A genetic algorithm with neighborhood search for the resource‐constrained project scheduling problem

The resource‐constrained project scheduling problem (RCPSP) consists of a set of non‐preemptive activities that follow precedence relationship and consume resources. Under the limited amount of the resources, the objective of RCPSP is to find a schedule of the activities to minimize the project makespan. This article presents a new genetic algorithm (GA) by incorporating a local search strategy in GA operators. The local search strategy improves the efficiency of searching the solution space while keeping the randomness of the GA approach. Extensive numerical experiments show that the proposed GA with neighborhood search works well regarding solution quality and computational time compared with existing algorithms in the RCPSP literature, especially for the instances with a large number of activities. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Technical note: Find a hidden “treasure”

This paper deals with a two searchers game and it investigates the problem of how the possibility of finding a hidden object simultaneously by players influences their behavior. Namely, we consider the following two‐sided allocation non‐zero‐sum game on an integer interval [1,n]. Two teams (Player 1 and 2) want to find an immobile object (say, a treasure) hidden at one of n points. Each point i ∈ [1,n] is characterized by a detection parameter λ_i (μ_i) for Player 1 (Player 2) such that p_i(1 ? exp(?λ_ix_i)) (p_i(1 ? exp(?μ_iy_i))) is the probability that Player 1 (Player 2) discovers the hidden object with amount of search effort x_i (y_i) applied at point i where p_i ∈ (0,1) is the probability that the object is hidden at point i. Player 1 (Player 2) undertakes the search by allocating the total amount of effort X(Y). The payoff for Player 1 (Player 2) is 1 if he detects the object but his opponent does not. If both players detect the object they can share it proportionally and even can pay some share to an umpire who takes care that the players do not cheat each other, namely Player 1 gets q₁ and Player 2 gets q₂ where q₁ + q₂ ≤ 1. The Nash equilibrium of this game is found and numerical examples are given. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Constrained minimax optimization of continuous search efforts for the detection of a stationary target

Analytical resolution of search theory problems, as formalized by B.O. Koopman, may be applied with some model extension to various resource management issues. However, a fundamental prerequisite is the knowledge of the prior target density. Though this assumption has the definite advantage of simplicity, its drawback is clearly that target reactivity is not taken into account. As a preliminary step towards reactive target study stands the problem of resource planning under a min–max game context. This paper is related to Nakai's work about the game planning of resources for the detection of a stationary target. However, this initial problem is extended by adding new and more general constraints, allowing a more realistic modeling of the target and searcher behaviors. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

基于PCI总线的多路数据总线接口设计与实现

提出了一种主处理机可以直接控制的1553B总线接口模块的设计与实现方案,并简要介绍了其硬件结构、工作过程和软件开发.利用PCI协议芯片实现PCI局部总线与军用1553B总线的桥接,采用WDM技术编写驱动程序.所设计的总线接口模块符合MIL-STD-1553B总线标准,具有结构简单、成本低廉、操作方便等特点,可用于仿真,开发1553B总线产品.     

任务分配问题的对称群方法

探讨了无人飞行器(UAV)编队的任务分配问题。任务分配是UAV协同控制的基础,其解是任务区域内各任务的一个排列。求解UAV任务分配问题的有效方法是能在合理的计算时间内找到近似最优解的启发式算法。用对称群描述UAV任务分配的搜索空间,基于右乘运算构造搜索邻域。仿真结果验证了群论禁忌搜索算法的有效性。     

高超声速飞行器组合导航体系效能评估

集合当今世界高精尖端技术的高超声速飞行器,单一或简单的组合导航方式已经无法保障其高超声速全球到达的飞行任务。研究了一种INS\GNSS\CNS组合导航体系方案,与典型的INS\GNSS组合导航系统方案相比较,并采用改进的贝叶斯法融合算法对这两种导航方案进行综合效能评估。结果证明,该评估方法切实可行,便于计算应用,证实了INS\GNSS\CNS组合导航体系保障高超声速飞行器作战能力更强。     

一种基于部分搜索的GNSS模糊度解算方法

为解决LAMBDA算法搜索策略效率不高的问题,提出了一种基于部分搜索的模糊度解算新方法。该方法的解算成功率接近LAMBDA算法,且只需对最后3维模糊度元素采取搜索策略。同时,通过合理设定搜索椭球的大小和搜索策略;大大提高了搜索效率。通过实验对该新算法与Bootraping算法、LAMBDA算法进行了比较分析,进一步验证了该新算法的有效性。     

求解CVRP问题的一种改进启发式蚁群算法

针对蚁群算法求解CVRP问题时收敛速度慢、求解质量不高的缺点,提出了一种改进启发式蚁群算法。该算法借鉴蚁群系统和基于排列的蚂蚁系统的优点设计信息素更新策略,既加强了对每次迭代最好解的利用,又避免了陷入局部最优;按一定比例使用基本方法和基于PFIH方法构造路径,扩大了算法的搜索空间;采用一种混合局部搜索算子,增强了算法局部寻优能力。实验结果表明,改进启发式蚁群算法可以大幅度减少车辆运行成本,具有较快的收敛速度。     

远程制导火箭子母弹对机场跑道毁伤研究

对机场跑道毁伤封锁是远程制导武器的重要任务,其毁伤判断的依据是最小升降窗口存在与否。采用Monte-Carlo方法对远程制导火箭子母弹打击机场跑道的炸点分布进行仿真模拟,提出了一种改进的搜索飞机起降最小升降窗口算法,并用该算法计算分析了远程制导火箭子母弹相关战技术因素对机场跑道毁伤的影响程度。计算结果表明改进算法计算封锁概率的值相对于区域搜索算法更加精确,研究结果对远程制导火箭子母弹的作战运用及进一步优化设计具有参考与实用价值。     

A learning tabu search for a truck allocation problem with linear and nonlinear cost components

The two‐level problem studied in this article consists of optimizing the refueling costs of a fleet of locomotives over a railway network. The goal consists of determining: (1) the number of refueling trucks contracted for each yard (truck assignment problem denoted TAP) and (2) the refueling plan of each locomotive (fuel distribution problem denoted FDP). As the FDP can be solved efficiently with existing methods, the focus is put on the TAP only. In a first version of the problem (denoted (P1)), various linear costs (e.g., fuel, fixed cost associated with each refueling, weekly operating costs of trucks) have to be minimized while satisfying a set of constraints (e.g., limited capacities of the locomotives and the trucks). In contrast with the existing literature on this problem, two types of nonlinear cost components will also be considered, based on the following ideas: (1) if several trucks from the same fuel supplier are contracted for the same yard, the supplier is likely to propose discounted prices for that yard (Problem (P2)); (2) if a train stops too often on its route, a penalty is incurred, which represents the dissatisfaction of the clients (Problem (P3)). Even if exact methods based on a mixed integer linear program formulation are available for (P1), they are not appropriate anymore to tackle (P2) and (P3). Various methods are proposed for the TAP: a descent local search, a tabu search, and a learning tabu search (LTS). The latter is a new type of local search algorithm. It involves a learning process relying on a trail system, and it can be applied to any combinatorial optimization problem. Results are reported and discussed for a large set of instances (for (P1), (P2), and (P3)), and show the good performance of LTS. © 2014 Wiley Periodicals, Inc. 62:32–45, 2015