Two-stage Fuzzy Flexible Flow Shop Scheduling Model And Algorithm

Flexible flow shop scheduling is an extension of parallel machine scheduling and flow shop scheduling. It has important practical applications in continuous process industries such as chemical industry and steel making industry. There are many uncertain factors in scheduling problems. Uncertainty programming was proposed by many researchers such as Baoding Liu in 2002. Jin Peng and Baoding Liu et.al studied parallel machine scheduling problems and flow shop scheduling problems with fuzzy processing times. However, the two-stage fuzzy flexible flow shop scheduling was not considered. This paper considers two-stage flexible flow shop scheduling with fuzzy processing times. We propose two novel scheduling methods and give a definition of fuzzy precedence constraints. Three types of fuzzy scheduling model: expected value model, chance-constrained programming and dependent-chanced programming are formulated. Meanwhile, the goal programming models are also considered in this paper.Scheduling problems are a class of combinatorial optimization problems which belongs to NP problems. Many intelligent computation methods such as genetic algorithm, simulated annealing algorithm are introduced into solving scheduling problems. The hybrid intelligent algorithm integrated by simulation and genetic algorithm has been applied in solving the flow shop scheduling with fuzzy processing times by Peng and Liu. In this paper, the hybrid intelligent algorithm is improved. A novel hybrid simulated annealing algorithm which incorporate fuzzy simulation, the initialization process and mutation process of genetic algorithm into simulated annealing is developed. Finally, some numerical examples are given to illustrate the effectiveness of the algorithm.