An interactive approach to multiple criteria optimization with multiple decision-makers

In this article we propose a formal man-machine interactive approach to multiple criteria optimization with multiple decision makers. The approach is based on some of our earlier research findings in multiple criteria decision making. A discrete decision space is assumed. The same framework may readily be used for multiple criteria mathematical programming problems. To test the approach two experiments were conducted using undergraduate Business School students as subjects in Finland and in the United States. The context was, respectively, a high-level Finnish labor-management problem and the management-union collective bargaining game developed at the Krannert Graduate School of Management, Purdue University. The results of the experiments indicate that our approach is a potentially useful decision aid for group decision-making and bargaining problems.     

随机动态规划的混合算法研究

针对随机条件下动态规划模型的主要特点,运用智能算法混合编程理论,设计了一种探索多阶段决策问题的智能混合算法.该算法首先将问题转化成一族同类型的一步决策子问题,然后利用随机模拟和遗传算法,依据训练样本形成的训练神经元网络,在单步决策中寻求最优策略和最优目标值,逐个求解,再据初始状态逆序求出最优策略序列和最优目标值.仿真结果表明,该算法具有一定的通用性,初始设计点可以随机产生,其计算精度不因函数的非线性强弱而受影响,对目标和约束的限制较少,可应用于多种形式的随机多阶段决策优化问题,较好地满足了随机动态规划模型求解和优化的要求.     

The interactive fixed charge linear programming problem

In many decision-making situations, each activity that can be undertaken may have associated with it both a fixed and a variable cost. Recently, we have encountered serveral practical problems in which the fixed cost of undertaking an activity depends upon which other activities are also undertaken. To our knowledge, no existing optimization model can accomodate such a fixed cost structure. To do so, we have therefore developed a new model called the interactive fixed charge linear programming problem (IFCLP). In this paper we present and motivate problem (IFCLP), study some of its characteristics, and present a finite branch and bound algorithm for solving it. We also discuss the main properties of this algorithm.     

面向装备作战仿真的弹药调拨供应决策建模方法研究

针对用线性规划方法在解决弹药调拨供应决策过程中存在的弹药调拨供应量的分配与弹药保障原则关联不紧密的问题,采用目标规划理论与方法,引入优先级区分主要作战方向和次要作战方向弹药库的重要程度,在同一优先级上以权重区分一梯队、二梯队及预备队弹药库的弹药保障优先顺序,使得模型的构建过程与弹药保障原则紧密相联。同时,在模型构建过程中,综合考虑了弹药消耗预测量、弹药储备标准和弹药运输时限等因素对弹药调拨供应决策的影响,并从本级弹药储备不足和充足两种情况进行了数值模型及分析,验证了模型的正确性与可靠性,为弹药调拨供应提供了决策支持与理论支撑。     

流动危险源泄漏事故应急疏散决策优化

在流动危险源泄漏事故中,如何根据流动危险源的危险特性,快速制定最优疏散决策,使应急疏散的成本最低是现场指挥人员急需解决的重要问题。在分析讨论疏散范围、疏散人员、疏散方式、疏散路径、避难场所等影响应急疏散决策因素的基础上,采用线性规划方法,建立了应急疏散的优化模型。以运输液氯槽车泄漏事故为例,研究危险区域的人员疏散,并利用Lingo软件对优化模型进行了求解,得到了应急疏散的最佳方案。研究结果对消防部队有效地指导疏散救人行动具有参考价值。     

What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

An interactive branch-and-bound algorithm for bicriterion nonconvex/mixed integer programming

Although there has been extensive research on interactive multiple objective decision making in the last two decades, there is still a need for specialized interactive algorithms that exploit the relatively simple structure of bicriterion programming problems. This article develops an interactive branch-and-bound algorithm for bicriterion nonconvex programming problems. The algorithm searches among only the set of nondominated solutions since one of them is a most preferred solution that maximizes the overall value function of the decision maker over the set of achievable solutions. The interactive branch-and-bound algorithm requires only pairwise preference comparisons from the decision maker. Based on the decision maker's responses, the algorithm reduces the set of nondominated solutions and terminates with his most preferred nondominated solution. Branching corresponds to dividing the subset of nondominated solutions considered at a node into two subsets. The incumbent solution is updated based on the preference of the decision maker between two nondominated solutions. Fathoming decisions are based on the decision maker's preference between the incumbent solution and the ideal solution of the node in consideration.     

基于随机提前期的备件携行量计算方法

舰艇出海执行任务期间,需要携行一定种类和数量的物资自给。文中研究了这种单级供应条件下,如何确定备件最优携行量的问题。建立了以服务水平为目标,以资源为约束的目标规划模型,模型考虑了随机提前期和备件重要度。提出一种基于边际效费比的增量法用于求解这个模型。最后,给出一个算例并得到了一些结论。     

认知电子战综述

认知电子战通常被定义为以具备认知性能的电子战装备为基础,注重自主交互式的电磁环境学习能力与动态智能化的对抗任务处理能力的电子战形态。自其被首次提出以来,以其感知准、推理强、决策快的优势备受国内外研究学者广泛关注。随着人工智能新理念、新技术、新应用的不断涌现,认知电子战步入崭新的发展阶段。为捕捉其未来发展方向,从人工智能角度出发,总结并丰富了认知电子战概念内涵,梳理认知电子战的发展脉络及外国典型项目,搭建认知电子战系统框架及架构,从感知、判断、决策等方面对认知听电子战关键技术进行了全面系统综述,并总结了认知电子战面临的挑战和发展趋势。     

Interactive solution of bi-criteria mathematical programs

In this paper, we develop efficient interactive methods for the solution of bicriteria nonlinear programming problems. The methods do not require trade-off information from the decision maker, pose less cognitive burden and converge to the “best compromise solution” fast. Two methods, called the paired comparison method and comparative trade-off method, are presented with examples. A real application of the interactive method to a bicriteria problem that arose in the planning of the cardiovascular disease control program in the U.S. Air Force is also presented.     

