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

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