Limiting behavior of the stochastic sequential assignment problem

The stochastic sequential assignment problem (SSAP) considers how to allocate available distinct workers to sequentially arriving tasks with stochastic parameters such that the expected total reward obtained from the sequential assignments is maximized. Implementing the optimal assignment policy for the SSAP involves calculating a new set of breakpoints upon the arrival of each task (i.e., for every time period), which is impractical for large‐scale problems. This article studies two problems that are concerned with obtaining stationary policies, which achieve the optimal expected reward per task as the number of tasks approaches infinity. The first problem considers independent and identically distributed (IID) tasks with a known distribution function, whereas in the second problem tasks are derived from r different unobservable distributions governed by an ergodic Markov chain. The convergence rate of the expected reward per task to the optimal value is also obtained for both problems. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

匈牙利法的末段两层火力反TBM目标分配优化

介绍了匈牙利法解决Assignment Problem的基本原理,对匈牙利法解决指派问题的解题步骤和特殊情况进行了说明。在给出末段两层火力防御TBM的目标分配原则的基础上,阐述了相关概念,改进了匈牙利法运用方式,并建立了目标分配模型。最后通过实例的解算,证明了该方法应用于末段两层火力防御TBM目标分配中的可行性,为末段协同反TBM的目标分配提供了一种有效的算法。     

The bounded interval generalized assignment model

The bounded interval generalized assignment model is a “many-for-one” assignment model. Each task must be assigned to exactly one agent; however, each agent can be assigned multiple tasks as long as the agent resource consumed by performing the assigned tasks falls within a specified interval. The bounded interval generalized assignment model is formulated, and an algorithm for its solution is developed. Algorithms for the bounded interval versions of the semiassignment model and sources-to-uses transportation model are also discussed.     

火力分配多目标优化的IBACO算法

应用蚁群优化算法(Ant Colony Optimization)求解多目标优化问题已经引起广泛关注,多目标火力分配问题的目标是求出一个合适的武器目标分配方案,使满足决策需要。建立了多目标火力分配的数学模型,提出一种基于指标的蚁群优化算法Indicator-Based Ant Colony Optimization),给出了算法的具体步骤。IBACO的核心思想是利用二元性能指标来引导人工蚂蚁进行搜索,由于该算法中的信息素是根据指标的值来更新的,通过奖励信息素可以强化最优解。仿真实验证明了该算法的有效性,在解决火力分配问题上,所提算法和蚁群优化算法相比具有较好的收敛性。     

舰艇编队作战任务优化分配

从舰艇编队的实际作战需求出发,将作战使命分解为多个作战任务,然后分配给相关平台。首先在每个平台只能承担一项任务的前提下,构建了编队作战任务分配的基本模型。在此基础上研究了两平台协同执行任务时效能互补、单平台在同时执行多项任务时武器装备相互干扰等情况,进一步构建了考虑平台间协同效益和平台执行多任务的作战任务分配模型。应用改进的遗传算法给出了编队作战任务分配方案优化选择的具体方法步骤,最后针对一个典型案例进行了仿真计算与分析,验证了三种模型的合理性。     

抢险救灾非战争军事行动中应急资源调度网络的优化方法

抢险救灾非战争军事行动包括道路抢修和物资运输等任务,而这两类任务在灾后应急资源调度中存在关联性的影响,且面临路网结构可变及需求随机模糊等挑战,对此,提出了一种非确定性应急资源调度网络双层规划模型,设计了基于蒙特卡洛方法与遗传算法耦合的智能启发式求解策略.通过对典型情境下应急资源调度案例进行分析建模和数值求解,说明了该模型和算法的合理性和有效性.     

基于模糊匈牙利算法的炮兵火力单位分配问题

发挥诸火力单位的整体协调优势,寻求在给定约束条件下总的射击效果最好的分配方案,是火力单位最优分配的基本任务.匈牙利算法是求解传统的指派问题的一种较好的方法,运用模糊匈牙利算法在决策过程中将主观因素与客观因素有机地结合起来,解决火力单位分配方案决策中多指标指派问题,从而可以有效地解决炮兵火力单位分配最优化问题.     

货郎担问题新算法

将指派问题的匈牙利解法用于货郎担问题,通过恰当地添加大正数构造效率矩阵,得到了计算货郎担问题较快的算法。文中给出的2个例子具体地说明了算法实施过程,该算法具有一定的实用性。     

A generalized assignment model for dynamic supply chain capacity planning

We consider a generalization of the well‐known generalized assignment problem (GAP) over discrete time periods encompassed within a finite planning horizon. The resulting model, MultiGAP, addresses the assignment of tasks to agents within each time period, with the attendant single‐period assignment costs and agent‐capacity constraint requirements, in conjunction with transition costs arising between any two consecutive periods in which a task is reassigned to a different agent. As is the case for its single‐period antecedent, MultiGAP offers a robust tool for modeling a wide range of capacity planning problems occurring within supply chain management. We provide two formulations for MultiGAP and establish that the second (alternative) formulation provides a tighter bound. We define a Lagrangian relaxation‐based heuristic as well as a branch‐and‐bound algorithm for MultiGAP. Computational experience with the heuristic and branch‐and‐bound algorithm on over 2500 test problems is reported. The Lagrangian heuristic consistently generates high‐quality and in many cases near‐optimal solutions. The branch‐and‐bound algorithm is also seen to constitute an effective means for solving to optimality MultiGAP problems of reasonable size. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012     

一种基于博弈论的无线网状网络路由与信道分配联合优化算法

无线网络中的路由与信道分配可极大地影响网络的性能.为了解决无线网状网络中的路由与信道分配问题,提出并研究了一种称为CRAG(基于博弈论的无线网状网络路由与信道分配联合优化)的方法.CRAG采用协同博弈的方式将网络中的每个节点模型化为一个弈者,每个弈者的策略为与其相关的路由与信道分配方案,收益函数为给定流量需求矩阵下的成功传输流量.弈者通过协同博弈来优化收益函数以最大化网络的吞吐量.基于NS3的仿真结果表明,CRAG在收敛性、时延、丢包率和吞吐量方面优于其他当前的算法,从而证明了协同博弈的方法可以用于无线网状网络的路由与信道分配联合优化,并有效地改进网络性能.     

