Synergetic energy-conscious scheduling optimization of part feeding systems via a novel chaotic reference-guided policy

Purpose This paper aims to investigate a multi-objective electric vehicle's (EV's) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks. Design/methodology/approach A chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance. Findings The involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries. Originality/value This research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

Automated summary

This paper investigates the synergetic scheduling problem of multi-objective electric vehicles in the automotive industry. It proposes a synergetic delivery mechanism to coordinate multiple EVs for part feeding tasks. A chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is developed, incorporating a novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy. The computational results demonstrate the superior performance of CRMSL compared to other benchmark algorithms, which is inspiring for future research on energy-efficient co-scheduling in manufacturing industries.