Exponential size neighborhoods for makespan minimization scheduling

We investigate the quality of local search heuristics for the scheduling problem of minimizing the makespan on identical parallel machines. We study exponential size neighborhoods (whose size grows exponentially with the input length) that can be searched in polynomial time, and we derive worst‐case approximation guarantees for the local optima of such neighborhoods. The so‐called split neighborhood splits a feasible schedule into two layers, and then recombines the two layers by finding a perfect matching. We show that the makespan of every local optimum for split is at most a factor of 2 away from the globally optimal makespan. We then combine the split neighborhood with two neighborhoods from the literature. The combination of split with the jump neighborhood only marginally improves the approximation guarantee, whereas the combination with the lexicographic‐jump neighborhood decreases the approximation guarantee from 2 to 3/2. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

On the optimal detection of an underwater intruder in a channel using unmanned underwater vehicles

Given a number of patrollers that are required to detect an intruder in a channel, the channel patrol problem consists of determining the periodic trajectories that the patrollers must trace out so as to maximized the probability of detection of the intruder. We formulate this problem as an optimal control problem. We assume that the patrollers' sensors are imperfect and that their motions are subject to turn‐rate constraints, and that the intruder travels straight down a channel with constant speed. Using discretization of time and space, we approximate the optimal control problem with a large‐scale nonlinear programming problem which we solve to obtain an approximately stationary solution and a corresponding optimized trajectory for each patroller. In numerical tests for one, two, and three underwater patrollers, an underwater intruder, different trajectory constraints, several intruder speeds and other specific parameter choices, we obtain new insight—not easily obtained using simply geometric calculations—into efficient patrol trajectory design under certain conditions for multiple patrollers in a narrow channel where interaction between the patrollers is unavoidable due to their limited turn rate.© 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Inventory management with advance capacity information

An important aspect of supply chain management is dealing with demand and supply uncertainty. The uncertainty of future supply can be reduced if a company is able to obtain advance capacity information (ACI) about future supply/production capacity availability from its supplier. We address a periodic‐review inventory system under stochastic demand and stochastic limited supply, for which ACI is available. We show that the optimal ordering policy is a state‐dependent base‐stock policy characterized by a base‐stock level that is a function of ACI. We establish a link with inventory models that use advance demand information (ADI) by developing a capacitated inventory system with ADI, and we show that equivalence can only be set under a very specific and restrictive assumption, implying that ADI insights will not necessarily hold in the ACI environment. Our numerical results reveal several managerial insights. In particular, we show that ACI is most beneficial when there is sufficient flexibility to react to anticipated demand and supply capacity mismatches. Further, most of the benefits can be achieved with only limited future visibility. We also show that the system parameters affecting the value of ACI interact in a complex way and therefore need to be considered in an integrated manner. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

信息心理对抗条件下联合作战能力分析

信息化条件下作战,部队的作战能力表现为包括火力、防护力、保障力、信息力等多种因素的联合作战能力,作战双方的对抗表现为兵力对抗,心理对抗,信息对抗等多种复杂的形式.在联合作战能力量化分析基础上,分析了信息、心理和战斗力相互作用机制,得出了战斗对抗、信息对抗、心理对抗3个反馈回路环,并据此建立了系统动力学模型,从定量的角度...     

复杂环境下基于多目标粒子群的DWA路径规划算法

针对机器人在障碍物分布密集的复杂环境中运行时,动态窗口法(dynamic window approach,DWA)易出现避障失败或规划不合理的情况,提出一种基于多目标粒子群优化算法(multi-objective particle swarm optimization,MOPSO)的改进DWA规划算法。在建立多障碍物环境覆盖模型的基础上,提出一种障碍物密集度的判断方法;优化DWA算法中的子评价函数;利用改进的MOPSO算法实现DWA权重系数的动态调整,将权重系数的自适应变化问题转化为多目标优化问题;根据路径规划的要求将安全距离和速度作为优化目标,并使用改进的MOPSO算法对相应的多目标优化模型进行优化求解。仿真结果表明,该算法使机器人有效地通过障碍〖BHDWG8,WK10YQ,DK1*2,WK1*2D〗〖XCLXY.TIF;%129%129〗听语音 聊科研与作者互动 物密集区的同时兼顾了运行的安全性和速度,具有更好的路径规划效果。     

