关键词: 共享制造, 人机协同, 服务组合优选, 自适应调度, 改进启发式算法

Abstract: Human machine collaboration (HRC) provides a new way to solve the problem of personalized requirements in shared manufacturing. At present, the research on the optimization of shared manufacturing service composition mostly aims at the interests of supply and demand, ignoring the human factors in the process of service composition, which makes the optimization of service composition difficult to meet the requirements of HRC. In order to make up for this deficiency and further improve the optimization effect, firstly, a four-dimensional optimization indicator system of time, cost, carbon emissions and matching degree is established considering the task attribute and HRC task allocation process. Secondly, considering the service occupancy constraint, the scheduling problem is added to the optimization model, and an adaptive scheduling method is proposed; Considering the differences of transportation mode load, speed, cost and energy consumption in the process of service composition, the sub problem of transportation composition optimization is solved. Finally, an update operator based on Hamming distance and Manhattan distance is proposed, which is applied to the improvement of gray wolf optimizer and whale optimization algorithm to improve the adaptability of the algorithm to the service composition problem. The simulation results show that the division of labor of service composition optimization results from the perspective of HRC is reasonable, which reduces the human load; The adaptive scheduling method improves the success rate of the scheme; The improved algorithm has stronger optimization ability and robustness.

Key words: Service composition optimization, Human-robot collaboration, Adaptive scheduling, Improved heuristic algorithm