Tavakkoli-Moghaddam - Dep. of Industrial Eng., Faculty of Eng., University of Tehran, Iran
Jolai - Dep. of Industrial Eng., Faculty of Eng., University of Tehran, Iran
Haji - Dep. of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Vaziri - Dep. of Industrial Eng., Faculty of Eng., University of Tehran, Iran

This paper presents a non-linear mathematical programming model for a stochastic job shop scheduling problem. Due to the complicity of the proposed model, traditional algorithms have low capability in producing a feasible solution. So in this paper, a hybrid method is proposed to solve the above problem in 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. We assume that the machine flexibility in processing the operations to decrease the complexity of the proposed model. 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. The compared results of these two methods show that the proposed hybrid method is more effective when the problem size increases requiring large parameters.

Stochastic Job Shop Scheduling, Non-linear mathematical model, Neural Network