Product disassembly is critically important in recycling end-of-life products, reducing their negative impact on environmental pollution and minimizing resource waste. Disassembly line balancing problems have attracted much attention from researchers and industrial practitioners. Most of the existing studies, however, consider only human disassembly or robot disassembly alone. This work considers human-robot collaboration. It proposes an human-robot collaborative disassembly line balancing model considering stochastic task time, where an AND/OR graph is adopted to describe a product’s disassembly process. The objectives are to maximize the total profit and minimize energy consumption. A Pareto improved multi-objective shuffled frog leaping algorithm with a stochastic simulation strategy is proposed to solve the model. In addition, an elite strategy is introduced in global search to enhance the algorithm’s optimization capability. Through experiments on disassembling products of different sizes, the feasibility and effectiveness of this algorithm are demonstrated. Its comparison with some most popular state-of-the-art methods is performed. <italic>Note to Practitioners</italic>—This paper is motivated by the benefits of human-robot collaboration in the disassembly systems. The presented approach is suitable for disassembly lines with multiple objectives, and the weight of each objective cannot be accurately grasped. Most of the existing operation allocation methods are based on the correlation between humans and robots and the factors affecting disassembly. This paper suggests the selection of humans and robots is completely random and decided by an optimization algorithm. This paper designs an improved multi-objective shuffled frog leaping algorithm based on Pareto’s rule. Experimental results show that this algorithm can be applied to solve practical disassembly line balancing problems.
|Original language||English (US)|
|Number of pages||12|
|Journal||IEEE Transactions on Automation Science and Engineering|
|State||Accepted/In press - 2023|
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering