车载光学测量设备任务调度问题建模与求解

为提高观测方案效费比、改进探测能力,探索了一类车载光学测量设备的观测任务调度问题,并给出了解决方案。将观测任务调度问题建模为一个寻找最优观测方案的数学问题,结合设备的性能特点,给出了观测方案的数学描述,梳理了观测方案应满足的约束,提出了评价观测方案质量的指标,进而利用多属性决策方法来计算不同方案的总体效能,并排序获得最优方案。仿真算例验证了方法的有效性,相关研究成果对车载光学测量设备的运用实践具有一定的参考价值。     

Total optimality of incrementally optimal allocations

This paper considers the problem of finding optimal solutions to a class of separable constrained extremal problems involving nonlinear functionals. The results are proved for rather general situations, but they may be easily stated for the case of search for a stationary object whose a priori location distribution is given by a density function on R, a subset of Euclidean n-space. The functional to be optimized in this case is the probability of detection and the constraint is on the amount of effort to be used Suppose that a search of the above type is conducted in such a manner as to produce the maximum increase in probability of detection for each increment of effort added to the search. Then under very weak assumptions, it is proven that this search will produce an optimal allocation of the total effort involved. Under some additional assumptions, it is shown that any amount of search effort may be allocated in an optimal fashion.     

Optimal search using two nonconcurrent sensors

Search for a stationary target is considered for a situation in which two sensors are available, but cannot be used simultaneously. The cost (in time) of switching from one sensor to the other is ignored, and each sensor is assumed to have perfect discrimination. For a specified class of searches an optimal allocation of search effort is obtained. In the case of a circular normal prior target location distribution, an example is presented in which one sensor is assumed to have a fixed sweep width and the other a stochastic sweep width. An optimal plan is found for this example. This plan produces an allocation of search effort which is expended in a disk by one sensor and in a bounding annulus by the other.     

Planning accelerated life tests for censored two‐parameter exponential distributions

We consider optimal test plans involving life distributions with failure‐free life, i.e., where there is an unknown threshold parameter below which no failure will occur. These distributions do not satisfy the regularity conditions and thus the usual approach of using the Fisher information matrix to obtain an optimal accelerated life testing (ALT) plan cannot be applied. In this paper, we assume that lifetime follows a two‐parameter exponential distribution and the stress‐life relationship is given by the inverse power law model. Near‐optimal test plans for constant‐stress ALT under both failure‐censoring and time‐censoring are obtained. We first obtain unbiased estimates for the parameters and give the approximate variance of these estimates for both failure‐censored and time‐censored data. Using these results, the variance for the approximate unbiased estimate of a percentile at a design stress is computed and then minimized to produce the near‐optimal plan. Finally, a numerical example is presented together with simulation results to study the accuracy of the approximate variance given by the proposed plan and show that it outperforms the equal‐allocation plan. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 169–186, 1999     

Competition under time‐varying demands and dynamic lot sizing costs

We develop a competitive pricing model which combines the complexity of time‐varying demand and cost functions and that of scale economies arising from dynamic lot sizing costs. Each firm can replenish inventory in each of the T periods into which the planning horizon is partitioned. Fixed as well as variable procurement costs are incurred for each procurement order, along with inventory carrying costs. Each firm adopts, at the beginning of the planning horizon, a (single) price to be employed throughout the horizon. On the basis of each period's system of demand equations, these prices determine a time series of demands for each firm, which needs to service them with an optimal corresponding dynamic lot sizing plan. We establish the existence of a price equilibrium and associated optimal dynamic lotsizing plans, under mild conditions. We also design efficient procedures to compute the equilibrium prices and dynamic lotsizing plans.© 2008 Wiley Periodicals, Inc. Naval Research Logistics 2009     

A parallel machine replacement model

This paper considers a finite horizon parallel machine replacement problem where a fixed number of machines is in operation at all times. The operating cost for a machine goes up as the machine gets older. An older machine may have to be replaced by a new one when its operating cost becomes too high. There is a fixed order cost associated with the purchase of new machines. Machine purchase prices and salvage values may depend on the period in which they were purchased. The objective is to find a replacement plan that minimizes the total discounted cost over the problem horizon. We believe that the costs in our model are more commonly observed in practice than those previously used in the literature. The paper develops properties of optimal solutions and an efficient forward‐time algorithm to find an optimal replacement plan. A dominance property is developed that further limits the options to be considered, and a simple forecast horizon result is also presented. Future research possibilities are mentioned. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 275–287, 2002; Published online in Wiley InterScience (http://www.interscience.wiley.com). DOI 10.1002/nav.10012     

基于可拓方法的C3I作战方案生成与评价

作战指挥决策支持系统是指挥自动化系统的重要组成部分,作战方案生成与评价是作战指挥决策支持系统的基本功能.根据作战指挥决策支持系统特点将可拓方法应用于作战方案生成与评价,并分析了其优势所在.结合空战案例运用发散树方法进行作战方案集的开拓,并利用优度评价方法进行作战方案的评价,从而得到最佳作战方案.     

