Dynamic flexible job shop scheduling based on multi-dimensional space collaborative guidance evolution

Abstract

With the advancement of green manufacturing, energy-saving scheduling in production systems has become a current research focus. To achieve balanced optimization between completion time and production energy consumption targets, a multi-space guided evolutionary (MSGE) algorithm is designed to solve energy scheduling under machine failures. Additionally, a window hybrid rescheduling strategy is proposed to address the issue of machine malfunctions. Based on the equipment characteristics of the production workshop, the processing equipment is simulated into three different levels to obtain a reasonable configuration between the workpiece and the machine. To balance the convergence and diversity of the population, global and local optimization strategies are adopted to guide the population’s evolution. For the scheduling plan, a low-carbon strategy is adopted to reduce energy consumption in production. MSGE is experimentally compared with other algorithms on test cases, and the results show that the proposed MSGE algorithm outperforms other algorithms in terms of generation distance (GD) and hypervolume (HV) indicators when solving energy-flexible workshop scheduling problems.