Solving Disassembly Line Balancing Problems with Fuzzy Parameters Using an Artificial Intelligence Technique - Watanasungsuit, Arnat; Jomtong, Peerapop; Inthawongse, Choat; Pornsing, Choosak (Science & Technology Asia, 2025)
End-of-life (EOL) product recovery has become a critical issue driven by economic, social, and environmental concerns, along with stricter environmental regulations that emphasize disassembly and product recovery. Disassembly lines are used to dismantle EOL products into reusable components, but their efficiency depends heavily on accurately estimating task times, which are often uncertain. Since average task times cannot fully represent this uncertainty, task time can instead be modeled as a fuzzy number, allowing fuzzy logic to quantify representative values. This study introduces a disassembly line balancing problem where processing times are expressed as fuzzy numbers and solved using particle swarm optimization (PSO). The optimization aims to minimize the number of workstations, total idle time, maximum disassembly cost, and direction changes. The proposed method was benchmarked against LINGO and GUROBI solvers. Computational experiments, using the number of non-inferior solutions as a stopping criterion, demonstrated that the PSO-based approach achieved superior results. The findings indicate that the proposed method effectively outperforms existing algorithms, providing efficient and promising solutions for EOL product recovery and disassembly optimization.