An approximate dynamic programing approach to the development of heuristics for the scheduling of impatient jobs in a clearing system

A single server is faced with a collection of jobs of varying duration and urgency. Each job has a random lifetime during which it is available for nonpreemptive service. Should a job's lifetime expire before its service begins then it is lost from the system unserved. The goal is to schedule the jobs for service to maximize the expected number served to completion. Two heuristics have been proposed in the literature. One (labeled π^S) operates a static priority among the job classes and works well in a “no premature job loss” limit, whereas the second (π^M) is a myopic heuristic which works well when lifetimes are short. Both can exhibit poor performance for problems at some distance from the regimes for which they were designed. We develop a robustly good heuristic by an approximative approach to the application of a policy improvement step to the asymptotically optimal heuristic π^S, in which we use a fluid model to obtain an approximation for the value function of π^S. The performance of the proposed heuristic is investigated in an extensive numerical study. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

模糊启发规则的防空作战智能指控系统决策

为提高防空作战智能指控系统决策效率,在充分考虑专家经验知识的基础上,利用模糊启发规则对智能指控系统决策模型中的规则和推理方法进行改进.通过对防空作战打击决策输出结果的编程计算,得到打击决策输出方案与实际作战决策结果一致.该方法可使模糊规则条数减少,输入向量和模糊矩阵的维数降低,推理计算速度加快,却不影响最终决策输出结果.     

Efficient sequential decision‐making algorithms for container inspection operations

Following work of Stroud and Saeger (Proceedings of ISI, Springer Verlag, New York, 2006) and Anand et al. (Proceedings of Computer, Communication and Control Technologies, 2003), we formulate a port of entry inspection sequencing task as a problem of finding an optimal binary decision tree for an appropriate Boolean decision function. We report on new algorithms for finding such optimal trees that are more efficient computationally than those presented by Stroud and Saeger and Anand et al. We achieve these efficiencies through a combination of specific numerical methods for finding optimal thresholds for sensor functions and two novel binary decision tree search algorithms that operate on a space of potentially acceptable binary decision trees. The improvements enable us to analyze substantially larger applications than was previously possible. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Quay crane scheduling at container terminals to minimize the maximum relative tardiness of vessel departures

In this paper, we study the problem of scheduling quay cranes (QCs) at container terminals where incoming vessels have different ready times. The objective is to minimize the maximum relative tardiness of vessel departures. The problem can be formulated as a mixed integer linear programming (MILP) model of large size that is difficult to solve directly. We propose a heuristic decomposition approach to breakdown the problem into two smaller, linked models, the vessel‐level and the berth‐level models. With the same berth‐level model, two heuristic methods are developed using different vessel‐level models. Computational experiments show that the proposed approach is effective and efficient. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

二级库存-运输系统的联合经济订购模型

结合器材供应的实际,构建了一个国家仓库-地区仓库形成的二级库存-运输系统,以最低费用为目标,建立了该系统的联合经济订购模型,求解了该系统各仓库的最佳订货量和订货次数;运用该模型对3种订货策略（年度1次订货、年度多次订货、零库存订货）进行了算例验证,表明了该模型在优化多级库存运输系统订货参数方面的意义。     

启发式教学在心理健康教育中的应用及思考

启发式教育有利于发展学生智力、培养创新能力、开阔思维,是目前绝大多数教育者推崇的教学模式。本文对如何把启发式教学模式融入心理健康教学过程进行了探索,构建了"激发、启发、开发、焕发"四步启发式教学模式。启发式教学从学生的实际出发,依据学习规律和学生身心发展规律,体现了素质教育的思想;同时,在教学中调动学生积极性、主动性和创造性,提高学生分析问题和解决问题的能力。     

【专题】无人机空战决策技术研究进展

随着无人机相关技术领域的飞速发展,无人机迅速成为世界各国军事领域的研究热点。无人机自主决策作为无人机领域的核心问题,指的是无人机基于空战态势,利用数学优化理论、人工智能等方法,独立自主地生成机动动作控制指令以完成设定目标的过程。本文首先介绍了世界各国该领域的研究进展,并基于空战决策的求解思路,将决策方法分为三类：基于对策理论、基于专家知识以及基于启发式学习算法的决策方法。其次,针对基于对策理论的空战决策方法,阐述了从微分对策到矩阵对策的发展及联系;针对基于专家知识的空战决策方法,介绍了该类方法的建模方法,改进方向;针对基于启发式学习算法的决策方法,论述了各典型方法的适用条件、改进途径等。最后,对无人机空战决策的研究难点进行分析,并展望了未来的研究方向与趋势。     

舰艇防空反导自适应干扰HDP算法分析与仿真

为干扰来袭的多波次、多方向反舰导弹,提高舰艇防空反导能力,分析了自适应干扰HDP算法模型,并通过实例进行仿真。通过对结果进行分析,验证了启发式动态规划(HDP)算法在自适应电子干扰策略最优组合的生成过程中的时效性、预测精度以及适用性,为该算法的后续研究提供支持。     

基于赋时Petri网的自动测试系统建模

提出了利用赋时Petri网(Tim ed Petri Net)对自动测试系统建模和性能分析的方法。采用启发式算法,对系统的可达树进行搜索,实现测试序列的优化,不仅降低了计算的复杂度,而且获得了满意的并行度,提高了自动测试系统的测试效率。     

两级修理模式下民机修理级别非经济性分析研究

针对两级修理模式在飞机维修规划中得到逐步重视的问题,提出了两级修理模式下民机修理级别非经济性分析模型。其中基于GJB-2961-1997标准,建立了两级修理级别分析的指标体系,在此基础上,利用模糊层次分析法确定各评价指标的权系数,并对灰色关联系数加权得到灰色关联度,实现了基于模糊层次分析法和灰色关联分析法的飞机修理级别综合评估。最后通过实例分析,验证了所提模型的合理性和有效性。