Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model
This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, bec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/11/1/1 |
| _version_ | 1798000616647163904 |
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| author | Eduardo Guzman Beatriz Andres Raul Poler |
| author_facet | Eduardo Guzman Beatriz Andres Raul Poler |
| author_sort | Eduardo Guzman |
| collection | DOAJ |
| description | This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses. |
| first_indexed | 2024-04-11T11:23:11Z |
| format | Article |
| id | doaj.art-df87114657b94168811eaa4154e8c658 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-11T11:23:11Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-df87114657b94168811eaa4154e8c6582022-12-22T04:26:33ZengMDPI AGComputers2073-431X2021-12-01111110.3390/computers11010001Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical ModelEduardo Guzman0Beatriz Andres1Raul Poler2Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Calle Alarcón 1, 03801 Alcoy, Alicante, SpainResearch Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Calle Alarcón 1, 03801 Alcoy, Alicante, SpainResearch Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València (UPV), Calle Alarcón 1, 03801 Alcoy, Alicante, SpainThis paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses.https://www.mdpi.com/2073-431X/11/1/1schedulingproduction planningmatheuristicgenetic algorithmdisjunctive mathematical model |
| spellingShingle | Eduardo Guzman Beatriz Andres Raul Poler Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model Computers scheduling production planning matheuristic genetic algorithm disjunctive mathematical model |
| title | Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model |
| title_full | Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model |
| title_fullStr | Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model |
| title_full_unstemmed | Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model |
| title_short | Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model |
| title_sort | matheuristic algorithm for job shop scheduling problem using a disjunctive mathematical model |
| topic | scheduling production planning matheuristic genetic algorithm disjunctive mathematical model |
| url | https://www.mdpi.com/2073-431X/11/1/1 |
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