A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem

摘要: For environmentally conscious and sustainable manufacturing, manufacturers need to incorporate product recovery by designing manufacturing systems include reverse considering both assembly disassembly systems. Just as the line is considered most efficient way assemble a product, seen be disassemble product. While having some similarities assembly, not of process. The challenge lies in fact that it possesses unique characteristics. In this paper, we consider sequence-dependent balancing problem (SDDLBP) concerned with assignment tasks set ordered workstations while satisfying precedence constraints optimizing effectiveness several measures part removal time increments. SDDLBP trivial since proven NP-complete. Further complications occur multiple objectives including environmental economic goals are often contradictory. Therefore, essential an methodology developed. A new approach based on particle swarm optimization algorithm neighborhood-based mutation operator proposed solve SDDLBP. Case scenarios considered, comparisons ant colony optimization, river formation dynamics, tabu search approaches provided demonstrate superior functionality algorithm.