一种基于权重修正的多目标决策方法

研究了目标优先级无法凭经验确定条件下多目标决策问题, 提出了一种合理选择权重的方法。该方法以线性加权法为基础, 通过引入能量函数概念来模拟决策过程, 导出了非劣解演化方程和权重学习规则, 并结合算例进行了实例分析, 结果表明了该方法具有一定的实用价值。     

河南省大豆品种灰色布局决策研究

运用作物灰色育种学原理,对1996～1997年河南省大豆品种区域试验资料进行了品种中心灰靶布局分析。在此基础上,提出了河南省不同生态类型区大豆品种灰色布局决策,并对品种区域试验中应当引起重视的问题进行了讨论。表2,参5。     

突击作战干扰时机决策

干扰时机的确定是进行干扰决策的前提和基础。通过对问题分析,制定了两套干扰时机决策方案。为实现攻击机的飞行控制最优规划,综合考虑攻击机飞行性能、威胁约束以及边界约束,建立了最优航迹控制模型。引入主要应用于航天控制的勒让德伪谱法(Legendre pseudospectral method, LPM),将最优控制问题转化为非线性规划(nonlinear programming, NLP)问题,在此基础上设计了干扰时机解算流程。通过对基于c代码的可行序列二次规划(c code feasible sequential quadratic programming, CFSQP)算法的改进,有效减少了优化变量的数量,使其能对NLP问题快速寻优。仿真结果表明,所提出的干扰时机决策方案能有效地获得最优干扰开始和结束时刻。     

模糊状态概率下的组合投资决策

针对决策者在进行项目选择投资时各种自然状态发生概率的不确定性,提出并讨论了模糊状态概率下的组合投资决策问题。通过对状态集合上模糊概率评价的一致性进行分析,运用与模糊概率评价相对应的一个有效概率分布,给出了模糊状态概率下组合投资策略的期望收益率和风险的表示方法,在此基础上建立了组合投资的多目标决策模型,并给出了其求解过程。最后,给出了一个数值例子,结果表明:方法合理、有效,易于实现。     

Decision making with fuzzy probability assessments and fuzzy payoff

1.INTRODUCTION Aproblemoftenfacedbymanydecisionmakersisthe selectionofsomealternativesinrandomuncertainsit uationsinwhichprobabilitiesofeventsareprovided byhumanbeings.Humanbeings,ratherthanbeing abletoprovideexactspecificprobabilities,areusually bestatassessingprobabilitiesinimpreciselinguistic termssuchashigh,low,about70%.Anaturaland usefulwayofrepresentingimpreciselinguistecproba bilitiesiswithafuzzysubset[1～3],afuzzyprobabili ty.Yager[4]investigatedtheproblemofdecision makingwithf…     

考虑双重预期的云模型决策

针对指标值为云模型且考虑目标预期和增长预期的多属性决策问题,提出了一种基于前景理论的决策方法。提出了增长预期和目标预期的刻画方法,针对以云模型形式给出的属性评价值和双预期值建立了相似性度量方法,根据累积前景理论和云模型相似度定义了综合前景价值函数,最后根据方案之间的综合前景值总体差异最大化为目标建立权重寻优模型;案例说明了方法应用步骤和可行性。     

复杂群决策系统的适应性及其决策机理研究

随着社会上各类群决策问题的不断发展,例如上市公司董事会决策、政府宏观规划与战略决策以及网络招投标决策等,所有的这些群决策问题都呈现了一些新特点,传统的群决策理论已逐渐显得无能为力,需要发展新的群决策理论体系。最近兴起的复杂性科学理论为研究此类群决策问题提供了一种新的研究视角。鉴于上述研究背景,本文首先运用复杂适应系统理论分析复杂群决策系统的适应性特性,其次对复杂群决策系统的决策机理进行深入剖析,最后对复杂群决策系统的管理与控制问题进行探讨。     

