Designing a reliable aggregate production planning problem during the disaster period
The purpose of this research is to introduce a Bi-Objective (BO) model for dealing with Aggregate Production Planning (APP) for a multi-product and multi-period Supply Chain Network (SCN) that incorporates multiple suppliers, factories, and demand points. One of the goals of the model is to minimize...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2023-01-01
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| Series: | Sustainable Operations and Computers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666412723000144 |
| _version_ | 1797378631430832128 |
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| author | Ernesto DR. Santibanez Gonzalez Sina Abbasi Mahsa Azhdarifard |
| author_facet | Ernesto DR. Santibanez Gonzalez Sina Abbasi Mahsa Azhdarifard |
| author_sort | Ernesto DR. Santibanez Gonzalez |
| collection | DOAJ |
| description | The purpose of this research is to introduce a Bi-Objective (BO) model for dealing with Aggregate Production Planning (APP) for a multi-product and multi-period Supply Chain Network (SCN) that incorporates multiple suppliers, factories, and demand points. One of the goals of the model is to minimize the total cost of this network during the disaster period. The other goal is to account for probabilistic lead times to maximize the minimum level of producers' reliability during the COVID-19 pandemic. They are done to ameliorate the system's performance and improve the reliability of production plans. Finally, considering that the mentioned problem is NP-hard, a Multi-Objective Imperialist Competitive Algorithm (MOICA) based on Pareto is used to solve the proposed model, and a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is also utilized to measure the performance of the mentioned algorithm. The generated experimental problems' results demonstrate the proposed algorithm's power in finding Pareto solutions. According to innovation, this is the first paper on these topics considering the conditions of the COVID-19 disaster. |
| first_indexed | 2024-03-08T20:10:33Z |
| format | Article |
| id | doaj.art-cf0aff8a082b4f2487f62b97a561b3b1 |
| institution | Directory Open Access Journal |
| issn | 2666-4127 |
| language | English |
| last_indexed | 2024-03-08T20:10:33Z |
| publishDate | 2023-01-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Sustainable Operations and Computers |
| spelling | doaj.art-cf0aff8a082b4f2487f62b97a561b3b12023-12-23T05:22:48ZengKeAi Communications Co. Ltd.Sustainable Operations and Computers2666-41272023-01-014158171Designing a reliable aggregate production planning problem during the disaster periodErnesto DR. Santibanez Gonzalez0Sina Abbasi1Mahsa Azhdarifard2Department of Industrial Engineering, Faculty of Engineering, University of Talca, Executive Director Circular Economy and Sustainable 4.0 Initiative (CES4.0), Los Niches Km. 1, Curico, ChileDepartment of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran; Corresponding author.Department of Industrial Engineering, Faculty of Engineering, Khatam University, Tehran, IranThe purpose of this research is to introduce a Bi-Objective (BO) model for dealing with Aggregate Production Planning (APP) for a multi-product and multi-period Supply Chain Network (SCN) that incorporates multiple suppliers, factories, and demand points. One of the goals of the model is to minimize the total cost of this network during the disaster period. The other goal is to account for probabilistic lead times to maximize the minimum level of producers' reliability during the COVID-19 pandemic. They are done to ameliorate the system's performance and improve the reliability of production plans. Finally, considering that the mentioned problem is NP-hard, a Multi-Objective Imperialist Competitive Algorithm (MOICA) based on Pareto is used to solve the proposed model, and a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is also utilized to measure the performance of the mentioned algorithm. The generated experimental problems' results demonstrate the proposed algorithm's power in finding Pareto solutions. According to innovation, this is the first paper on these topics considering the conditions of the COVID-19 disaster.http://www.sciencedirect.com/science/article/pii/S2666412723000144COVID-19Integrated production planningSupply chain managementMulti-objective optimizationImperialist competitive algorithmNSGA-II algorithm |
| spellingShingle | Ernesto DR. Santibanez Gonzalez Sina Abbasi Mahsa Azhdarifard Designing a reliable aggregate production planning problem during the disaster period Sustainable Operations and Computers COVID-19 Integrated production planning Supply chain management Multi-objective optimization Imperialist competitive algorithm NSGA-II algorithm |
| title | Designing a reliable aggregate production planning problem during the disaster period |
| title_full | Designing a reliable aggregate production planning problem during the disaster period |
| title_fullStr | Designing a reliable aggregate production planning problem during the disaster period |
| title_full_unstemmed | Designing a reliable aggregate production planning problem during the disaster period |
| title_short | Designing a reliable aggregate production planning problem during the disaster period |
| title_sort | designing a reliable aggregate production planning problem during the disaster period |
| topic | COVID-19 Integrated production planning Supply chain management Multi-objective optimization Imperialist competitive algorithm NSGA-II algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2666412723000144 |
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