离散搜索力的最优配置模型及增量搜索计划

目前,离散搜索正越来越普遍地应用于各种搜索实践。为了优化搜索过程,提高离散搜索的效率,应用最优搜索理论,导出了待搜目标服从均匀分布、正态分布时离散搜索力的最优配置模型、目标的踪迹预测方法和最优增量搜索计划的求法,并通过实例作了演示,上述结论和方法为离散搜索力如何实施最优搜索提供了一定的理论依据。     

Design of accelerated life testing plans under multiple stresses

Accelerated life testing (ALT) using multiple stresses is commonly used in practice to resemble the operating stresses at normal operating conditions and obtain failure observations in a much shorter time. However, to date, there is little research into the theory of planning ALT for reliability estimation with multiple stresses. ALT with multiple stresses can result in a large number of stress‐level combinations which presents a challenge for implementation. In this article, we propose an approach for the design of ALT plans with multiple stresses and formulate multistress test plans based on different objectives and practical constraints. We develop a simulated annealing algorithm to efficiently determine the testing plan parameters. We demonstrate the proposed method with examples based on an actual test conducted using three stress types. The obtained optimal test plans are compared with those based on fractional factorial design. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 468–478, 2013     

成败型产品的Bayes鉴定试验方案研究

利用可靠性增长模型给出了成败型产品鉴定试验的一种Bayes方法.提出了Bayes鉴定试验的最大后验风险准则,利用这种准则制定的鉴定试验方案综合了产品研制过程中的先验信息.在确保产品质量的前提下,与传统的鉴定试验方案相比,将大大节省试验时间.     

Dynamic regulation on innovation and adoption of green technology with information asymmetry

This study investigates a regulator's dynamic policy to motivate firms' research on and adoption of green technology. In the proposed model, a firm makes unobservable efforts and can hide the technology's arrival from the regulator to avoid adoption costs. We find that the optimal policy follows a simple structure and induces part-time efforts, rather than the maximal effort reported in previous studies. In particular, the regulator should offer no subsidy before the arrival of a technology report, provide a one-time subsidy contingent upon that report, and always set a termination deadline. At the deadline, the firm is forced to select an external option that is associated with social costs. The optimal report-based subsidy decreases with time. Under the optimal policy, the firm works until an effort deadline, makes no effort thereafter, and reports the technology as soon as it arrives. This study also characterizes the necessary and sufficient conditions under which the optimal policy reduces to one that leads, in terms of throughout time, to effort that is maximal or minimal. Our results indicate that policymakers should implement a policy that compensates firms more in the present and less in the future.     

Optimal control policies for a periodic review inventory system with emergency orders

We describe a periodic review inventory system where emergency orders, which have a shorter supply lead time but are subject to higher ordering cost compared to regular orders, can be placed on a continuous basis. We consider the periodic review system in which the order cycles are relatively long so that they are possibly larger than the supply lead times. Study of such systems is important since they are often found in practice. We assume that the difference between the regular and emergency supply lead times is less than the order-cycle length. We develop a dynamic programming model and derive a stopping rule to end the computation and obtain optimal operation parameters. Computational results are included that support the contention that easily implemented policies can be computed with reasonable effort. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 187–204, 1998     

Allocation of quality improvement targets based on investments in learning

Purchased materials often account for more than 50% of a manufacturer's product nonconformance cost. A common strategy for reducing such costs is to allocate periodic quality improvement targets to suppliers of such materials. Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across‐the‐board percentage improvement for all suppliers, which, however, may not be the most effective or efficient approach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for suppliers, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an independent supplier's process. We assume that a manufacturer's quality improvement is a result of reductions in supplier process variances, obtained through learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to assess the sensitivity of investment in learning on the allocation of quality improvement targets. Solutions for determining optimal learning rates, and concomitant quality improvement targets are derived for each learning investment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computational study is conducted to investigate the differences between optimal variance allocations and a fixed percentage allocation. These differences are examined with respect to (i) variance improvement targets and (ii) total expected cost. For certain types of learning investment models, the results suggest that orders of magnitude differences in variance allocations and expected total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learning investments characterized by a quadratic function, there is surprisingly close agreement with an “across‐the‐board” allocation of 20% quality improvement targets. © John Wiley & Sons, Inc. Naval Research Logistics 48: 684–709, 2001     

Integrated production scheduling and preventive maintenance planning for a single machine under a cumulative damage failure process