基于三参数区间灰数的TOPSIS决策方法

针对决策问题的灰色系统特征,引入三参数区间灰数对评价指标进行度量。从类型一致化和无量纲化这两个方面着手对评价矩阵进行标准化。考虑到三参数区间灰数的上下界与重心点的影响,给出了基于可能度的三参数区间灰数排序方法以及三参数区间灰数距离测度方法。在此基础上,提出了基于三参数区间灰数的逼近理想解排序(technique for order preference by similarity to ideal solution,TOPSIS)决策方法,并用实例验证了其科学性与有效性。     

Overview of complex systems in sport

The complex systems approach offers an opportunity to replace the extant pre-dominant mechanistic view on sport-related phenomena. The emphasis on the environment-system relationship, the applications of complexity principles, and the use of nonlinear dynamics mathematical tools propose a deep change in sport science. Coordination dynamics, ecological dynamics, and network approaches have been successfully applied to the study of different sport-related behaviors, from movement patterns that emerge at different scales constrained by specific sport contexts to game dynamics. Sport benefit from the use of such approaches in the understanding of technical, tactical, or physical conditioning aspects which change their meaning and dilute their frontiers. The creation of new learning and training strategies for teams and individual athletes is a main practical consequence. Some challenges for the future are investigating the influence of key control parameters in the nonlinear behavior of athlete-environment systems and the possible relatedness of the dynamics and constraints acting at different spatio-temporal scales in team sports. Modelling sport-related phenomena can make useful contributions to a better understanding of complex systems and vice-versa.     

融合新型偏好度模型和改进累积前景理论的决策方法

针对决策问题的不确定性和复杂性以及属性权重和专家权重未知等特征,提出一种融合新型偏好度模型和改进累积前景理论的决策方法。首先,定义群决策问题的基本框架,将属性值和心理参考点改进为区间值;其次,根据区间心理参考点的二维几何表示定义新型偏好度模型,由此确定属性权重和专家权重;再次,聚合专家意见,考虑专家心理行为,由属性值和区间心理参考点的位置关系,得到收益值和损失值的计算公式,进而确定各方案的总前景值及排序关系;最后,通过案例分析和方法对比验证该方法的有效性、可行性和实用性。     

拦截鱼雷作战的深弹布设优化决策模型

在悬浮式深弹拦截鱼雷作战中,深弹布设是影响作战效能的关键因素。在分析深弹对鱼雷拦截概率及其影响因素的基础上,提出了深弹阵对预定拦截线有效覆盖率的概念;考量深弹的布设数量及阵形因素,建立了拦截概率的计算模型和深弹布设的优化决策模型;采用随机模拟和二项分布的正态逼近方法估算拦截概率,通过对可选方案的比较得到深弹布设的最优策略,并分析了模型的有效性。结果表明,该方法可为装备的指控自动化系统研发、战训法研讨提供参考。     

On integration and adaptation in complex service systems

The services sector employs a large and growing proportion of workers in the industrialized nations, and it is increasingly dependent on information and communication technologies. While the interdependences, similarities and complementarities of manufacturing and services are significant, there are considerable differences between goods and services, including the shift in focus from mass production to mass customization （whereby a service is produced and delivered in response to a customer＇s stated or imputed needs）. In general, services can be considered to be knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Like manufacturing systems, an efficient service system must be an integrated system of systems, leading to greater connectivity and interdependence. Integration must occur over the physical, temporal, organizational and functional dimensions, and must include methods concerned with the component, the management, and the system. Moreover, an effective service system must also be an adaptable system, leading to greater value and responsiveness. Adaptation must occur over the dimensions of monitoring, feedback, cybernetics and learning, and must include methods concerned with space, time, and system. In sum, service systems are indeed complex, especially due to the uncertainties associated with the human-centered aspects of such systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation. The paper concludes with several insights, including a plea to shift the current misplaced focus on developing a science or discipline for services to further developing a systems engineering approach to services, an approach based on the integration and adaptation of a host of sciences or disciplines （e.g., physics, mathematics, statistics, psychology, sociology, etc.）. In fact, what is required is a services-related transdisciplinary - beyond a single disciplinary - ontology or taxon     

