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.

Green Scheduling of Jobs and Flexible Periods of Maintenance in a Two-Machine Flowshop to Minimize Makespan, a Measure of Service Level and Total Energy Consumption

“…Li et al [13] presented a suggested multiobjective optimization algorithm using crossover agents to minimize energy consumption. Considering the speed level and machine turn on/off, the researchers [14][15][16][17] proposed a genetic algorithm to minimize makespan and energy consumption. e basis of the analysis of the algorithms is based on the investigation in physical space.…”

Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm

“…In the rule, position values in 1 X are first ranked in an increasing order, then the operation permutation can be acquired according to the new order, which is shown in Figure 4. For the speed-level selection segment, the conversion process can be modified from the method proposed by Yuan and Xu [40], which can be represented by Equation (19). z(j) denotes the size of alternative speed-level set for the operation corresponding to the jth element, u(j) means the selected speed level, u(j) ∈ [1, z(j)].…”

“…The conversion process is shown in Figure 7. For the speed-level selection segment, the conversion is generally an inverse process of Equation (19). However, there is a special case, that is, z(j) = 1, x(j) is obtained by choosing a random value between [−ε, ε].…”

“…Escamilla et al [18] presented a genetic algorithm to minimize the makespan and the energy consumption. Yin et al [19] developed a multi-objective genetic algorithm based on simplex lattice design to optimize the productivity, energy efficiency, and noise. In view of the complexity of the problem under study, meta-heuristic algorithms can achieve satisfactory scheduling within a reasonable time.…”

Modeling, scheduling, and control in advanced production systems

“…It presents a synchronous switch control method to schedule the system such that it can achieve the performance of one with two inverts. The 11th article, authored by Yin et al, 19 reports a method for job shop scheduling for energy efficiency, productivity increase, and noise reduction by controlling the machine spindle speed. The problem is formulated as a mixed integer programming problem and a genetic algorithm is developed to solve the problem.…”