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