Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm
In this work, we seek to design a model that contributes to the study and resolution of a multi-objective rescheduling problem in dynamic permutation flow shop contexts. In this type of problem, where the objectives can be valued in heterogeneous units, the difficulty of achieving an optimal solutio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/14/2395 |
| _version_ | 1797417202665652224 |
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| author | Pablo Valledor Alberto Gomez Javier Puente Isabel Fernandez |
| author_facet | Pablo Valledor Alberto Gomez Javier Puente Isabel Fernandez |
| author_sort | Pablo Valledor |
| collection | DOAJ |
| description | In this work, we seek to design a model that contributes to the study and resolution of a multi-objective rescheduling problem in dynamic permutation flow shop contexts. In this type of problem, where the objectives can be valued in heterogeneous units, the difficulty of achieving an optimal solution leads to finding a set of non-dominated efficient solutions (also called Pareto front). On the other hand, we will also consider the potential appearance of disruptions in planned scheduling (such as machine breakdowns or arrival of new priority jobs) that require a rapid re-planning of the aforementioned scheduling. In this paper, a hybrid dynamic non-dominated sorting genetic II metaheuristic (HDNSGA-II) is proposed to find the optimal Pareto front. The algorithm is applied to a benchmark already tested in previous studies, defined by three conflicting objective functions (makespan, total weighted tardiness, and stability) and three different types of disruption (machine breakdowns, incorporation of new jobs, and modifications in process times). According to the statistical comparison performed, the HDNSGA-II algorithm performs better in the designed environment, especially in larger problems. |
| first_indexed | 2024-03-09T06:15:22Z |
| format | Article |
| id | doaj.art-26bfecb36d774a09a1129f4d1d72e870 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T06:15:22Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-26bfecb36d774a09a1129f4d1d72e8702023-12-03T11:53:25ZengMDPI AGMathematics2227-73902022-07-011014239510.3390/math10142395Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II AlgorithmPablo Valledor0Alberto Gomez1Javier Puente2Isabel Fernandez3Global R & D Asturias, ArcelorMittal Inc., 33400 Avilés, SpainDepartment of Business Administration, Polytechnic School of Engineering, University of Oviedo, 33204 Gijon, SpainDepartment of Business Administration, Polytechnic School of Engineering, University of Oviedo, 33204 Gijon, SpainDepartment of Business Administration, Polytechnic School of Engineering, University of Oviedo, 33204 Gijon, SpainIn this work, we seek to design a model that contributes to the study and resolution of a multi-objective rescheduling problem in dynamic permutation flow shop contexts. In this type of problem, where the objectives can be valued in heterogeneous units, the difficulty of achieving an optimal solution leads to finding a set of non-dominated efficient solutions (also called Pareto front). On the other hand, we will also consider the potential appearance of disruptions in planned scheduling (such as machine breakdowns or arrival of new priority jobs) that require a rapid re-planning of the aforementioned scheduling. In this paper, a hybrid dynamic non-dominated sorting genetic II metaheuristic (HDNSGA-II) is proposed to find the optimal Pareto front. The algorithm is applied to a benchmark already tested in previous studies, defined by three conflicting objective functions (makespan, total weighted tardiness, and stability) and three different types of disruption (machine breakdowns, incorporation of new jobs, and modifications in process times). According to the statistical comparison performed, the HDNSGA-II algorithm performs better in the designed environment, especially in larger problems.https://www.mdpi.com/2227-7390/10/14/2395schedulingmulti-objectivedynamic schedulingpredictive-reactivegreedy |
| spellingShingle | Pablo Valledor Alberto Gomez Javier Puente Isabel Fernandez Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm Mathematics scheduling multi-objective dynamic scheduling predictive-reactive greedy |
| title | Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm |
| title_full | Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm |
| title_fullStr | Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm |
| title_full_unstemmed | Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm |
| title_short | Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments with Multiple Objectives Using the Hybrid Dynamic Non-Dominated Sorting Genetic II Algorithm |
| title_sort | solving rescheduling problems in dynamic permutation flow shop environments with multiple objectives using the hybrid dynamic non dominated sorting genetic ii algorithm |
| topic | scheduling multi-objective dynamic scheduling predictive-reactive greedy |
| url | https://www.mdpi.com/2227-7390/10/14/2395 |
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