Solving Type-2 Fuzzy Distributed Hybrid Flowshop Scheduling Using an Improved Brain Storm Optimization Algorithm

The distributed hybrid flowshop scheduling (DHFS) problem is a common scheduling problem that has been researched in both academic and industrial fields during recent years. The uncertainty levels in realistic applications are generally too high to be represented by a deterministic value or a triangular fuzzy number (TFN) value. Considering the DHFS problem with type-2 fuzzy processing time and setup time constraints, an improved version of brain storm optimization was developed, where the objective is to minimize the maximum type-2 fuzzy completion time among all factories. The main contributions of this study are as follows: (1) each solution is represented by a two vectors, i.e., a scheduling vector and a factory assignment vector; (2) two realistic constraints, i.e., the type-2 fuzzy processing time in an uncertain environment and the setup time, make the problem more realistic; (3) a novel constructive heuristic based on the Nawaz-Enscore-Ham (NEH) method, called distributed NEH, is proposed; (4) several local search heuristics considering the problem features and the objective are developed to enhance the local search abilities; and (5) a simulated-annealing-based acceptance criterion is embedded to enhance the exploration abilities. The experimental results demonstrate that the proposed algorithm is more efficient and effective for solving the considered type-2 fuzzy DHFS problems in comparison with other recently published efficient algorithms.

Automated summary

An improved optimization algorithm has been developed to solve the distributed hybrid flowshop scheduling problem efficiently, aiming to minimize the completion time in uncertain environments. The study introduces a vector structure for representing solutions, a constructive heuristic based on the NEH method, and various local search heuristics considering problem features and objectives.