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如何充分发挥小型通信干扰装备的作战效能是指挥员面临的棘手问题。本文将战术指标——干扰后的通信畅通区覆盖程度作为评估通信干扰装备分配优劣的指标,分别建立了单机和多部干扰时的通信干扰有效压制区和通信畅通区边界的计算模型,并给出了通信干扰有效压制区和通信畅通区面积的计算方法。构建了基于机会约束规划的空域频域通信干扰任务分配模型,并设计了混合蚁群算法和遗传算法的模型求解算法。最后进行了仿真实验,验证分析了模型和算法的合理性。研究成果为通信对抗战术计算和作战运用研究拓展了思路。 相似文献
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投掷式通信干扰机是未来通信对抗装备发展的一种趋势,对任务区域内无线战术通信实施投掷式干扰时,需要在干扰压制概率满足一定置信水平的条件下最小化干扰机的需求量。建立了基于随机机会约束规划的投掷式通信干扰兵力部署模型,采用随机模拟、遗传算法相结合的混合智能算法求解模型,并通过实例分析验证了模型的有效性。 相似文献
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在分析影响雷达网系统效能的几个重要的不确定因素基础上,应用随机机会约束规划,分别建立雷达网中雷达数量估计和部署的数学模型,并用混合智能算法求解建立的数学模型,得到随机机会约束条件下的最优雷达网结构。 相似文献
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本文引进压制概率这一概念作为衡量雷达对抗作战效能的数量指标 ,给出了地对空雷达对抗压制概率的计算方法。利用搜索论、规划论、雷达原理、雷达对抗原理建立了一个地对空雷达对抗干扰任务分配模型 相似文献
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There are n boxes with box i having a quota value Balls arrive sequentially, with each ball having a binary vector attached to it, with the interpretation being that if Xi = 1 then that ball is eligible to be put in box i. A ball's vector is revealed when it arrives and the ball can be put in any alive box for which it is eligible, where a box is said to be alive if it has not yet met its quota. Assuming that the components of a vector are independent, we are interested in the policy that minimizes, either stochastically or in expectation, the number of balls that need arrive until all boxes have met their quotas. © 2014 Wiley Periodicals, Inc. 62:23–31, 2015 相似文献
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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 相似文献
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在应用GA求解大规模无人作战飞机(UCAV s)任务分配这个典型组合优化问题时,需要使用描述问题直观的序号编码方式,但由于传统的交叉、变异算子操作复杂,因而进化效率不高。针对上述的不足,提出了一种单亲遗传算法,采用序号编码,使用基因换位等遗传算子,简化了遗传操作。通过对单亲遗传算法、传统遗传算法求解该问题所得的结果作了详细的比较,证明了单亲遗传算法在寻优效率上的优越性。 相似文献
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In this article, we address a stochastic generalized assignment machine scheduling problem in which the processing times of jobs are assumed to be random variables. We develop a branch‐and‐price (B&P) approach for solving this problem wherein the pricing problem is separable with respect to each machine, and has the structure of a multidimensional knapsack problem. In addition, we explore two other extensions of this method—one that utilizes a dual‐stabilization technique and another that incorporates an advanced‐start procedure to obtain an initial feasible solution. We compare the performance of these methods with that of the branch‐and‐cut (B&C) method within CPLEX. Our results show that all B&P‐based approaches perform better than the B&C method, with the best performance obtained for the B&P procedure that includes both the extensions aforementioned. We also utilize a Monte Carlo method within the B&P scheme, which affords the use of a small subset of scenarios at a time to estimate the “true” optimal objective function value. Our experimental investigation reveals that this approach readily yields solutions lying within 5% of optimality, while providing more than a 10‐fold savings in CPU times in comparison with the best of the other proposed B&P procedures. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 131–143, 2014 相似文献
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We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap. 相似文献