Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm

Abstract

Most traditional scheduling problems prioritize optimizing production efficiency, cost, and quality. However, with gradually increasing energy consumption and environmental pollution, the novel "energy-efficient scheduling" model has received increasing attention from scholars and engineers. This scheduling model focuses on reducing workshop energy consumption and environmental pollution and has become a hot topic in the scheduling area. This article proposes a new energy-efficient scheduling mathematical model considering productivity, energy efficiency, and noise reduction with flexible spindle speed for the job shop environment. This model considers the machining spindle speed that impacts the production time, power, and noise to be flexible and an independent decision-making variable. In addition, the evaluation methods of productivity, energy consumption, and noise are presented in this model. To cleanly solve this mixed integer programming model, an effective multi-objective genetic algorithm based on simplex lattice design is proposed. The corresponding encoding/decoding method, fitness function, and crossover/mutation operators are designed based on the features of this problem. To evaluate the performance of this method, three instances with different scales have been designed. The results demonstrate the effectiveness of the proposed model for the energy-efficient job shop scheduling problem.