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Many varieties of small batch production mode, intensive manufacturing enterprises to develop a reasonable production plan for rapid response to market demands, reduce the manufacturing process waste, reduce inventory backlog , etc. all have a positive meaning. However, the actual business environment, there is a lot of uncertainty , inaccurate information , to the production plan development has caused great difficulties . This paper manufacturing enterprises in the production planning stage decision-making process in the face of all aspects of uncertainty analysis , and propose practical solutions intensive production plan . In the fuzzy theory to describe uncertain information , based on the established based on credibility theory , in pursuit of profit maximization as the goal of fuzzy chance constrained programming model , and translate them into a clear equivalent form . Aggregate Production Planning for single-target model for the expansion , considering the maximum profit , the total overtime hours , for a total amount of product shortages , staff turnover problems , the establishment of multi-objective aggregate production planning model . The fuzzy simulation, neural network and genetic algorithm combining hybrid intelligent algorithm, and its solution , the introduction of human-computer interaction mechanism for continuous improvement of program . Different for each target is difficult to assess the magnitude of the problem , will introduce the concept of membership degree related to multi-program decision-making process , taking into account the objective comparison of subjective preferences and decision-makers . The model and optimization algorithm can solve the traditional methods of intensive production plan seriously out of decision-makers expect problems, make plans to meet the actual needs of production programs more . An example of application, we prove that the set of solutions of the effectiveness and feasibility . Based on the above research , design and development of uncertain information environment intensive production planning management system, to achieve practical application of the theoretical results .
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