Grey Wolf Optimizer Adapted for Disassembly Sequencing Problems

One of important industrial problems is related to the recovery, reuse, and disposal of products at their end-of-life (EOL). There are a vast number of methods to perform such operations; however, each method is associated with different cost. To harvest materials from an EOL product while remaining environmentally conscious, an optimal, or at least near-optimal, disassembly sequence is necessary to ensure the efficiency, sustainability, and economic viability of a remanufacturing plant. Because the number of possible sequences increases factorially, a heuristic algorithm is an effective time-efficient method for finding a near-optimal sequence. This paper explores a recent novel optimization algorithm named Grey Wolf Optimizer (GWO) and its application to the disassembly sequencing problem. Since GWO is an algorithm designed for continuous optimization problems, it cannot be directly applied to a disassembly sequencing problem that is discrete. Therefore, keeping the main ideas of GWO intact, a Sequencing GWO (SGWO) is proposed for the first time to specifically solve this problem. Experimental results have shown that SGWO performs better than some existing algorithms, such as Genetic Algorithm and a Teacher-Learner-based Optimizer Algorithm previously used to solve this problem.