制造系统中不确定因素的行为决策分析与机理研究

项目名称： 制造系统中不确定因素的行为决策分析与机理研究

项目编号： No.51305400

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 机械、仪表工业

项目作者： 裴植

作者单位： 浙江工业大学

项目金额： 25万元

中文摘要： 制造系统的绩效评估和产能计算环节中不可避免地受到决策者行为的影响，同时，制造系统内部也存在着诸如不完整信息和不精确信息的传递，此类型的不确定性因素很大程度上干扰了系统的绩效评估结果，使得基于随机理论的产能计算模型不能很好地贴合决策者心理预期。本课题基于行为决策方法，通过建立决策者行为模式与制造系统产能之间的一致性测量优化模型，以减少生产资料投放冗余度，增加系统产出与顾客行为决策结果的相似度。在计算制造系统内部的产能时，将以随机系统绩效评估方法中的聚合算法为原型，通过在随机加工中心模型中引入模糊信息，并在系统的输入条件中糅合进语言信息，重新构建基于可信性理论的聚合评估算法。并以此为基础，探讨将聚合单元拓展为具有更复杂结构的制造系统的可能。实验研究作为行为决策模型的重要组成，将衡量决策结果同行为主体心理之间的相似度，并通过与比较实验测定混合不确定性信息聚合算法的鲁棒性和算法性能。

中文关键词： 制造系统；多属性决策；不确定信息；直觉模糊集；行为运筹学

英文摘要： The processes of performance assessment and production capability in the manufacturing systems are influenced by the behaviors of decision makers inevitably. Meanwhile, the transfer of incomplete and inaccurate information inside the production lines also renders the stochasitc production computation model improper, as to be consistent with the expectations of the decision maker. In this research proposal, a noval optimization model is built to measure the actual correlations between the decision makers' behavior and the production output of the manufacturing system, based on the rational decision making theory, which is introduced by the applicant recently. The fundamental goal of this theory is to make the production outputs resemble the decision makers' attitudes, via the reduction of redundant information and materials. When the production capability of the production system is calculated, the aggregation algorithm from the probability theory framework is employed as a prototype. The workstations in this research are described by additional fuzzy information, and the input conditions of the systems includesome linguistic information as well. A new aggregation algorithem based on credibility theory is constructed. Possible extensions are made to model more complex manufacturing systems in terms of structure.

英文关键词： Manufacturing system；Multi-attribute decision making；Uncertain information；Intuitionistic fuzzy set；Behavioral Operational Management