A multi-population cooperative coevolution artificial bee colony algorithm for partial multi-robotic disassembly line balancing problem considering preventive maintenance scenarios

Existing literature on the robotic disassembly line balancing problem often assume that robots are always in good working condition. But in fact, due to the inevitable aging of the machines, robots may break down unexpectedly. And the original task assignment would become infeasible, which may lead to unplanned shutdowns of the disassembly line. To tackle this situation, a partial multi-robotic disassembly line balancing problem considering preventive maintenance scenarios (PMRDLBP-PM) is proposed. The objectives of the PMRDLBP-PM not only encompass the traditional goals of robotic disassembly line balancing problem, such as cycle time and disassembly profitability, but also take into account the potential additional time and cost incurred from the reconfiguration of workstations necessitated by changes in task allocation. Then, a multi-population cooperative coevolution artificial bee colony (MPCCABC) algorithm is developed. Specifically, to enhance the quality of the initial population and ensure population diversity, the population is divided into four subpopulations, including three high-quality subpopulations generated by heuristic rules based on optimization objectives, and one randomly generated subpopulation. And an adaptive progressive neighborhood search strategy is proposed to improve search efficiency by adjusting the complexity of neighborhood operations based on search feedback. Moreover, a cooperative co-evolution strategy with historical information is adopted to supplement historical optimal information in subpopulation information exchange, increasing computing resource utilization and accelerating convergence speed. Finally, three instances are conducted to test the validity of the proposed model and algorithm. The results demonstrate that the MPCCABC can achieve superior performance for the considered PMRDLBP-PM.