Energy-Saving Production Scheduling in a Single-Machine Manufacturing System by Improved Particle Swarm Optimization
Joint Authors
Jiang, Qingquan
Liao, Xiaoya
Zhang, Rui
Lin, Qiaozhen
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-05
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Jiang, Qingquan& Liao, Xiaoya& Zhang, Rui& Lin, Qiaozhen. 2020. Energy-Saving Production Scheduling in a Single-Machine Manufacturing System by Improved Particle Swarm Optimization. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1201793
Modern Language Association (MLA)
Jiang, Qingquan…[et al.]. Energy-Saving Production Scheduling in a Single-Machine Manufacturing System by Improved Particle Swarm Optimization. Mathematical Problems in Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1201793
American Medical Association (AMA)
Jiang, Qingquan& Liao, Xiaoya& Zhang, Rui& Lin, Qiaozhen. Energy-Saving Production Scheduling in a Single-Machine Manufacturing System by Improved Particle Swarm Optimization. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1201793
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201793