete & Kucukkoc, 2020), mixed integer linear programming (Edis, Ilgin, & Edis, 2019), dynamic programming (Koc, Sabuncuoglu, & Erel, 2009;Zhou, Guo, & Li, 2020), linear physical programming (Ilgin, Akçay, & Araz, 2017) are some of the techniques used in those methodologies. Some researchers integrate mathematical programming techniques and fuzzy logic (Özceylan & Paksoy, 2014b(Özceylan & Paksoy, , 2014a.…”

Section: Introductionmentioning

confidence: 99%

Disassembly Line Balancing by Using Simulation Optimization

Akpınar

Ilgın

Aktaş

2021

Alphanumeric Journal

Self Cite

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Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.

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Disassembly line balancing with sequencing decisions: A mixed integer linear programming model and extensions

Cited by 23 publications

References 42 publications

Energy-Efficient Optimization of Two-Sided Disassembly Line Balance Considering Parallel Operation and Uncertain Using Multiobjective Flatworm Algorithm

Energy-Efficient Optimization of Two-Sided Disassembly Line Balance Considering Parallel Operation and Uncertain Using Multiobjective Flatworm Algorithm

Optimization of Remanufacturing Disassembly Line Balance Considering Multiple Failures and Material Hazards

Disassembly Line Balancing by Using Simulation Optimization

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