基于CAIV/EA的武器装备经济可承受性优化设计

根据费用作为独立变量(CAIV)和渐进式采办(EA)策略的基本思想,研究了基于CAIV/EA的经济可承受性优化设计模型.根据多属性效用理论建立关于用户需求实现程度的综合效用目标函数;通过质量功能展开技术建立作战需求与设计参数的函数关系;依据"权衡空间"概念,确定约束条件.实践证明,该模型可以优化系统设计参数值,得出研制各批次的工作计划和资源分配规划,实现用户需求、资源能力和技术水平的平衡,从而提高装备经济可承受性.     

基于遗传算法的装备维修费用优化分配理论及优化方法研究

在美国工业界武器系统咨询委员会提出的郊能公式基础上 ,建立了武器装备效能与维修费用函数关系 ,为武器装备维修费用的优化分配提供了一个标准 .针对多种武器装备维修费用优化 ,建立了一个维修费用分配模型 ,用遗传算法进行了优化 ,并对优化过程进行了详细阐述 .在此基础上开发了装备维修经费优化与管理辅助决策支持系统 .     

综合考虑碳排放与运输费用的配送路径优化

针对日益严峻的环境问题,在传统的物流配送路径优化过程中,进行碳排放量计算,并转换成相应的经济效益,形成一个综合考虑碳排放和运输费用的配送路径优化模型并通过遗传算法进行求解.以厦门市某物流配送企业为例进行初步应用研究.结果表明:传统的配送路径安排中存在很大的碳排放改善空间;综合考虑碳排放和运输费用的车辆路径问题在有效实现绿色物流的同时能降低运输成本.     

产品开发费用估算模型研究

对于大型复杂项目 ,开发费用是总体费用的一个重要组成部分 .近年来 ,开发费用的估算日益受到重视 .在总结了现有的开发费用估算模型 ,并对这些模型进行了比较与讨论 .在对开发费用的 Weibul分布曲线进行分析和研究后 ,针对开发费用的 Weibul分布曲线拟合的局限 ,提出了开发费用的 Weibul分布累积曲线拟合模型 ,给出了模型使用的条什 .最后 ,给出了模型分析的一个实例     

求解GPRs条件下的项目最小费用问题

在项目管理中,最低成本是主要目标之一,也是求解诸如时间-费用权衡等相关问题时必备的起始点,通常只需令各工序选用费用最低的工期即可.但是当工序之间存在一般优先关系(简称GPRs)时,各工序选用费用最低的工期往往无法满足时间约束,使得项目不可行,因此需要考虑其它费用较高的工期.针对如何在满足GPRs条件下使项目成本最低,首先,通过分析GPRs网络的特点,建立了GPRs网络的最小费用模型;其次,对模型进行对偶变换,等效转化为最小费用流模型,其特点是,除了与起点或终点连接的弧,其余均没有容量限制.当前已有的算法能够有效求解该模型,并跟据其最优解可求得原问题的最优解.     

预防性维修条件下产品保修策略优化研究

为了降低保修费用,对产品的二维预防性保修策略进行优化.将预防性维修对产品的维修程度进一步细分,并用修复因子来区别不同的维修程度,提出两种程度预防性维修相结合的保修策略.针对提出的保修策略,建立保修费用模型,提出模型求解方法.通过算例,证明该保修策略的有效性,并对结果进行了分析.     

基于模糊理论的软件寿命周期费用评价模型

软件寿命周期费用评价模型涉及到软件开发、使用和维护过程中各种资源最有效利用的权衡分析。由于软件开发不是一门严谨的精确科学,往往存在大量具有不确定性的需求以及许多未知和不确定因素,所有这些都给软件寿命周期费用评价带来模糊效用。本文将模糊理论应用于软件寿命周期费用的评价,给出了从评价属性模糊值的确定、模糊评价模型的建立,到模型求解和最优方案选择的模糊评价方法,并通过对一个算例的分析,证明了该模型的可行性。     

基于综合效益的城市地下物流网络设计与优化方法

针对城市地下物流系统(Underground Logistics System,ULS)的特征,对一系列ULS网络节点选址与优化问题进行了建模分析.1)从解决城市交通拥堵的角度出发,探讨了物流地上地下分配的三种方案,并建立了地下货运OD评价模型.2)综合权衡货运量与货源距离,基于改进的模糊C均值聚类确定ULS—级节点的选址和辐射范围.3)对每个一级区域构建了ULS二级节点选址优化模型,通过人工免疫算法搜索最少覆盖节点群及节点的最优归属.4)建立多目标ULS网络规划模型,结合Prim算法与Dijkstra算法实现货物地下运输路径的最优选择,并采用栅格覆盖的思路在节点服务范围内对ULS网络进行费用优化.5)提出ULS网络效能评估指标,设置中心节点以提高系统运输效率和抗风险能力.     

