A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization

Han Wang, Zhigang Jiang, Yan Wang, Hua Zhang, Yanhong Wang

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving.

Original languageEnglish
Pages (from-to)575-588
Number of pages14
JournalJournal of Cleaner Production
Volume188
DOIs
Publication statusPublished - 27 Mar 2018

Fingerprint

Energy conservation
Scheduling
manufacturing
energy
genetic algorithm
Genetic algorithms
Flexible manufacturing systems
Nonlinear programming
Machine tools
Particle swarm optimization (PSO)
Job shop scheduling
energy saving
method
shop
Energy saving
Energy
Manufacturing
Genetic algorithm

Keywords

  • Energy consumption
  • Energy-saving scheduling
  • Flexible job-shop scheduling problem
  • Modified genetic algorithm
  • Particle swarm optimization

Cite this

@article{0eb16d8f58a04f87bd5f43583ffdbae5,
title = "A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization",
abstract = "Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving.",
keywords = "Energy consumption, Energy-saving scheduling, Flexible job-shop scheduling problem, Modified genetic algorithm, Particle swarm optimization",
author = "Han Wang and Zhigang Jiang and Yan Wang and Hua Zhang and Yanhong Wang",
year = "2018",
month = "3",
day = "27",
doi = "10.1016/j.jclepro.2018.03.254",
language = "English",
volume = "188",
pages = "575--588",
journal = "Journal of Cleaner Production",
issn = "0959-6526",

}

A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization. / Wang, Han; Jiang, Zhigang; Wang, Yan; Zhang, Hua; Wang, Yanhong.

In: Journal of Cleaner Production, Vol. 188, 27.03.2018, p. 575-588.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization

AU - Wang, Han

AU - Jiang, Zhigang

AU - Wang, Yan

AU - Zhang, Hua

AU - Wang, Yanhong

PY - 2018/3/27

Y1 - 2018/3/27

N2 - Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving.

AB - Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving.

KW - Energy consumption

KW - Energy-saving scheduling

KW - Flexible job-shop scheduling problem

KW - Modified genetic algorithm

KW - Particle swarm optimization

UR - http://www.scopus.com/inward/record.url?scp=85047493844&partnerID=8YFLogxK

U2 - 10.1016/j.jclepro.2018.03.254

DO - 10.1016/j.jclepro.2018.03.254

M3 - Article

VL - 188

SP - 575

EP - 588

JO - Journal of Cleaner Production

JF - Journal of Cleaner Production

SN - 0959-6526

ER -