A hybrid method for solving stochastic job shop scheduling problems

This paper presents a nonlinear mathematical programming model for a stochastic job shop scheduling problem. Due to the complexity of the proposed model, traditional algorithms have low capability in producing a feasible solution. Therefore, a hybrid method is proposed to obtain a near-optimal solution within a reasonable amount of time. This method uses a neural network approach to generate initial feasible solutions and then a simulated annealing algorithm to improve the quality and performance of the initial solutions in order to produce the optimal/near-optimal solution. A number of test problems are randomly generated to verify and validate the proposed hybrid method. The computational results obtained by this method are compared with lower bound solutions reported by the Lingo 6 optimization software. The compared results of these two methods show that the proposed hybrid method is more effective when the problem size increases.