Line Balancing Problem with Multi-Manned Workstations and Resource Constraints: The Case of Electronics Waste Disassembly

The increasing public awareness of environmental protection and the scarcity of rare earth elements have made closed-loop supply chains a necessity in many sectors. In particular, recycling components and parts from end-of-life consumer electronics have drawn the attention of both academics and practitioners. Disassembly line balancing improves the resource efficiency of recycling operations. This study proposes a new mathematical formulation and hybrid metaheuristics for solving the Disassembly Line Balancing Problem (DLBP) considering multi-manned workstations and resource constraints. The transformed AND/OR graph is used for prioritizing disassembly tasks in the modeling process. The method is applied for optimizing a real-world case of laptop disassembly to showcase the usefulness of the approach. The performance of the developed metaheuristics is compared to minimize the number of workstations, operators, and machines involved in the disassembly operations. Further, the results are analyzed through sensitivity analysis. This study concludes by providing practical insights and suggestions for the future development of DLBPs

Multi-objective volleyball premier league algorithm

This paper proposes a novel optimization algorithm called the Multi-Objective Volleyball Premier League (MOVPL) algorithm for solving global optimization problems with multiple objective functions. The algorithm is inspired by the teams competing in a volleyball premier league. The strong point of this study lies in extending the multi-objective version of the Volleyball Premier League algorithm (VPL), which is recently used in such scientific researches, with incorporating the well-known approaches including archive set and leader selection strategy to obtain optimal solutions for a given problem with multiple contradicted objectives. To analyze the performance of the algorithm, ten multi-objective benchmark problems with complex objectives are solved and compared with two well-known multiobjective algorithms, namely Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Computational experiments highlight that the MOVPL outperforms the two state-of-the-art algorithms on multi-objective benchmark problems. In addition, the MOVPL algorithm has provided promising results on well-known engineering design optimization problems