Fuzzy Hybrid Flow Shop Scheduling Problem: An Application

This research considers the fuzzy hybrid flow shop (FHFS) problem inspired by a real application in an apparel manufacturing process. A parallel greedy algorithm (PGA) is proposed for solving the FHFS scheduling problem with lot sizes. The uncertain setup, and processing time (PT), and due date (DD) is modeled by the fuzzy sets. The setup and PT are defined by a triangular fuzzy number (TFN) and DD by doublet fuzzy number (DFN). The objectives are minimizing the average tardiness, and the number of tardy jobs, the total setup time, and idle time of machines, and the total flow time. The proposed parallel greedy algorithm is compared with the genetic algorithm in the literature. The developed PGA is tested on real-world data collected at an apparel manufacturing process. Computational results are showed that the proposed parallel greedy algorithm is a more effective meta-heuristic method for FHFS problems with setup times and lot sizes.