A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context
Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-crite...
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| Format: | Article |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/11/5107 |
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| author | Miguel Ortíz-Barrios Antonella Petrillo Fabio De Felice Natalia Jaramillo-Rueda Genett Jiménez-Delgado Luz Borrero-López |
| author_facet | Miguel Ortíz-Barrios Antonella Petrillo Fabio De Felice Natalia Jaramillo-Rueda Genett Jiménez-Delgado Luz Borrero-López |
| author_sort | Miguel Ortíz-Barrios |
| collection | DOAJ |
| description | Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery. |
| first_indexed | 2024-03-10T10:51:02Z |
| format | Article |
| id | doaj.art-b7a54461c8994d15bfcc756a33a7ea04 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:51:02Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b7a54461c8994d15bfcc756a33a7ea042023-11-21T22:14:25ZengMDPI AGApplied Sciences2076-34172021-05-011111510710.3390/app11115107A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 ContextMiguel Ortíz-Barrios0Antonella Petrillo1Fabio De Felice2Natalia Jaramillo-Rueda3Genett Jiménez-Delgado4Luz Borrero-López5Department of Industrial Management, Agroindustry and Operations, Universidad de la Costa CUC, Barranquilla 080003, ColombiaDepartment of Engineering, University of Napoli “Parthenope”, 80143 Naples, ItalyDepartment of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, 03043 Cassino, ItalyDepartment of Industrial Management, Agroindustry and Operations, Universidad de la Costa CUC, Barranquilla 080003, ColombiaDepartment of Industrial Engineering, Institución Universitaria ITSA, Barranquilla 080003, ColombiaDepartment of Industrial Management, Agroindustry and Operations, Universidad de la Costa CUC, Barranquilla 080003, ColombiaScheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery.https://www.mdpi.com/2076-3417/11/11/5107FJSPMCDMfuzzyAHPTOPSISsmart manufacturing |
| spellingShingle | Miguel Ortíz-Barrios Antonella Petrillo Fabio De Felice Natalia Jaramillo-Rueda Genett Jiménez-Delgado Luz Borrero-López A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context Applied Sciences FJSP MCDM fuzzy AHP TOPSIS smart manufacturing |
| title | A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context |
| title_full | A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context |
| title_fullStr | A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context |
| title_full_unstemmed | A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context |
| title_short | A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context |
| title_sort | dispatching fuzzy ahp topsis model for scheduling flexible job shop systems in industry 4 0 context |
| topic | FJSP MCDM fuzzy AHP TOPSIS smart manufacturing |
| url | https://www.mdpi.com/2076-3417/11/11/5107 |
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