An Effective CDS Heuristic for the Fuzzy Flow Shop Scheduling Problem With Ordered Precedence Constraints Using Pentagonal Fuzzy Processing Time

In today’s modern manufacturing environment, effective production scheduling has a major impact on constructing schedules that meet customer demands efficiently, with uncertainties in production processes, such as variable job processing times and dynamic demand. This study presents an effective CDS heuristic for the n-job m-machine flow shop scheduling problem with ordered precedence constraints and fuzzy logic, aimed at determining a sequence that minimizes the fuzzy makespan. Job processing times are represented using pentagonal fuzzy numbers, and a new robust ranking method is employed for the defuzzification process. A key contribution of this research is the integration of these two methods to address the scheduling problem. Extensive numerical tests were conducted to validate the effectiveness of the proposed model. The results showed that the fuzzy makespan achieved its minimum value when ordered precedence constraints were applied. Additionally, the paper assesses the performance of these methods in terms of solution quality, utilizing test problems ranging from 10 to 40 jobs across various machines. The findings indicate that the proposed methods consistently yield better results.