Aggregated planning to solve multi-product multi-period disassembly line balancing problem by considering multi-manned stations: A generic optimization model and solution algorithms

Disassembly lines are crucial in the recovery process of end-of-life (EOL) products, facilitating the systematic extraction of parts to meet predetermined objectives. While previous studies have made significant advancements, they have not addressed the disassembly line planning and balancing problem by focusing on the real-life significance of combining long-term decision-making and short-term operational missions. This study fills this gap by combining multi-product, multi-period, and multi-manned disassembly line concepts into a novel integrated approach. Initially, a generic optimization model is formulated, incorporating the concept of multi-manned stations. Subsequently, genetic algorithm (GA)-based solution approaches are developed to address the problem's complexity. Three tactical-level policies, including economic disassembly quantity (EDQ), just-in-time (JIT), and random (R) based lot size policies, are explored within the algorithms. Additionally, simulated annealing-based local search algorithms and two crossover operators, CR1 and CR2, are incorporated to enhance solution quality. Through computational analysis, six algorithms are evaluated, with the GACR1-JIT algorithm demonstrating significant cost reductions compared to alternatives. The findings underscore the growing importance of JIT-based lot size policies, particularly with an increasing number of periods. This research bridges theoretical and practical considerations by highlighting the strategic importance of combining long-term disassembly line planning and short-term lot sizing decisions to improve system performance.