Hybrid self-adaptive biobjective optimization of multiple robot scheduling problem for mixed-model assembly lines considering energy savings

Abstract

With the wide application of robots in the material distribution process on the assembly lines, single robot scheduling cannot meet the actual production needs. However, the high degree of mechanization also brings about environmental problems. Therefore, this article aims to develop a scheduling methodology to accomplish material supply tasks using multiple robots with energy consumption consideration. Meanwhile, a targeted mathematical model to minimize total weighted penalty costs and total energy consumption is developed. Due to the NP-hard nature of the problem, an adaptive hybrid mutation population extremal optimization multi-objective algorithm based on uniform distribution selection is proposed to solve multi-objective problems. Furthermore, a new coding method for initialization is designed to optimize the whole iterative process. The performance of the proposed algorithm is evaluated by comparing with three benchmark multi-objective algorithms. Computational experiments are represented to prove the validity and feasibility of the proposed algorithm.