An Efficient Algorithm for Stochastic Job Shop Scheduling Problems
Publish place: 3rd International Industrial Engineering Conference
Publish Year: 1383
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
IIEC03_029
تاریخ نمایه سازی: 10 مهر 1385
Abstract:
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.
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Authors
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
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