DECISION MAKING - THE ANALYTIC HIERARCHY ANDNETWORK PROCESSES （AHP／ANP）

This is the first part of an introduction to multicriteria decision making using the Analytic Hierarchy Process (AHP) and its generalization, the Analytic Network Process (ANP). The discussion involves individual and group decisions both with the independence of the criteria from the alternatives as in the AHP and also with dependence and feedback in the entire decision structure as in the ANP. This part explains the Analytic Hierarchy Process, with examples, and presents in some detail the mathematical foundations. An exposition of the Analytic Network Process and its applications will appear in later issues of this journal. Thomas L. Saaty holds the Chair of University Professor, Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, and obtained his Ph.D. in mathematics from Yale University. Before that he was a professor at the Wharton School of the University of Pennsylvania for ten years. Prior to that he spent seven years at the Arms Control and Disarmament Agency in the State Department in Washington, DC, that carried out the arms reduction negotiations with the Soviets in Geneva. His current research interests include decision-making, planning, conflict resolution and synthesis in the brain. As a result of his search for an effective means to deal with weapons tradeoffs at the Disarmament Agency and, more generally, with decision-making and resource allocation, Professor Saaty developed The Analytic Hierarchy Process (AHP) and its generalization to dependence and feedback, the Analytic Network Process (ANP). He is co-developer of the software Expert Choice and of the software Super Decisions for decisions with dependence and feedback. He has authored or co-authored twelve books on the AHP/ANP. Professor Saaty has also written a number of other books that embrace a variety of topics, including Modern Nonlinear Equations, Nonlinear Mathematics, Graph Theory, The Four Color Problem, Behavioral Mathematics, Queuing Theory, Optimization in Integers, Embracing the Future and The Brain: Unraveling the Mystery of How It Works. His most recent book is Creative Thinking, Problem Solving & Decision Making. The book is a rich collection of ideas, incorporating research by a growing body of researchers and practitioners, profiles of creative people, projects and products, theory, philosophy, physics and metaphysics...all explained with a liberal dose of humor. He has published more than 300 refereed articles in a wide variety of professional journals. He has been on the editorial boards of Mathematical Reviews, Operations Research, Naval Research Logistics Quarterly, Mathematical and Computer Modeling, Socio-Economic Planning Sciences, Applied Mathematics Letters, and several others. He also served as consultant to many corporations and governments.     

Energy flows in complex ecological systems: a review

Energy flow drives the complex systems to evolve. The allometric scaling as the universal energy flow pattern has been found in different scales of ecological systems. It reflects the general power law relationship between flow and store. The underlying mechanisms of energy flow patterns are explained as the branching transportation networks which can be regarded as the result of systematic optimization of a biological target under constraints. Energy flows in the ecological system may be modelled by the food web model and population dynamics on the network. This paper reviews the latest progress on the energy flow patterns, explanatory models for the allometric scaling and modelling approach of flow and network evolution dynamics in ecology. Furthermore, the possibility of generalizing these flow patterns, modelling approaches to other complex systems is discussed. This research is supported by Guozhi Xu Post Doctoral Research Foundation and the National Natural Science Foundation of China under Grant No. 60574068.     

Expert discovery and knowledge mining in complex multi-agent systems

Complex problem solving requires diverse expertise and multiple techniques. In order to solve such problems, complex multi-agent systems that include both of human experts and autonomous agents are required in many application domains. Most complex multi-agent systems work in open domains and include various heterogeneous agents. Due to the heterogeneity of agents and dynamic features of working environments, expertise and capabilities of agents might not be well estimated and presented in these systems. Therefore, how to discover useful knowledge from human and autonomous experts, make more accurate estimation for experts＇ capabilities and find out suitable expert（s） to solve incoming problems （＂Expert Mining＂） are important research issues in the area of multi-agent system. In this paper, we introduce an ontology-based approach for knowledge and expert mining in hybrid multi-agent systems. In this research, ontologies are hired to describe knowledge of the system. Knowledge and expert mining processes are executed as the system handles incoming problems. In this approach, we embed more self-learning and self-adjusting abilities in multi-agent systems, so as to help in discovering knowledge of heterogeneous experts of multi-agent systems.     

