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 shows an effective CDS algorithm for the n-job m-machine flow shop scheduling problem with ordered precedence constraints and fuzzy logic, with the goal of reducing the fuzzy makespan to the lowest possible value. 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. There were a lot of numerical tests done to make sure the proposed model worked. The results showed that fuzzy makespan was a lot shorter than with and without ordered constraints scheduling techniques. 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 in terms of solution quality.