An adaptive large neighbourhood search-based optimisation for economic co-scheduling of mobile robots

The growing energy consumption and environmental pressures call for economic and friendly green manufacturing. The paper creatively studies an economic part feeding scheduling problem with a cooperative mechanism to coordinate multiple mobile robots in the fullest sense. When solving the economic co-scheduling problem in mixed-model assembly lines, this paper jointly considers the objective of energy saving as well as the robot employment cost, which incorporates traditional performance criterion with growing energy concerns. In order to improve the performance and diversity of solutions, a multi-phase adaptive search (MPAS) algorithm is proposed which is integrated with clustering heuristics, specific destroy and repair rules, adaptive selection and perturbation strategy. Computational experiments are conducted in order to test and verify the effectiveness and efficiency of the proposed MPAS algorithm. Comparison tests are carried out between the proposed MPAS and two widely-applied benchmark algorithms. The results obtained in this study might be inspiring for future studies on energy-efficient cooperative scheduling topics. [Received 16 February 2018; Revised 22 May 2018; Accepted 22 June 2018]