多指标可加模型及在医疗费用预测中的应用

对医疗费用的建模分析与合理预测是医疗保险费用厘定的基础与根本.医疗费用中的高维附加信息在长期预测中具有重要作用.然而,传统的统计建模方法不适用于处理高维纵向数据下的医疗费用.本文提出部分线性多指标可加模型,对具有高维特征的纵向医疗费用数据进行拟合与预测,并且使用两种不同的降维估计方法进行模型估计,并将该模型应用于一组含...     

一种新的武器总体综合设计方法

对武器装备的作战效能、寿命周期费用、风险和研制周期的模型进行了研究,提出了综合运用这四维指标作为目标函数的武器总体综合设计的思路和数学模型,可以全面而系统地认识武器装备的发展问题,避免设计中由于只注重提高性能而引起的弊端.     

A new robust DEA model and super-efficiency measure

Applications of traditional data envelopments analysis (DEA) models require knowledge of crisp input and output data. However, the real-world problems often deal with imprecise or ambiguous data. In this paper, the problem of considering uncertainty in the equality constraints is analyzed and by using the equivalent form of CCR model, a suitable robust DEA model is derived in order to analyze the efficiency of decision-making units (DMUs) under the assumption of uncertainty in both input and output spaces. The new model based on the robust optimization approach is suggested. Using the proposed model, it is possible to evaluate the efficiency of the DMUs in the presence of uncertainty in a fewer steps compared to other models. In addition, using the new proposed robust DEA model and envelopment form of CCR model, two linear robust super-efficiency models for complete ranking of DMUs are proposed. Two different case studies of different contexts are taken as numerical examples in order to compare the proposed model with other approaches. The examples also illustrate various possible applications of new models.     

Location-aided routing with uncertainty in mobile ad hoc networks: A stochastic semidefinite programming approach

We study location-aided routing under mobility in wireless ad hoc networks. Due to node mobility, the network topology changes continuously, and clearly there exists an intricate tradeoff between the message passing overhead and the latency in the route discovery process. Aiming to obtain a clear understanding of this tradeoff, we use stochastic semidefinite programming (SSDP), a newly developed optimization model, to deal with the location uncertainty associated with node mobility. In particular, we model both the speed and the direction of node movement by random variables and construct random ellipses accordingly to better capture the location uncertainty and the heterogeneity across different nodes. Based on SSDP, we propose a stochastic location-aided routing (SLAR) strategy to optimize the tradeoff between the message passing overhead and the latency. Our results reveal that in general SLAR can significantly reduce the overall overhead than existing deterministic algorithms, simply because the location uncertainty in the routing problem is better captured by the SSDP model.     

串联式多级火箭成本问题初论

本文以火箭最大速度值的一般变化规律为基础, 改进了以前考虑火箭发射的成本问题的常用数学模型：最省的最省推进剂方案, 详细研究了各种情况下串联式多级火箭的成本问题,并以算例验证了所得的新成本计算模型的有效性.     

Minimum cost allocation of quality improvement targets under supplier process disruption

This paper presents a system cost model to assist a manufacturer in assessing the minimum cost allocations of quality improvement targets to suppliers. The model accounts for the effects of autonomous learning and induced learning on quality improvement, via variance reductions of supplier processes. The model further accounts for the effects of planned and unplanned disruptions in supplier production processes, where such gaps in production decreases the amount of autonomous learning while providing an opportunity for induced learning, thereby counteracting the effect of disruptions on process improvement. An optimization model is developed that obtains the quality improvement allocations that minimize system expected cost to both suppliers and manufacturer. The proposed models also account for both the uncertainty in the realized induced learning rate as well as uncertainty in the realized level of process disruptions. An example is used to demonstrate an implementation of the proposed models and to assess the sensitivity of the optimal target allocations to several model parameters.     

