Elsevier

Journal of Cleaner Production

Volume 238, 20 November 2019, 117826
Journal of Cleaner Production

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

https://doi.org/10.1016/j.jclepro.2019.117826Get rights and content

Highlights

  • Proposed MILP model considers both sequencing and disassembly line balancing issues.

  • The test instances with up to 30 disassembly tasks were solved to optimality.

  • In four test instances, the MILP model improved the best solution(s) found so far.

  • Significant reductions in CPU times were realized by using proposed balance metric.

  • Effects of the proposed hazard, demand and direction measures were demonstrated.

Abstract

Due to the acceleration of technological developments and shortening of product life cycles, product recovery has gained great importance in recent years. Disassembly line balancing (DLB) problem is one of the most important problems encountered during disassembly operations in product recovery. In this study, a single model and complete DLB problem with balancing issues, hazardousness of parts, demand quantities and direction changes is considered. Majority of DLB studies in the literature solve this problem using heuristics or metaheuristics which do not guarantee the optimality. Although a few studies present mathematical formulations for some variants of this problem, they prefer to solve the problem by using heuristics or metaheuristics due to the non-linear structure and combinatorial nature of the problem. In this study, a generic mixed integer linear programming (MILP) model is developed for the investigated problem and its performance is tested through a series of benchmark instances. The computational results demonstrate that the proposed MILP model is able to solve test instances with up to 30 tasks. Hence, it can effectively be utilized to evaluate the optimality performance of DLB approaches. Moreover, several extensions on the MILP model regarding to line balancing, hazardousness and demand of parts and direction changes are proposed and their effects are analyzed through computational studies.

Introduction

Environment-friendly treatment of end-of-life (EOL) products has received growing attention of research institutions and companies in recent years due to increasing environmental awareness, stricter governmental regulations and short courtship of products by customers (Wang et al., 2019a). This has increased the importance of product recovery that involves the recovery of materials and components from EOL products (Ilgin, 2019). Disassembly which can be defined as the separation of a product into its constituent parts is the most critical operation in a product recovery system since all product recovery options (e.g., recycling, remanufacturing) require the disassembly of a returned product at a certain level (Gungor and Gupta, 1999, McGovern and Gupta, 2011).

Although different layout options are available for disassembly, the highest efficiency can be achieved by carrying out disassembly operations on a disassembly line (Güngör and Gupta, 2002). Similar to assembly lines, disassembly lines must be balanced in order to minimize the number of workstations and the variability in processing times of workstations. Besides these two objectives, several other disassembly-related objectives should also be considered in disassembly line balancing (DLB). For instance, hazardous parts should be disassembled as early as possible in order to reduce the probability of an unexpected event (e.g. explosions). Hence, a DLB approach may have an objective of assigning hazardous parts to the earliest positions in disassembly sequence. A detailed review of other disassembly-related line balancing objectives can be found in Özceylan et al. (2019) and Deniz and Ozcelik (2019).

Various solution approaches were presented for DLB problems. The heuristic procedure proposed by Güngör and Gupta (2001) was the first DLB approach. Due to their simplicity and ease of implementation, several other heuristic procedures were developed (Avikal et al., 2014a, Ilgin, 2019). Combinatorial nature of DLB problem stimulated research on the development of metaheuristics-based DLB methodologies (McGovern and Gupta, 2007a, McGovern and Gupta, 2007b; Wang et al., 2019b). Since heuristics and metaheuristics do not guarantee the optimality, several researchers developed DLB approaches based on exact solution methodologies including mixed integer linear programming (MILP) (Kalaycılar et al., 2016) and dynamic programming (Koc et al., 2009). Although those methodologies provide optimal solutions, they do not consider sequencing of disassembly tasks and sequence-based performance measures such as hazard measure which tries to assign hazardous tasks to the earliest positions in disassembly sequence. In this study, we fill this research gap by developing and solving a generic MILP model for the DLB problem with sequencing decisions. Moreover, several extensions on the objective functions of the proposed MILP model are defined and their effects are demonstrated by using sample cases.

