An elite genetic algorithm for flexible job shop scheduling problem with extracted grey processing time

This paper investigates a flexible job shop scheduling problem with uncertain processing time. The uncertainty of the processing time is characterized by a generalized grey number. We extract generalized grey numbers from limited information in real-world production, and then extend their operations for scheduling. With generalized grey numbers, the problem is formulated by a mathematical model to minimize the makespan. We develop an elite genetic algorithm for finding excellent solutions. The algorithm employs an elite strategy and neighborhood search method to search for promising individuals on the premise of ensuring population diversity. To assess the performance of the suggested methods, we construct 10 benchmark instances using generalized grey numbers. The results of the experiments demonstrate the effectiveness and competitiveness of the proposed algorithm and characterization.