Stochastic call center staffing with uncertain arrival,service and abandonment rates: A Bayesian perspective

In this article, we introduce staffing strategies for the Erlang‐A queuing system in call center operations with uncertain arrival, service, and abandonment rates. In doing so, we model the system rates using gamma distributions that create randomness in operating characteristics used in the optimization formulation. We divide the day into discrete time intervals where a simulation based stochastic programming method is used to determine staffing levels. More specifically, we develop a model to select the optimal number of agents required for a given time interval by minimizing an expected cost function, which consists of agent and abandonment (opportunity) costs, while considering the service quality requirements such as the delay probability. The objective function as well as the constraints in our formulation are random variables. The novelty of our approach is to introduce a solution method for the staffing of an operation where all three system rates (arrival, service, and abandonment) are random variables. We illustrate the use of the proposed model using both real and simulated call center data. In addition, we provide solution comparisons across different formulations, consider a dynamic extension, and discuss sensitivity implications of changing constraint upper bounds as well as prior hyper‐parameters. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 460–478, 2016     

A markov chain version of the secretary problem

This paper deals with the Secretary Problem where n secretaries are interviewed sequentially and the best k must be hired. The values of the secretaries are observed as they are interviewed, but beforehand only the distributions of these values are known. Furthermore, the distributions of two successive secretaries' values are governed by a Markov chain. Optimal hiring policies for finite n and limiting optimal policies as k and n approach infinity are obtained.     

Minimizing costs of sequential and parallel personnel testing programs

We consider groups of tests for personnel selection purposes in which each test has a known a priori probability of being failed, such failure resulting in outright rejection and termination of testing. Each test has a fixed cost and given duration. We consider the minimization of the total expected cost due to both the fixed costs and the delay costs when the tests may be conducted sequentially or in parallel. In the latter situation, a heuristic algorithm is proposed and illustrated.     

态势估计中一种基于贝叶斯估计的统计时间推理方法

统计时间推理是态势估计中的一个重要组成部分。Ｋｉｒｉｌｏｖ的基于极大似然估计（Ｍａｘｉ－ｍｕｍＬｉｋｅｌｉｈｏｏｄＥｓｔｉｍａｔｉｏｎ,ＭＬＥ）的推理方法将未知时间变量看作常数,估计方差较大。文中建立的已知时间信息和未知时间变量之间的关系模型,将未知时间变量扩展为随机变量,将贝叶斯估计（ＢａｙｅｓＥｓｔｉｍａｔｉｏｎ,ＢＥ）引入时间推理。经过对两种推理算法的性能进行分析和比较,发现在一定范围内,基于ＢＥ的方法性能优于基于ＭＬＥ的方法。     

Designing an optimal life test with type I censoring

The present study considers the design of an optimal life test plan with Type I censoring in the case where only one testing machine is available and a machine tests items sequentially as seen in fatigue life tests of metallic materials. Under the condition that the life test is conducted in a fixed censoring time and with a fixed number of test items, a method for determining the censoring time and the number of test items is proposed from the Bayesian point of view. The proposed method is also applied to the Weibull life distribution with a known shape parameter.     

基于层次化的SVDDBNs的多机动平台目标分配方式

针对天基机动平台集群作战时的目标分配问题。提出一种基于层次化思想的变结构离散动态贝叶斯网络(SVDDBNs)的平台-目标分配方式。首先提出了目标分配算法。在平台-目标分配算法中所需的攻击优势指数的计算上,提出利用SVDDBNs进行攻击优势指数的计算。由于SVDDBNs具有可以融合不同类型信息进行推理决策的特点,因此给出的目标分配算法考虑的因素更加全面,计算结果具有更高的置信度。最后的仿真算例证明了此方法的正确性和有效性。     

Minimizing makespan in a multimode multiprocessor shop scheduling problem

We study the problem of multimode scheduling tasks on dedicated processors, with the objective of minimizing the maximum completion time. Each task can be undertaken in one among a set of predefined alternative modes, where each mode specifies a required set of dedicated processors and a processing time. At any time each processor can be used by a single task at most. General precedence constraints exist among tasks, and task preemption is not allowed. The problem consists of assigning a mode and a starting time to each task, respecting processor and precedence constraints, to minimize the time required to complete all tasks. The problem is NP-hard in several particular cases. In previous works, we studied algorithms in which a solution was obtained by means of an iterative procedure that combines mode assignment and sequencing phases separately. In this paper, we present some new heuristics where the decision on the mode assignment is taken on the basis of a partial schedule. Then, for each task, the mode selection and the starting time are chosen simultaneously considering the current processor usage. Different lower bounds are derived from a mathematical formulation of the problem and from a graph representation of a particular relaxed version of the problem. Heuristic solutions and lower bounds are evaluated on randomly generated test problems. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 893–911, 1999     

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     

一种面向多核处理器的高效并行PCA-SIFT算法

提出一种面向多核处理器的并行PCA-SIFT算法,采用数据级并行方法实现并行的特征提取和特征点匹配,将计算任务分配到各个DSP核并行处理,充分开发多核处理器的多级并行性.实验结果表明,并行PCA-SIFT算法对各种不同图像形变的图像具有良好的适应性,具有接近串行PCA-SIFT算法的图像匹配能力,平均加速比达3.12.