A Hybrid Approach for Fuzzy Just-In-Time Flow Shop Scheduling with Limited Buffers and Deteriorating Jobs

This paper investigates the problem of just-in-time permutation flow shop scheduling with limited buffers and linear job deterioration in an uncertain environment. The fuzzy set theory is applied to describe this situation. A novel mixed-integer nonlinear program is presented to minimize the weighted sum of fuzzy earliness and tardiness penalties. Due to the computational complexities, the proposed mathematical model is NP-hard and therefore a hybrid meta-heuristic approach based on imperialist competitive algorithm and genetic algorithm (ICA-GA) is designed to tackle the considered flow shop scheduling problem. A set of random test problems with different structures are developed to evaluate the performance of this approach. The results illustrate the effectiveness of the presented model and hybrid algorithm for different problem sizes.