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

• Published in: Journal of cleaner production 2019-11, Vol.238, p.117826, Article 117826
• Main Authors: Edis, Emrah B., Ilgin, Mehmet Ali, Edis, Rahime Sancar
• Format: Article
• Language: English
• Subjects: Disassembly line balancing; Mixed integer linear programming; Sequencing
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2019.117826

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. •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.