A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context

Applied Sciences

FabioDe Felice¹  Antonella Petrillo²  Genett Jiménez-Delgado³  Luz Borrero-López⁴  Miguel Ortíz-Barrios⁴  Natalia Jaramillo-Rueda⁴ 

[1] Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy;Department of Engineering, University of Napoli “Parthenope”, 80143 Naples, Italy;Department of Industrial Engineering, Institución Universitaria ITSA, Barranquilla 080003, Colombia;Department of Industrial Management, Agroindustry and Operations, Universidad de la Costa CUC, Barranquilla 080003, Colombia;

关键词: FJSP;    MCDM;    fuzzy;    AHP;    TOPSIS;    smart manufacturing;   

DOI  :  10.3390/app11115107

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown