Multi-objective heterogeneous interactive human-robot collaborative disassembly line balancing with partial destructive mode in type-2 fuzzy environment

Interactive human-robot collaborative disassembly lines represent a prominent application of the Industry 5.0 paradigm, brought to life by the synergy of industrial information integration and advanced automation. This advanced production model enhances recycling efficiency for End-of- Life products by integrating the complementary strengths of humans and robots, enabling their cooperative or parallel execution of disassembly tasks. However, balancing such lines remains challenging. Existing research has limited consideration of the uncertainties in the disassembly process and the heterogeneity of operators, typically adopting non-destructive disassembly modes that are not always feasible in practice, thereby limiting their applicability. Therefore, this study proposes a fuzzy partial destructive heterogeneous interactive human-robot collaborative disassembly line balancing problem. The problem supports flexible configurations of diverse operators, employs interval type-2 triangular fuzzy sets to represent uncertainties during disassembly more precisely, and introduces a partial destructive disassembly mode. It aims to minimise the number of active workstations, differences in operator idle time, and disassembly energy consumption. Given the NP-hard nature of this problem, a multi-objective discrete bees algorithm is developed. This algorithm adopts a two-parameter bees algorithm search framework, incorporating four scout bee initialisation rules, five neighbourhood search operators, an adaptive operator selection strategy, and two enhanced search operators. The proposed model and algorithm are applied to two disassembly scenarios involving a retired power battery and a transmission. Further comparative analysis shows that the proposed method improves disassembly performance. To further understand the behaviour and performance of the model, we also conduct comprehensive sensitivity analyses. Finally, comparative experiments are performed against the GUROBI solver and five other advanced algorithms, validating that the proposed algorithm exhibits superior performance.