面向武器装备体系组合规划的集成决策优化框架

针对当前武器装备体系组合规划存在选择空间规模大、决策目标数量多等问题,提出一种集成决策优化框架,用于组合选择和规划武器装备的发展型号、时间和数量。首先对武器装备体系组合规划问题的NP-Hard和高维多目标性质进行定量化分析和公式化描述;然后采用目标规划方法将该问题构建为双目标优化模型;再基于NSGA-II多目标演化计算方法,开发面向本问题的优化算法,求得该模型的Pareto解集合;最后通过TOPSIS方法,从Pareto解集合中求取符合决策者偏好的满意解。通过某侦察预警监视体系发展规划示例,验证了当给定经验数据和决策者偏好信息后,该框架可获得符合要求的武器装备体系组合规划方案,能够支撑武器装备体系发展论证和规划。     

Interactive optimization methodology for fleet scheduling

This article addresses the problem of scheduling the United States Navy's Atlantic Fleet to satisfy overseas strategic requirements. An integer programming formulation is developed but results in a model with prohibitive size. This fact and the qualitative nature of additional secondary objectives and constraints suggest an interactive optimization approach. A system that solves a natural relaxation of the integer program within an interactive environment is discussed.     

弹炮混编防空群兵力分配模型

针对对付现代日益发展的空袭手段的一种有效方法--弹炮混编防空布势中存在的防空武器种类繁多,兵力分配复杂,难以充分发挥各自效能的问题,建立了非线性规划模型,利用动态规划对该模型进行分析,运用拉格朗日降维法进行降维,将三维降为二维.经防空作战的实例验证该算法不但能将两维降到单维,而且还可以将多维降到单维,能节省计算机的内存使用率,提高仿真计算的速度,符合信息化条件下指挥决策实时化的要求且分配效果较好.     

信息不完备群组决策问题的ANP方法

群组决策是一类重要的决策问题,信息不完备的群组决策问题,因为判断矩阵有缺损,传统的AHP不能求解.文中将评价图引入信息不完备的群组决策问题,在此基础上提出用二次规划问题解决在这种情况下的权重确定问题,最后通过ANP的超矩阵得到总排序结果.经过实例检验,证明了ANP方法的正确性与可行性.     

基于Z形式化规范的多Agent协作模型研究

用形式化规范语言Z定义了多A gen t协作模型的基本元素,包括:A gen t、目标、角色、交互协议和过程,给出了应满足的一致性约束,并通过城市区域的连锁超市物流调度系统说明了该形式化规范为多A gen t协作提供了一致性和连贯性。     

对策理论的防空作战决策级融合模型

随着防空C3I系统的发展,防空作战决策的过程较以往更为自动化,用于生成各种作战辅助决策方案的计算机模型也具备随着作战环境改变所表现出一定的自适应调整的特征.影响防空作战决策结果正确性的因素众多,表现这些因素的信息有些是冲突的,为得到更为准确、有效的决策方案,各类冲突信息的处理成为防空作战的核心问题.构造了一种在冲突信息环境下基于对策理论的融合模型,以对策理论的相关算法实现了融合中心各类信息的交互决策.实践表明,这一融合模型能有效地解决防空作战决策中各类信息的综合处理问题,具有一定的实用价值.     

二维数控机床零件加工的自动编程系统

面向部队修理分队的编程人员,提供了一种界面美观、交互性好的自动编程系统,目的是减少数控机床在零件加工过程中人的参与,进一步提高加工自动化程度.该软件以Visual C 作为开发工具,具有良好的用户界面,操作简单、方便,输入的参数尽可能少,降低数控编程对编程人员过高要求,简化数控编辑,因而编程效率大大提高,并可避免许多人为错误,提高了编程质量.     

一种基于多目标优化的QoS路由交互式算法

为了满足通信网络中一些特定业务对于多个网络指标性能的同时要求 ,研究了一类基于多目标决策的QoS路由算法。通过选取带宽作为约束条件 ,把时延和丢失率作为优化目标 ,建立了QoS路由选择的多目标非线性整数规划模型 ,并给出了一种求解模型的交互式算法。该算法通过逐步调整目标函数的上界 ,压缩目标函数的搜索空间来满足决策者的要求和网络条件。实例计算结果表明了算法的可行性     

合成分队双层主从决策模型研究

针对合成分队不同指挥层级之间的决策交互优化问题,提出了基于主从决策的双层武器目标分配模型。该模型能够体现上下指挥层级之间交互式决策协调优化的特点:上层目标为主攻方向我方遭受威胁最小,下层目标为对敌打击最大。结合合成分队作战决策的特点和要求,提出了一种求解该模型的改进型粒子群优化算法。仿真结果表明,该模型合理有效,改进的求解算法能够获得满意解。     

Entropy-based selection with multiple objectives

In this article we present an approach to determine the initially unspecified weights in an additive measurable multiattribute value function. We formulate and solve a series of nonlinear programming problems which (1) incorporate whatever partial information concerning the attribute weights or overall relative value of alternatives the decision maker chooses to provide, yet (2) yield a specific set of weights as a result. Although each formulation is rather easily solved using the nonlinear programming software GINO (general interactive optimizer), solutions in closed form dependent on a single parameter are also provided for a number of these problems.