推理树概率逻辑公式集有效赋值列数计算

本文通过对概率逻辑推理树知识结构模型的研究,得出在树推理中对矩阵列消减起主要作用的相邻关系及其计算方法,给出了树推理模型的有效赋值列数的一般计算方法。     

空地支援多目标模型

本文研究空地支援优化问题。模型的构造是基于目标规划理论。首先建立基础模型,然后根据问题条件,建立多目标模型。     

战场弹药贮供建模分析

本文运用库存论、目标规划和计算机模拟等理论与方法,分析研究了战场弹药的储备与补充问题,建立了“两级”弹药贮供数学模型,并应用实战数据在计算机上进行了模拟运算和分析。     

C3I时隙分配优化设计

用线性规划方法讨论了Ｃ３Ｉ通信网络中时隙分配的优化设计问题     

高级程序设计语言综述

为读者选择语言提供了总体思路,给出了高级语言的发展的趋势。     

科研经费管理决策支持系统若干模型

本文应用多目标决策原理和计算机技术,在分析用户需求的基础上,建立了科研任务项目的预测评价和经费分配模型,并分析了有关模型的关系和解决性质。根据这些模型和决策的特点,简要叙述了该系统的主要功能。我们相信,该系统的应用,将给管理人员提供方便,且使管理进一步科学化,让有限的经费发挥更大的作用。     

Cyclic best first search: Using contours to guide branch‐and‐bound algorithms

The cyclic best‐first search (CBFS) strategy is a recent search strategy that has been successfully applied to branch‐and‐bound algorithms in a number of different settings. CBFS is a modification of best‐first search (BFS) that places search tree subproblems into contours which are collections of subproblems grouped in some way, and repeatedly cycles through all non‐empty contours, selecting one subproblem to explore from each. In this article, the theoretical properties of CBFS are analyzed for the first time. CBFS is proved to be a generalization of all other search strategies by using a contour definition that explores the same sequence of subproblems as any other search strategy. Further, a bound is proved between the number of subproblems explored by BFS and the number of children generated by CBFS, given a fixed branching strategy and set of pruning rules. Finally, a discussion of heuristic contour‐labeling functions is provided, and proof‐of‐concept computational results for mixed‐integer programming problems from the MIPLIB 2010 database are shown. © 2017 Wiley Periodicals, Inc. Naval Research Logistics, 64: 64–82, 2017     

Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

We propose a novel simulation‐based approach for solving two‐stage stochastic programs with recourse and endogenous (decision dependent) uncertainty. The proposed augmented nested sampling approach recasts the stochastic optimization problem as a simulation problem by treating the decision variables as random. The optimal decision is obtained via the mode of the augmented probability model. We illustrate our methodology on a newsvendor problem with stock‐dependent uncertain demand both in single and multi‐item (news‐stand) cases. We provide performance comparisons with Markov chain Monte Carlo and traditional Monte Carlo simulation‐based optimization schemes. Finally, we conclude with directions for future research.     

Exact algorithms and bounds for the dynamic assignment interdiction problem

We study a multi‐stage dynamic assignment interdiction (DAI) game in which two agents, a user and an attacker, compete in the underlying bipartite assignment graph. The user wishes to assign a set of tasks at the minimum cost, and the attacker seeks to interdict a subset of arcs to maximize the user's objective. The user assigns exactly one task per stage, and the assignment costs and interdiction impacts vary across stages. Before any stage commences in the game, the attacker can interdict arcs subject to a cardinality constraint. An interdicted arc can still be used by the user, but at an increased assignment cost. The goal is to find an optimal sequence of assignments, coupled with the attacker's optimal interdiction strategy. We prove that this problem is strongly NP‐hard, even when the attacker can interdict only one arc. We propose an exact exponential‐state dynamic‐programming algorithm for this problem as well as lower and upper bounds on the optimal objective function value. Our bounds are based on classical interdiction and robust optimization models, and on variations of the DAI game. We examine the efficiency of our algorithms and the quality of our bounds on a set of randomly generated instances. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 373–387, 2017     

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。