Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives
In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried ou...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/3/1491 |
| _version_ | 1797489079052402688 |
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| author | Jesus Para Javier Del Ser Antonio J. Nebro |
| author_facet | Jesus Para Javier Del Ser Antonio J. Nebro |
| author_sort | Jesus Para |
| collection | DOAJ |
| description | In recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area. |
| first_indexed | 2024-03-10T00:12:22Z |
| format | Article |
| id | doaj.art-b9920b306107449b845572ad609e5d68 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T00:12:22Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b9920b306107449b845572ad609e5d682023-11-23T15:58:28ZengMDPI AGApplied Sciences2076-34172022-01-01123149110.3390/app12031491Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and PerspectivesJesus Para0Javier Del Ser1Antonio J. Nebro2Kurago Software, S.L., Alameda Urquijo 4, 48008 Bilbao, SpainTECNALIA, Basque Research & Technology Alliance (BRTA), P. Tecnologico Bizkaia, Ed. 700, 48160 Derio, SpainDepartamento de Lenguajes y Ciencias de la Computación, ITIS Software, University of Malaga, 29071 Malaga, SpainIn recent years, the application of artificial intelligence has been revolutionizing the manufacturing industry, becoming one of the key pillars of what has been called Industry 4.0. In this context, we focus on the job shop scheduling problem (JSP), which aims at productions orders to be carried out, but considering the reduction of energy consumption as a key objective to fulfill. Finding the best combination of machines and jobs to be performed is not a trivial problem and becomes even more involved when several objectives are taken into account. Among them, the improvement of energy savings may conflict with other objectives, such as the minimization of the makespan. In this paper, we provide an in-depth review of the existing literature on multi-objective job shop scheduling optimization with metaheuristics, in which one of the objectives is the minimization of energy consumption. We systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. The manuscript also informs with empirical results the main findings of our bibliographic critique with a performance comparison among representative multi-objective evolutionary solvers applied to a diversity of synthetic test instances. The ultimate goal of this article is to carry out a critical analysis, finding good practices and opportunities for further improvement that stem from current knowledge in this vibrant research area.https://www.mdpi.com/2076-3417/12/3/1491job shop schedulingenergy efficiencymetaheuristicsmulti-objective optimization |
| spellingShingle | Jesus Para Javier Del Ser Antonio J. Nebro Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives Applied Sciences job shop scheduling energy efficiency metaheuristics multi-objective optimization |
| title | Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives |
| title_full | Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives |
| title_fullStr | Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives |
| title_full_unstemmed | Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives |
| title_short | Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives |
| title_sort | energy aware multi objective job shop scheduling optimization with metaheuristics in manufacturing industries a critical survey results and perspectives |
| topic | job shop scheduling energy efficiency metaheuristics multi-objective optimization |
| url | https://www.mdpi.com/2076-3417/12/3/1491 |
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