The remainder of the paper is organized as follows. The next section gives a compact review of DLB literature. Section 3 presents the problem definition, the proposed MILP model and the computational results. Section 4 introduces several extensions to the proposed MILP model and gives their implementation details. Finally, conclusions and future research directions are presented in Section 5.

Section snippets

Literature review

The criticality of disassembly operations in product recovery has fueled the research on different aspects of disassembly (Gungor and Gupta, 1999, Ilgin and Gupta, 2010). Among them, DLB has received increasing attention of researchers in recent years (Özceylan et al., 2019, Deniz and Ozcelik, 2019). In this section, a review of DLB studies was conducted by covering published and accepted journal papers in scientific English language from 2001 to 2019. Google Scholar database was searched for

Problem definition and generic mixed integer linear programming model

A single model and complete DLB problem with balancing issues, hazardousness of parts, demand quantities and direction changes is investigated in this study. To be formally defined, there exists a set of disassembly tasks to be assigned to a set of potential workstations subject to a pre-specified cycle time. The following five objective functions are considered in a preemptive lexicographic order (McGovern and Gupta, 2007a):

  • (1)

    Minimize the total number of workstations (NWS) needed.

  • (2)

    Distribute the

Extensions on the generic MILP model

This section presents a number of extensions on the MILP model and analyzes their effects by carrying out computational studies in the following sub-sections.

Conclusions and further research

In this study, a generic MILP model was developed in order to solve a single model and complete DLB problem with balancing issues, hazardousness of parts, demand quantities and direction changes. The proposed MILP model and its several extensions were analyzed through a number of test instances/cases.

The following managerial insights were derived from this study:

  • The proposed MILP model can be used as a reference model to determine the optimality gap of other DLB approaches.

  • The proposed linear

References (49)

  • S.M. McGovern et al.

    A balancing method and genetic algorithm for disassembly line balancing

    Eur. J. Oper. Res.

    (2007)
  • S. Mete et al.

    A solution approach based on beam search algorithm for disassembly line balancing problem

    J. Manuf. Syst.

    (2016)
  • E. Özceylan et al.

    Modeling and optimizing the integrated problem of closed-loop supply chain network design and disassembly line balancing

    Transp. Res. E Logist. Transp. Rev.

    (2014)
  • Y. Ren et al.

    Disassembly line balancing problem using interdependent weights-based multi-criteria decision making and 2-Optimal algorithm

    J. Clean. Prod.

    (2018)
  • K. Wang et al.

    Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit

    J. Clean. Prod.

    (2019)
  • Z. Zhang et al.

    A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem

    Expert Syst. Appl.

    (2017)
  • S. Agrawal et al.

    A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem

    Int. J. Prod. Res.

    (2008)
  • F.T. Altekin

    A comparison of piecewise linear programming formulations for stochastic disassembly line balancing

    Int. J. Prod. Res.

    (2017)
  • F.T. Altekin et al.

    Task-failure-driven rebalancing of disassembly lines

    Int. J. Prod. Res.

    (2012)
  • F.T. Altekin et al.

    Profit-oriented disassembly-line balancing

    Int. J. Prod. Res.

    (2008)
  • S. Avikal et al.

    A Fuzzy AHP and PROMETHEE method-based heuristic for disassembly line balancing problems

    Int. J. Prod. Res.

    (2014)
  • S. Avikal et al.

    A PROMETHEE method based heuristic for disassembly line balancing problem

    Ind. Eng. Manag. Syst.

    (2013)
  • M.L. Bentaha et al.

    An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times

    Int. J. Prod. Res.

    (2015)
  • M.L. Bentaha et al.

    Profit-oriented partial disassembly line design: dealing with hazardous parts and task processing times uncertainty

    Int. J. Prod. Res.

    (2018)
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