Energy-efficient scheduling for a flexible job shop with machine breakdowns considering machine idle time arrangement and machine speed level selection

Abstract

Reasonable production optimization method can effectively reduce the total energy consumption of flexible job shop. In order to address the dynamic scheduling problem of flexible job shop under the machine breakdowns constraint, a method of calculating machine energy consumption and completion time under different states is proposed. In this method, two energy-saving measures for machining, machine idle time arrangement and machine speed level selection, are considered. A dynamic scheduling mathematical model of flexible job shop with the goals of minimizing the total energy consumption and maximum completion time of machines is then established. A heuristic multi-objective non-dominated genetic ranking algorithm (NSGA-Ⅱ) using real number coding and considering batch transportation rule is proposed to solve this problem. The corresponding Pareto frontier solution sets are generated through base case experiments, and the effects of two energy saving measures and batch transportation rule are evaluated in static and dynamic scheduling, respectively, and finally 15 base cases are cited to evaluate the proposed algorithm. The results show that the model and algorithm can effectively reduce energy consumption and maximum completion time, thus reducing the environmental impact and achieving green energy conservation.