Extension of the TOPSIS method based on prospect theory and trapezoidal intuitionistic fuzzy numbers for group decision making

Considering the decision maker＇s risk psychological factors and information ambiguity under uncertainty, a novel TOPSIS based on prospect theory （PT） and trapezoidal intuitionistic fuzzy numbers （YrIFNs） for group decision making is investigated, in which the criteria values and the criteria weights take the form of TrIFNs, and weights of decision makers are unknown. Firstly, distance measures for TrIFNs are used to induce value function under trapezoidal intuitionistic fuzzy environment. Secondly, the concepts of distance measures and trapezoidal intuitionistie fuzzy weighted averaging operator are employed to induce the weights of decision makers and thus the decision makers＇ options can be aggregated. Then the PT-based separation measures and relative closeness coefficient are defined and an algorithm for ranking alternatives under trapezoidal intuitionistic fuzzy environment is proposed. Finally, a numerical example further illustrates the practicality and effectiveness of the proposed TOPSIS method.     

The Technology Selection and Critical Successful Factors in the Information Technology （IT） Growth of Manufacturing Corporations in China

With the rapid development of the information technology （IT） and the more competitive environment of the corporations, IT is not only important for the enterprises tactically but also strategically, We extend the concept of the critical success factors （CSF） and Nolan＇s stages theory to identify the IT issues and measure their CSF of the manufacturers in China. We put forward the IT selection matrix based on Nolan＇s stages theory to analyze the IT application in manufacturers in Beijing based on our survey. We analyze the development of IT, IT selection strategy and the CSF influencing the IT growth of manufacturers in China.     

Ranking grey numbers based on dominance grey degrees

With respect to the decision making problems where a lot of fuzzy and grey information always exists in the real-life decision making information system methods as fuzzy mathematics, it is difficult for such uncertainty probability, and interval numbers to deal with. To this end, based on the thought and method of grey numbers, grey degrees and interval numbers, the concept of dominance grey degree is defined. And then a method of ranking interval grey numbers based on the dominance grey degree is proposed. After discussing the relevant properties, the paper finally uses an example to demonstrate the effectiveness and applicability of the model. The result shows that the proposed model can more accurately describe uncertainty decision making problems, and realize the total ordering process for multiple-attribute decision-making problems.     

Method for uncertain multi-attribute decisionmaking with preference information in the form of interval numbers complementary judgment matrix

The uncertain multi-attribute decision-making problems because of the information about attribute weights being known partly, and the decision maker's preference information on alternatives taking the form of interval numbers complementary to the judgment matrix, are investigated. First, the decision-making information, based on the subjective uncertain complementary preference matrix on alternatives is made uniform by using a translation function, and then an objective programming model is established. The attribute weights are obtained by solving the model, thus the overall values of the alternatives are gained by using the additive weighting method. Second, the alternatives are ranked, by using the continuous ordered weighted averaging (C-OWA) operator. A new approach to the uncertain multi-attribute decision-making problems, with uncertain preference information on alternatives is proposed. It is characterized by simple operations and can be easily implemented on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.     

An approach to group decision making based on interval fuzzy preference relations

In this paper, we investigate group decision making problems where the decision information given by decision makers takes the form of interval fuzzy preference relations. We first give an index to measure the similarity degree of two interval fuzzy preference relations, and utilize the similarity index to check the consistency degree of group opinion. Furthermore, we use the error-propagation principle to determine the priority vector of the aggregated matrix, and then develop an approach to group decision making based on interval fuzzy preference relations. Finally, we give an example to illustrate the developed approach.     

Application of MIGP in Phosphate Rock Districts Development Planning and System Economic Evaluation

This paper, based on the study results of a subproject under the Seventh Fiveyear-plan National Major Scientific and Technological Research Project, will concentrate on the application of the mixed integer goal programming in the studies on the optimization and comprehensive evaluation of investment decision and production planning in Yichang phosphate rock district in Hubei Province and in Dianchi district in Yunnan Province. The optimization models for mineral district development planning system are formulated through system analysis. A microcomputer software based on the combination of mixed integer programming and goal programming is developed to continuously implement the calculation for the mathematical model with arbitrary objective priority levels and for a group of such models. And a system economic evaluation model is developed to measure the profitability of optimal planning system scenarios. Finally, a large amount of output information are provided for the reference of decision makers.     

