Extending the MAD portfolio optimization model to incorporate downside risk aversion

A mathematical model of portfolio optimization is usually represented as a bicriteria optimization problem where a reasonable tradeoff between expected rate of return and risk is sought. In a classical Markowitz model, the risk is measured by a variance, thus resulting in a quadratic programming model. As an alternative, the MAD model was developed by Konno and Yamazaki, where risk is measured by (mean) absolute deviation instead of a variance. The MAD model is computationally attractive, since it is easily transformed into a linear programming problem. An extension to the MAD model proposed in this paper allows us to measure risk using downside deviations, with the ability to penalize larger downside deviations. Hence, it provides for better modeling of risk averse preferences. The resulting m‐MAD model generates efficient solutions with respect to second degree stochastic dominance, while at the same time preserving the simplicity and linearity of the original MAD model. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 185–200, 2001     

Capacitated selection problem

Optimizing the selection of resources to accomplish a set of tasks involves evaluating the tradeoffs between the cost of maintaining the resources necessary to accomplish the tasks and the penalty cost associated with unfinished tasks. We consider the case where resources are categorized into types, and limits (capacity) are imposed on the number of each type that can be selected. The objective is to minimize the sum of penalty costs and resource costs. This problem has several practical applications including production planning, new product design, menu selection and inventory management. We develop a branch‐and‐bound algorithm to find exact solutions to the problem. To generate bounds, we utilize a dual ascent procedure which exploits the special structure of the problem. Information from the dual and recovered primal solutions are used to select branching variables. We generate strong valid inequalities and use them to fix other variables at each branching step. Results of tests performed on reasonably sized problems are presented. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 19–37, 1999     

坦克兵力行为智能体模型研究

为了有效模拟兵力物理行为和智能行为的不同特性,提出了一个复合结构坦克兵力智能体模型,该模型综合了反应式行为和慎思行为两方面的优点。采用层次化组合方法建立了基于行为模式的兵力物理行为模型,使兵力智能体能对动态战场环境作出灵活的反应。结合兵力不同类型任务的分析,设计了基于规划的兵力智能行为模拟方法,使兵力智能体能按照战术原则进行作战任务规划。     

排课中合班问题的探讨

基于排课的实际需要,分析了合班对排课的影响,结合遗传算法在排课中的应用,建立了针对班级上课时间分布均匀性以及课程上课时间分布均匀性的适配值函数.在对课程表进行编码后,把合班问题的解决方案应用于排课算法,使用合班信息与课表编码信息的双表操作,从而能在遗传基因中判断出非法个体,并能进行合理化调整.算例结果表明本算法能有效解决排课中的合班问题.     

排课中时间表问题的探讨

提出一种时间表的设计方案及其改进形式,讨论了它的相关性能。应用本方案,不仅可以提高排课中搜索的效率(最高可达(2n 1)/2倍),还可以满足排课过程中对时间的各种需求,具有很强的适应性和可移植性;再利用层次分析法得到各时间段的可接受度,分析了使用改进时间表的公平性,其公平性因子将得到成倍提高。     

翼伞系统线目标归航方法

针对运载火箭助推器或子级无控坠落地面落点散布较大的情况,提出一种翼伞系统的线目标归航方法。建立了矩形边界下的归航计算模型,对点目标归航和线目标归航两种方法进行了收缩区域分析;对翼伞归航能力、落区散布边界特征对归航性能的影响进行对比分析,得到了相应的归航策略,并提出在较大散布区域下设置多个着陆区域的工程解决方案;建立动力学和操纵控制模型,对线目标归航过程进行了仿真。结果表明,所提方法对工程有一定的参考价值。     

The Serbian defence institution: the need for clear policy guidance to improve the Serbian defence planning,management, and command processes

ABSTRACT

The uncritical layering of western liberal defence governance norms and concepts on top of existing legacy concepts has impeded achieving coherent military capabilities and capacities when Serbia’s political and military leadership tried to reform the defence system using Western benchmarking principles and Western countries’ best practices. The process of this change has been more valuable than its actual output, as defined by increased capabilities. Such outcomes should be reflective of policy guidance, and can be thought of as closing the trinity loop of a defence planning system: plans, money and execution. This article addresses two key functional areas of the Serbian defence institution. First, it assesses the current state of defence planning to discern its strengths and weaknesses to ascertain if plans are tied to financial decision-making. Second, a full examination of current Serbian defence management is conducted to discern whether weaknesses exist that distract from producing operational capabilities. Both areas are analyzed thoroughly and some solutions for change are proposed. Also, the article analyzes the appearance of two negative phenomena in the planning process – economization and managerialism.     

Optimal motion planning in rapid‐fire combat situations with attacker uncertainty

This article provides a modeling framework for quantifying cost and optimizing motion plans in combat situations with rapid weapon fire, multiple agents, and attacker uncertainty characterized by uncertain parameters. Recent developments in numerical optimal control enable the efficient computation of numerical solutions for optimization problems with multiple agents, nonlinear dynamics, and a broad class of objectives. This facilitates the application of more realistic, equipment‐based combat models, which track both more realistic models, which track both agent motion and dynamic equipment capabilities. We present such a framework, along with a described algorithm for finding numerical solutions, and a numerical example.     

通信对抗无人机集群侦察区域规划规则研究

侦察区域规划是通信对抗无人机集群智能侦察的首要任务.能否合理规划侦察区域,对通信对抗侦察任务的完成至关重要.通过研究侦察区域规划分配和动态调整流程,结合通信对抗战斗原则和相关军事经验,整理归纳出通信对抗无人机集群进行侦察区域规划的行为规则、判定规则和策略规则,并提出了规则的使用建议,构想了具体应用场景.     

A general strategic capacity planning model under demand uncertainty

Capacity planning decisions affect a significant portion of future revenue. In equipment intensive industries, these decisions usually need to be made in the presence of both highly volatile demand and long capacity installation lead times. For a multiple product case, we present a continuous‐time capacity planning model that addresses problems of realistic size and complexity found in current practice. Each product requires specific operations that can be performed by one or more tool groups. We consider a number of capacity allocation policies. We allow tool retirements in addition to purchases because the stochastic demand forecast for each product can be decreasing. We present a cluster‐based heuristic algorithm that can incorporate both variance reduction techniques from the simulation literature and the principles of a generalized maximum flow algorithm from the network optimization literature. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006