Simulation Optimization for the Multihoist Scheduling Problem
Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processe...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/6639769 |
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| author | Ricardo Pérez-Rodríguez |
| author_facet | Ricardo Pérez-Rodríguez |
| author_sort | Ricardo Pérez-Rodríguez |
| collection | DOAJ |
| description | Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison. |
| format | Article |
| id | doaj-art-51d092529be944b09646e3319bef2e08 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-51d092529be944b09646e3319bef2e082025-02-03T01:24:48ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322021-01-01202110.1155/2021/66397696639769Simulation Optimization for the Multihoist Scheduling ProblemRicardo Pérez-Rodríguez0CONACYT-UAQ, Autonomous University of Queretaro, Faculty of Engineering, Centro Universitario, Cerro de las Campanas s/n, 76010, Santiago de Queretaro, MexicoAlthough the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison.http://dx.doi.org/10.1155/2021/6639769 |
| spellingShingle | Ricardo Pérez-Rodríguez Simulation Optimization for the Multihoist Scheduling Problem Applied Computational Intelligence and Soft Computing |
| title | Simulation Optimization for the Multihoist Scheduling Problem |
| title_full | Simulation Optimization for the Multihoist Scheduling Problem |
| title_fullStr | Simulation Optimization for the Multihoist Scheduling Problem |
| title_full_unstemmed | Simulation Optimization for the Multihoist Scheduling Problem |
| title_short | Simulation Optimization for the Multihoist Scheduling Problem |
| title_sort | simulation optimization for the multihoist scheduling problem |
| url | http://dx.doi.org/10.1155/2021/6639769 |
| work_keys_str_mv | AT ricardoperezrodriguez simulationoptimizationforthemultihoistschedulingproblem |