This paper finds the optimal integrated production schedule and preventive maintenance plan for a single machine exposed under a cumulative damage process, and investigates how the optimal preventive maintenance plan interacts with the optimal production schedule. The goal is to minimize the total tardiness. The optimal policy possesses the following properties: Under arbitrary maintenance plan when jobs have common processing time, and different due dates, the optimal production schedule is to order the jobs by earliest due date first rule; and when jobs have common due date and different processing times, the optimal production schedule is shortest processing time first. The optimal maintenance plan is of control limit type under any arbitrary production schedule when machine is exposed under a cumulative damage failure process. Numerical studies on the optimal maintenance control limit of the maintenance plan indicate that as the number of jobs to be scheduled increases, the effect of jobs due dates on the optimal maintenance control limit diminishes. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

A comparison of several attribute sampling plans

The purpose of this research is to examine several types of procedures for attribute sampling inspection-the widely used Military Standard 105D plans [8], the lesser known Double Zero plans as developed by Ellis [4] and the Narrow Limit gaging plans of Ott and Mundel [9]. Each of the procedures is described with an effort made to illuminate their more subtle features. Then the plans are compared, whence it is revealed that (i) Narrow Limit gaging plans have a serious weakness in comparison to the others and (ii) Double Zero plans tend to be essentially conservative, but that sufficiently tight Military Standard 105D plans can be selected to achieve comparable performance in all ways.     

Exact analysis of (R,s, S) inventory control systems with lost sales and zero lead time

We study an (R, s, S) inventory control policy with stochastic demand, lost sales, zero lead‐time and a target service level to be satisfied. The system is modeled as a discrete time Markov chain for which we present a novel approach to derive exact closed‐form solutions for the limiting distribution of the on‐hand inventory level at the end of a review period, given the reorder level (s) and order‐up‐to level (S). We then establish a relationship between the limiting distributions for adjacent values of the reorder point that is used in an efficient recursive algorithm to determine the optimal parameter values of the (R, s, S) replenishment policy. The algorithm is easy to implement and entails less effort than solving the steady‐state equations for the corresponding Markov model. Point‐of‐use hospital inventory systems share the essential characteristics of the inventory system we model, and a case study using real data from such a system shows that with our approach, optimal policies with significant savings in inventory management effort are easily obtained for a large family of items.     

Toward a determination of optimal multilevel sampling plans

Traditionally continuous sampling plans have been evaluated according to relatively few criteria. These typically include the OC curve (on which AQL systems are based), the ASN, and the AOQ curve. These characteristics are all calculated under the assumption that the process is “in control” so that mathematically they are derived as long-term averages. Thus, any two plans which (long term) spend the same proportion of time on each type of sampling inspection will be identical relative to these criteria. This is true whether sampling from lots or doing unit-by-unit inspection. The goal is to first establish desirable additional criteria and then to develop methods to determine which procedure (of those which satisfy the standard criteria) is optimal relative to the new criteria. To be considered will be measures of a plan's ability to detect a sudden drop in quality (such as ARL).     

Efficient distributions of arms‐control inspection effort

A rule that constrains decision‐makers is enforced by an inspector who is supplied with a fixed level of inspection resources—inspection personnel, equipment, or time. How should the inspector distribute its inspection resources over several independent inspectees? What minimum level of resources is required to deter all violations? Optimal enforcement problems occur in many contexts; the motivating application for this study is the role of the International Atomic Energy Agency in support of the Treaty on the Non‐Proliferation of Nuclear Weapons. Using game‐theoretic models, the resource level adequate for deterrence is characterized in a two‐inspectee problem with inspections that are imperfect in the sense that violations can be missed. Detection functions, or probabilities of detecting a violation, are assumed to be increasing in inspection resources, permitting optimal allocations over inspectees to be described both in general and in special cases. When detection functions are convex, inspection effort should be concentrated on one inspectee chosen at random, but when they are concave it should be spread deterministicly over the inspectees. Our analysis provides guidance for the design of arms‐control verification operations, and implies that a priori constraints on the distribution of inspection effort can result in significant inefficiencies. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

指数型产品可靠性验收试验的一种贝叶斯方法

采用贝叶斯方法 ,利用大量的历史数据和工程技术人员的丰富经验确定先验分布 ,制定出验收试验的一种新方案 .按照这一方案 ,在确保产品质量的前提下 ,可大大节省试验时间 ,从而得到十分可观的经济效益 .     

On the use of standard tables to obtain Dodge-Romig LTPD sampling inspection plans

Procedures are described which yield single and double sample Dodge-Romig [1] lot tolerance percent defective (LTPD) rectifying inspection plans. For the determination of such plans only a desk calculator and standard tables of the discrete probability distributions are required. Some advantages gained by using these procedures rather than the Dodge-Romig table include: (a) The Consumer's Risk is not limited to 0.10. (b) More choices of LTPD are available. (c) Smaller average total inspection is achieved by using a plan designed for specific “process average” and lot size rather than a compromise plan designed to cover intervals on these two parameters.