基于模糊逻辑的一类复杂系统全息控制设计

针对一类结构上具有相似性的非线性不确定复杂系统,利用模糊逻辑系统可以充分逼近连续函数的性质,研究这类复杂系统的鲁棒镇定问题,设计出了系统的全息鲁棒控制器和模糊逻辑系统参数估计的自适应律。为了简化控制器结构,在构造模糊逻辑系统时对被控系统的不确定性进行了归一化处理。在较弱的假设条件下,证明了这种控制器使被控系统的状态及参数估计误差一致终极有界,同时给出了误差的严密的数学分析。仿真实例表明所提出的方法是有效的。     

两类市场结构下企业生产方式选择决策

在当今产品日益多样化及交付期日益缩短的市场需求环境下,选择传统大量生产方式还是大规模定制生产方式来组织生产已成为大多数企业面临的迫切课题.针对此课题,分别构建了完全垄断与双寡头垄断两类市场结构下的企业利润模型,并运用子博弈精炼纳什均衡的有效求解方法--逆向归纳法,对完全垄断市场结构下大量生产方式决策和大规模定制生产方式决策、双寡头垄断市场结构下大量生产方式与大规模定制生产方式不同组合决策问题进行了系统分析与求解,得出了各种不同条件下的企业生产方式选择方案.研究成果为企业在两类市场结构下生产方式选择决策提供了理论与方法支持.     

复杂网络视角下的大规模群体DEMATEL决策方法

针对如何实现群体DEMATEL意见集结的问题,以关系网络为切入点,提出一种新的复杂网络视角下的大规模群体DEMATEL决策方法.首先,将决策者视为网络节点,并依据决策者DEMATEL评分矩阵的一致性构造复杂网络;其次,设计网络凝聚度和子群凝聚度,在此基础上确定节点权重和子群权重;最后,基于网络密度算子对大规模群体DEMATEL矩阵进行有效集结,并利用DEMATEL方法进行多属性决策.文末通过一个算例验证了所提方法的可行性与合理性.数值算例分析表明,该方法能够较为全面、准确地集结大规模群体DEMATEL评分,且其结果的稳定性亦较为理想.     

基于突变理论与模糊集的复杂系统多准则决策

针对复杂系统的多准则决策通常具有模糊性和突变理论适合处理具有矛盾性的多准则决策问题的特点,提出了基于突变理论和模糊集的方法对多准则决策问题进行分析建模,并对该方法的灵敏度进行了分析。该方法利用归一公式机理本身来确定各指标对各目标重要性的确定性量化,因而不需要额外考虑指标间的主观权重,所以能够较好地处理复杂系统决策的模糊性。给出了应用改方法的具体步骤,并以一个导弹选型决策问题实例验证了该方法的合理性和可行性。     

A complex systems science perspective on wireless networks

The paper targets a future world where all wireless networks are self-organising entities and in which the predominant mode of spectrum access is dynamic. The paper explores whether the behaviour of a collection of autonomous self-organising wireless systems can be treated as a complex system and whether complex systems science can shed light on the design and deployment of these networks. The authors focus on networks that self-organise from a frequency perspective to understand the behaviour of a collection of wireless self-organising nodes. Each autonomous network is modelled as a cell in a lattice and follows a simple set of self-organisation rules. Two scenarios are considered, one in which each cell is based on cellular automata and which provides an abstracted view of interference and a second in which each cell uses a self-organising technique which more accurately accounts for interference. The authors use excess entropy to measure complexity and in combination with entropy gain an understanding of the structure emerging in the lattice for the self-organising networks. The authors show that the self-organising systems presented here do exhibit complex behaviour. Finally, the authors look at the robustness of these complex systems and show that they are robust against changes in the environment.