Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments
The aim of this paper is to analyse, model, and solve the rescheduling problem in dynamic permutation flow shop environments while considering several criteria to optimize. Searching optimal solutions in multiobjective optimization problems may be difficult as these objectives are expressing differe...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2862186 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850169455505571840 |
|---|---|
| author | Pablo Valledor Alberto Gomez Paolo Priore Javier Puente |
| author_facet | Pablo Valledor Alberto Gomez Paolo Priore Javier Puente |
| author_sort | Pablo Valledor |
| collection | DOAJ |
| description | The aim of this paper is to analyse, model, and solve the rescheduling problem in dynamic permutation flow shop environments while considering several criteria to optimize. Searching optimal solutions in multiobjective optimization problems may be difficult as these objectives are expressing different concepts and are not directly comparable. Thus, it is not possible to reduce the problem to a single-objective optimization, and a set of efficient (nondominated) solutions, a so-called Pareto front, must be found. Moreover, in manufacturing environments, disruptive changes usually emerge in scheduling problems, such as machine breakdowns or the arrival of new jobs, causing a need for fast schedule adaptation. In this paper, a mathematical model for this type of problem is proposed and a restarted iterated Pareto greedy (RIPG) metaheuristic is used to find the optimal Pareto front. To demonstrate the appropriateness of this approach, the algorithm is applied to a benchmark specifically designed in this study, considering three objective functions (makespan, total weighted tardiness, and steadiness) and three classes of disruptions (appearance of new jobs, machine faults, and changes in operational times). Experimental studies indicate the proposed approach can effectively solve rescheduling tasks in a multiobjective environment. |
| format | Article |
| id | doaj-art-a1dc83edc63741e08c57dafd905fd563 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a1dc83edc63741e08c57dafd905fd5632025-08-20T02:20:42ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/28621862862186Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop EnvironmentsPablo Valledor0Alberto Gomez1Paolo Priore2Javier Puente3ArcelorMittal Inc., Global R&D Asturias, Gijón, SpainUniversity of Oviedo, Department of Business Administration, Polytechnic School of Engineering, 33203 Gijón, SpainUniversity of Oviedo, Department of Business Administration, Polytechnic School of Engineering, 33203 Gijón, SpainUniversity of Oviedo, Department of Business Administration, Polytechnic School of Engineering, 33203 Gijón, SpainThe aim of this paper is to analyse, model, and solve the rescheduling problem in dynamic permutation flow shop environments while considering several criteria to optimize. Searching optimal solutions in multiobjective optimization problems may be difficult as these objectives are expressing different concepts and are not directly comparable. Thus, it is not possible to reduce the problem to a single-objective optimization, and a set of efficient (nondominated) solutions, a so-called Pareto front, must be found. Moreover, in manufacturing environments, disruptive changes usually emerge in scheduling problems, such as machine breakdowns or the arrival of new jobs, causing a need for fast schedule adaptation. In this paper, a mathematical model for this type of problem is proposed and a restarted iterated Pareto greedy (RIPG) metaheuristic is used to find the optimal Pareto front. To demonstrate the appropriateness of this approach, the algorithm is applied to a benchmark specifically designed in this study, considering three objective functions (makespan, total weighted tardiness, and steadiness) and three classes of disruptions (appearance of new jobs, machine faults, and changes in operational times). Experimental studies indicate the proposed approach can effectively solve rescheduling tasks in a multiobjective environment.http://dx.doi.org/10.1155/2020/2862186 |
| spellingShingle | Pablo Valledor Alberto Gomez Paolo Priore Javier Puente Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments Complexity |
| title | Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments |
| title_full | Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments |
| title_fullStr | Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments |
| title_full_unstemmed | Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments |
| title_short | Modelling and Solving Rescheduling Problems in Dynamic Permutation Flow Shop Environments |
| title_sort | modelling and solving rescheduling problems in dynamic permutation flow shop environments |
| url | http://dx.doi.org/10.1155/2020/2862186 |
| work_keys_str_mv | AT pablovalledor modellingandsolvingreschedulingproblemsindynamicpermutationflowshopenvironments AT albertogomez modellingandsolvingreschedulingproblemsindynamicpermutationflowshopenvironments AT paolopriore modellingandsolvingreschedulingproblemsindynamicpermutationflowshopenvironments AT javierpuente modellingandsolvingreschedulingproblemsindynamicpermutationflowshopenvironments |