自组织特征映射在柴油机振动信号模式分类中的应用

通过分析柴油机在磨合期、不同摩托小时和拉缸等典型状态下的振动信号样本,计算出各类样本在幅域、时域和频域的特征参量,按照类别可分离性判据进行特征选择,寻找出能够代表发动机不同状态的有效特征参数,同时降低特征向量空间的维数,最后利用自组织特征映射神经网络(Self-Organizing Feature Map)进行发动机不同状态的分类.分析结果表明,SOFM能够对各类模式进行有效的分类,准确率达到92%以上.     

On optimality of one‐bug‐look‐ahead policies for a software testing model

The optimality of the One‐Bug‐Look‐Ahead (OLA) software release policy proposed by Morali and Soyer ( 15 ) is re‐examined in this paper. A counterexample is constructed to show that OLA is not optimal in general. The optimal stopping approach is then called upon to prove that OLA possesses weaker sense of optimality under conditional monotonicity and the strong sense of optimality holds under a more restrictive sample‐wise monotonicity condition. The NTDS data are analyzed for illustration, and OLA is shown to be robust with respect to model parameters. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007.     

Cyclic preference scheduling for nurses using branch and price

This paper presents a new methodology to solve the cyclic preference scheduling problem for hourly workers. The focus is on nurse rostering but is applicable to any organization in which the midterm scheduling decision must take into account a complex of legal, institutional, and preferential constraints. The objective is to strike a balance between satisfying individual preferences and minimizing personnel costs. The common practice is to consider each planning period independently and to generate new rosters at the beginning of each. To reduce some of the instability in the process, there is a growing trend toward cyclic schedules, which are easier to manage and are generally perceived to be more equitable. To address this problem, a new integer programming model is presented that combines the elements of both cyclic and preference scheduling. To find solutions, a branch‐and‐price algorithm is developed that makes use of several branching rules and an extremely effective rounding heuristic. A unique feature of the formulation is that the master problem contains integer rather than binary variables. Computational results are reported for problem instances with up to 200 nurses. Most were solved within 10 minutes and many within 3 minutes when a double aggregation approach was applicable. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007.     

Parallel machine scheduling with job assignment restrictions

In the classical multiprocessor scheduling problem independent jobs must be assigned to parallel, identical machines with the objective of minimizing the makespan. This article explores the effect of assignment restrictions on the jobs for multiprocessor scheduling problems. This means that each job can only be processed on a specific subset of the machines. Particular attention is given to the case of processing times restricted to one of two values, 1 and λ, differing by at most 2. A matching based polynomial time ε‐approximation algorithm is developed that has a performance ratio tending to . This algorithm is shown to have the best possible performance, tending to 3/2, for processing times 1 and 2. For the special case of nested processing sets, i.e., when the sets of machines upon which individual jobs may be assigned are non‐overlapping, the behavior of list scheduling algorithms is explored. Finally, for assignment restrictions determined by just one characteristic of the machines, such as disc storage or memory constraint in the case of high performance computing, we contribute an algorithm that provides a 3/2 worst case bound and runs in time linear in the number of jobs. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Capacity allocation to sales agents in a decentralized logistics network

Many logistics systems operate in a decentralized way, while most optimization models assume a centralized planner. One example of a decentralized system is in some sea cargo companies: sales agents, who share ship capacity on a network, independently accept cargo from their location and contribute to the revenue of the system. The central headquarters does not directly control the agents' decisions but can influence them through system design and incentives. In this paper, we model the firm's problem to determine the best capacity allocation to the agents such that system revenue is maximized. In the special case of a single‐route, we formulate the problem as a mixed integer program incorporating the optimal agent behavior. For the NP‐hard multiple‐route case, we propose several heuristics for the problem. Computational experiments show that the decentralized system generally performs worse when network capacity is tight and that the heuristics perform reasonably well. We show that the decentralized system may perform arbitrarily worse than the centralized system when the number of locations goes to infinity, although the choice of sales incentive impacts the performance. We develop an upper bound for the decentralized system, where the bound gives insight on the performance of the heuristics in large systems. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007