A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank＇s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today＇s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system （embedded in a multiple intelligent agent system） is proposed to predict the financial health of a company. In our model, each individual agent （classifier） makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge （represented by features） about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.     

Research on consistency measurement and weight estimation approach of hybrid uncertain comparison matrix

The consistency measurement and weight estimation approach of the hybrid uncertain comparison matrix in the analytic hierarchy process （AHP） are studied. First, the decision-making satisfaction membership function is defined based on the decision making＇s allowable error. Then, the weight model based on the maximal satisfactory consistency idea is suggested, and the consistency index is put forward. Moreover, the weight distributing value model is developed to solve the decision making misleading problem since the multioptimization solutions in the former model. Finally, the weights are ranked based on the possibility degree approach to obtain the ultimate order.     

PLANNED LEAD TIME OPTIMIZATION IN MATERIAL REQUIREMENT PLANNING ENVIRONMENT FOR MULTILEVEL PRODUCTION SYSTEMS

This paper deals with the problem of planned lead time calculation in a Material Requirement Planning （MRP） environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging costs. The proposed approach is based on discrete time inventory control where the decision variables are integer. Two types of systems are considered： multi-level serial-production and assembly systems. For the serial production systems （one type of component at each level）, a mathematical model is suggested. Then, it is proven that this model is equivalent to the well known discrete Newsboy Model. This directly provides the optimal values for the planned lead times. For multilevel assembly systems, a dedicated model is proposed and some properties of the decision variables and objective function are proven. These properties are used to calculate lower and upper limits on the decision variables and lower and upper bounds on the objective function. The obtained limits and bounds open the possibility to develop an efficient optimization algorithm using, for example, a Branch and Bound approach. The paper presents the proposed models in detail with corresponding proofs and se＇~eral numerical examples. Some advantages of the suggested models and perspectives of this research are discussed.     

Warfare command stratagem analysis for winning based on Lanchester attrition models

Lanchester equations and their extensions are widely used to calculate attrition in warfare models. The current paper addresses the warfare command decision-making problem for winning when the total combats capability of the attacking side is not superior to that of the defending side.For this problem,the corresponding warfare command stratagems,which can transform the battlefield situation, are proposed and analyzed quantitatively by considering the influence of the warfare information factor.The application examples in military conflicts show the feasibility and effectiveness of the proposed model and the warfare command stratagems for winning.The research results may provide a theoretical reference for warfare command decision making.     

复杂环境影响下三维雷达作用范围表现

地形、海洋、气象等环境影响下雷达电磁信息的三维表现是未来数字化战场不可回避的问题.在建立大气吸收系数、大气折射率以及地表等环境因素的数字模型基础上,利用高级传播模型、大气吸收衰减模型计算平面内雷达波的传播衰减,根据雷达和目标的相关参数获取平面内雷达作用范围,提出虚拟3D策略来构造三维的雷达作用范围.将典型雷达嵌入到数字战场环境的实验表明,该方法能及时准确形象地展示复杂环境影响下雷达的三维作用范围,允许交互调整雷达和目标参数,从而为用户决策和规划提供支持.     

面向服务的战场环境保障信息集成框架

为提高战场透明度,需研究和发展以卫星信息为支持的战场环境保障技术。深入分析了卫星支持的战场环境保障的特点及信息处理流程;构建了面向服务的战场环境保障信息集成框架,提出基础平台层、数据组织层、核心服务层和作战应用层的4层框架结构;研究了支持该框架的关键技术体系及接口,实现了原型系统。框架打破目前纵向的各卫星系统自成体系、数据共享困难的局面,使得系统具有更好的动态性和可扩展性,便于用户根据不同的作战需求建立不同的服务模式。     

不完备信息条件下基于证据理论的CGF行为决策方法

针对计算机生成兵力行为决策中遇到的战争迷雾问题, 特别是决策属性中出现的不完备信息情况, 提出基于证据理论的行为决策方法. 首先基于战场决策信息特点分析, 提出精确、模糊、不确定信息的决策属性可信度归一化表示, 然后提出表示备选方案不确定性的执行可信度, 最后根据DS合成法则对多属性可信度信息进行合成. 实验分析说明了该方法的可行性, 以及对于不完备信息处理的有效性.     

