An Enhanced Social Engineering Optimizer for Solving an Energy-Efficient Disassembly Line Balancing Problem Based on Bucket Brigades and Cloud Theory

A disassembly line is an industrialized and automated production line which should be scheduled with high production efficiency. Although many disassembly line balancing optimization studies are contributed recently, they increase or reduce the number of workstations to balance the disassembly line. From real-world managerial settings, an increase or decrease workstations, is too expensive and not realistic. The bucket brigades' disassembly line is self-balancing and self-organizing, which is not constrained by the workstation beat time and only needs to distribute workers on the line according to certain rules to achieve line balancing after a period of time. In this article, a bucket brigades disassembly line balancing optimization method considering uncertainty is proposed, in which a cloud model is used to represent the uncertain disassembly time. The proposed model handles multiple objectives including smoothness, disassembly cost and disassembly energy consumption to be minimized. To solve this complex problem, this article innovates a new heuristic method based on the social engineering optimizer as an enhanced local search metaheuristic. Finally, a ball collector is used to verify the effectiveness of the proposed method and extensive analysis is done to compare the performance of proposed model with other recent algorithms.

Automated summary

This article proposes a bucket brigades disassembly line balancing optimization method that considers uncertainty, using a cloud model to represent uncertain disassembly time. The proposed method handles multiple objectives, such as smoothness, cost, and energy consumption, to be minimized. It also innovates a new heuristic method based on the social engineering optimizer to solve this complex problem.