Hybrid evolutionary algorithm for stochastic multiobjective disassembly line balancing problem in remanufacturing

With the development of the industrial economy and the accelerated renewal of products, many end-of-life products (EOL) have been generated to pollute our environment. This fact highlights the importance of recycling and remanufacturing EOL products as an active research topic. An efficient disassembly line is one solution for improving the remanufacturing and recycling processes of EOL products while reducing the environmental pollution. Although many optimization models and intelligent algorithms were developed to address the disassembly line balancing problem (DLBP), uncertainty was ignored by them. To alleviate the drawbacks of uncertainty for the disassembly operation, this study proposes a stochastic multi-objective optimization model for the DLBP minimizing the disassembly idle rate, smoothness, and energy consumption generated during the operation under uncertain operation time. Another novelty of this paper is to present an improved version of the northern goshawk optimization algorithm using a stochastic simulation method to solve the proposed model. The feasibility of the proposed model and the applicability of the developed algorithm are shown by two extensive examples. Finally, the performance of the proposed algorithm is revealed by a comparison with recent and state-of-the-art algorithms from the literature.