Energy-Saving Production Scheduling in a Single-Machine Manufacturing System by Improved Particle Swarm Optimization

Qingquan Jiang,Rui Zhang,Qiaozhen Lin,Xiaoya Liao,Chenxi Huang

doi:10.1155/2020/8870917

Abstract

A single-machine scheduling problem that minimizes the total weighted tardiness with energy consumption constraints in the actual production environment is studied in this paper. Based on the properties of the problem, an improved particle swarm optimization (PSO) algorithm embedded with a local search strategy (PSO-LS) is designed to solve this problem. To evaluate the algorithm, some computational experiments are carried out using PSO-LS, basic PSO, and a genetic algorithm (GA). Before the comparison experiment, the Taguchi method is used to select appropriate parameter values for these three algorithms since heuristic algorithms rely heavily on their parameters. The experimental results show that the improved PSO-LS algorithm has considerable advantages over the basic PSO and GA, especially for large-scale problems.

Highlights

Manufacturing is an important industry that consumes about one-third of the world’s energy [1] and 56% of China’s energy [2]
Che [7] focused on biobjective optimization to minimize total energy consumption and maximum tardiness in a single-machine scheduling problem considering a power-down mechanism. e problem was built as a mixed integer linear programming (MILP) model. ey developed exact and approximate algorithms for small- and large-scale problems
For the genetic algorithm (GA) algorithm, the size of population, the probability of crossover pc, the probability of mutation pm, and the other two parameters relating to mutations θ and D are 200, 0.5, 0.9, 0.99, and 0.95 for the small-scale problem and 200, 0.3, 0.9, 0.99, and 0.95 for the large-scale problem, respectively.In order to verify the effectiveness of selected levels of factors by the Taguchi method, the responses of the selected parameter values need to be compared with that of all the level combinations displayed in Figures 5 and 6

Summary

Introduction

Manufacturing is an important industry that consumes about one-third of the world’s energy [1] and 56% of China’s energy [2]. Modos et al [6] studied robust single-machine scheduling to minimize release times and total tardiness with periodic energy consumption limits. Che et al [8] established a new continuous-time mixed integer programming model for an energy-sensitive single-machine scheduling problem and proposed a greedy insertion algorithm to reduce the total electricity cost of production, which provided energysaving solution for a large-scale single-machine scheduling problem, such as 5000 jobs, in a few tens of seconds. In [12], a memetic differential evolution (MDE) algorithm with superior performances to strength Pareto evolutionary algorithm II (SPEA-II) and nondominated sorting genetic algorithm II (NSGA-II) was proposed to solve an energy-saving biobjective unrelated parallel-machine scheduling problem to minimize maximum completion time and total energy consumption.

Problem Definition

Objective function value

Heuristic Algorithms

Computational Experiments

Conclusion