Human–Robot Collaborative Scheduling in Energy-Efficient Welding Shop

Human–robot collaborative scheduling has been widely applied in modern manufacturing industry. A rational scheduling of human–robot cooperation plays an important role in improving production efficiency. However, human–robot collaborative scheduling problem in welding production has not been studied so far. Thus, this article addresses a human–robot collaborative welding shop scheduling problem (HCWSSP) with minimization objectives of makespan and total energy consumption (TEC). To solve this multiobjective HCWSSP, a Pareto-based memetic algorithm (PMA), which hybridizes a genetic operator and variable neighborhood search (VNS), is presented to obtain a set of tradeoff solutions between makespan and TEC. In PMA, each solution is represented by two parts, i.e., job processing sequence and resource assignment. A novel integrated initialization strategy is proposed to generate one initial population with high quality and good diversity. Furthermore, five kinds of VNS are designed to improve the exploitation capability of PMA. Experimental results on test problems manifest that the proposed PMA performs better than its competitors.