DSmT理论在综合敌我识别中的应用

战场环境的复杂性要求使用多种传感器对战场目标进行综合敌我识别,而综合敌我识别有待解决的基础性难题之一是如何对异类传感器输出的不确定性信息进行有效处理。针对一包含雷达、红外、电子支援措施和敌我识别器等传感器的综合敌我识别系统,对异类传感器敌我识别过程进行分析,根据其特点提出采用DSmT理论进行决策级融合,同时针对每类传感器构造不同的基本置信指派方法,并进行了算法仿真。实验结果证实了该方法的有效性。     

基于认知图和直觉模糊推理的态势评估方法

针对战场环境中信息的不确定性和交互关联性问题,将直觉模糊推理理论与认知图推理相结合,提出一种基于认知图和直觉模糊理论的态势评估方法。首先,分析当前认知图推理的特点与不足,建立基于直觉模糊集理论和有序加权平均算子的直觉模糊认知图模型;其次,提出直觉模糊认知图的态势评估推理过程;最后,用实例给出评估结果,验证方法的有效性和正确性。该方法能更准确地描述战场客观环境,为决策者提供更多的决策信息和决策偏好自由度。     

基于马尔可夫的多功能雷达认知干扰决策建模研究

多功能雷达是现代电磁战场上不可或缺的重要装备, 针对多功能雷达的干扰一直是一个难题。本文在研究多功能雷达信号特点和雷达对抗过程的基础上, 提出了雷达状态联合表征的方法, 将多功能雷达的干扰决策问题建模为一个带收益的马尔可夫决策过程, 设计了认知干扰决策系统, 并通过基于Q-Learning的认知干扰决策算法求解该模型下的最佳干扰策略。通过仿真实验, 证明了基于Q-Learning的认知干扰决策算法能够在缺乏先验经验的情况下学习到最佳干扰策略, 具备“认知”的特性, 并且在不稳定的环境中也具有较强的适应性, 有效支撑了本文所提的干扰决策模型。     

Holonic-C2组织资源调度问题研究

为了解决复杂多变战场环境下指挥控制(command and control, C2)组织资源调度敏捷性不足的问题,将Holonic控制理论引入C2组织的设计领域,研究Holonic-C2组织的资源调度问题。首先,对基于Holonic理论的C2组织Holon单元进行定义,给出Holonic-C2组织的基本结构;其次,对Holonic-C2组织的决策权限进行分析,给出决策模式的划分及确定方法;最后,研究在不同决策模式下Holon单元资源调度及针对不确定性事件的动态调度方法,建立集中与协作相结合的资源调度和调整机制。     

战场抢修资源重组决策方法

针对战时抢修资源不满足抢修任务需求时如何进行抢修资源重组决策的问题,首先,通过约束满足问题的方法建立了以总抢修效益最大为目标的抢修资源重组决策数学模型。然后,设计了一种结合基于总消耗率贪婪算法和粒子群算法的混合粒子群算法及相应的操作方案。最后,进行了仿真实验,结果表明了该方法解决抢修资源重组决策问题的有效性。     

基于相关因素的决策优化模型建模方法

结合机载导弹阵地战场抢修决策平台项目开发,提出了基于相关因素的决策优化模型建模方法。该方法建立在建模的可分离原则上,首先对决策问题自顶而下地进行相关环境和相关因素的分离,然后自底而上地建立综合集成决策优化模型。同时,该方法采用形式化表示体系来描述建模过程。应用该方法建立了面向机载导弹阵地战场抢修决策的优化模型。应用表明,该方法协调了建模过程中多角色的交互问题,为模型的可用、应用和复用打下了良好的基础。     

多信息源条件下战损部件恢复能力评估方法

在进行战时装备战损部件恢复能力评估时,针对收集得到的部件抢修样本信息与实际战场情况不相符的问题,以战场环境和抢修分队维修保障条件为依据,分别建立了样本信息筛选模型和样本抢修时间向实际抢修时间折合的内积模型。采用随机加权法将样本信息转换为参数分布,提出支持度与最大熵相结合的信息源权重确定方法,将相似部件和部件历史信息进行融合,得出部件在对应战场条件下的恢复能力评估结果。算例分析表明,该评估方法可对多种复杂战场条件下的部件恢复能力进行评估,提出的多源融合方法较传统估计方法评估结果更加准确,具有很强的实用性。