Solving Planning and Design Problems in the Process Industry Using Mixed Integer and Global Optimization

This contribution gives an overview on the state-of-the-art and recent advances in mixed integer optimization to solve planning and design problems in the process industry. In some case studies specific aspects are stressed and the typical difficulties of real world problems are addressed. Mixed integer linear optimization is widely used to solve supply chain planning problems. Some of the complicating features such as origin tracing and shelf life constraints are discussed in more detail. If properly done the planning models can also be used to do product and customer portfolio analysis. We also stress the importance of multi-criteria optimization and correct modeling for optimization under uncertainty. Stochastic programming for continuous LP problems is now part of most optimization packages, and there is encouraging progress in the field of stochastic MILP and robust MILP. Process and network design problems often lead to nonconvex mixed integer nonlinear programming models. If the time to compute the solution is not bounded, there are already a commercial solvers available which can compute the global optima of such problems within hours. If time is more restricted, then tailored solution techniques are required.     

Robust optimization and portfolio selection: The cost of robustness

Robust optimization is a tractable alternative to stochastic programming particularly suited for problems in which parameter values are unknown, variable and their distributions are uncertain. We evaluate the cost of robustness for the robust counterpart to the maximum return portfolio optimization problem. The uncertainty of asset returns is modelled by polyhedral uncertainty sets as opposed to the earlier proposed ellipsoidal sets. We derive the robust model from a min-regret perspective and examine the properties of robust models with respect to portfolio composition. We investigate the effect of different definitions of the bounds on the uncertainty sets and show that robust models yield well diversified portfolios, in terms of the number of assets and asset weights.     

Expectation-maximization for Bayes-adaptive POMDPs

Bayes-adaptive POMDPs (BAPOMDPs) are partially observable Markov decision problems in which uncertainty in the state-transition and observation-emission probabilities can be captured by a prior distribution over the model parameters. Existing approaches to solving BAPOMDPs rely on model and trajectory sampling to guide exploration and, because of the curse of dimensionality, do not scale well when the degree of model uncertainty is large. In this paper, we begin by presenting two expectation-maximization (EM) approaches to solving BAPOMPs via finite-state controller (FSC) optimization, which at their foundation are extensions of existing EM algorithms for BAMDPs to the more general BAPOMDP setting. The first is a sampling-based EM algorithm that optimizes over a finite number of models drawn from the BAPOMDP prior, and as such is only appropriate for smaller problems with limited model uncertainty; the second approach leverages variational Bayesian methods to ensure tractability without sampling, and is most appropriate for larger domains with greater model uncertainty. Our primary novel contribution is the derivation of the constrained VB-EM algorithm, which addresses an unfavourable preference that often arises towards a certain class of policies when applying the standard VB-EM algorithm. Through an empirical study we show that the sampling-based EM algorithm is competitive with more conventional sampling-based approaches in smaller domains, and that our novel constrained VB-EM algorithm can generate quality solutions in larger domains where sampling-based approaches are no longer viable.     

Sparse-Grid Surrogate Models for Uncertainty Analysis

The computation of fuzzy arithmetical solutions of problems involving uncertain, fuzzy-valued model parameters can be formulated as a nested sequence of optimization problems. In order to reduce the amount of model evaluations, which is often a limiting factor for the applicability of uncertainty analysis if the original model is evaluated, a surrogate-model approach based on sparse-grid interpolation is investigated. The optimization process is then performed on the basis of the surrogate representation, leading to a significant improvement with respect to the computation time. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

LMI Approach to Robust Model Predictive Control

This paper introduces a new approach to robust model predictive control (MPC) based on conservative approximations to semi-infinite optimization using linear matrix inequalities (LMIs). The method applies to problems with convex quadratic costs, linear and convex quadratic constraints, and linear predictive models with bounded uncertainty. If the MPC optimization problem is feasible at the initial control step (the first application of the MPC optimization), it is shown that the MPC optimization problems will be feasible at all future time steps and that the controlled system will be closed-loop stable. The method is illustrated with a solenoid control example. The authors thank the anonymous reviewers for suggestions that improved the presentation of this work. The work was supported in part by the EPRI/DoD Complex Interactive Networks/Systems Initiative under Contract EPRI-W08333-05 and by the US Army Research Office Contract DAAD19-01-1-0485.     

A robust data envelopment analysis model with different scenarios

Input and output data, under uncertainty, must be taken into account as an essential part of data envelopment analysis (DEA) models in practice. Many researchers have dealt with this kind of problem using fuzzy approaches, DEA models with interval data or probabilistic models. This paper presents an approach to scenario-based robust optimization for conventional DEA models. To consider the uncertainty in DEA models, different scenarios are formulated with a specified probability for input and output data instead of using point estimates. The robust DEA model proposed is aimed at ranking decision-making units (DMUs) based on their sensitivity analysis within the given set of scenarios, considering both feasibility and optimality factors in the objective function. The model is based on the technique proposed by Mulvey et al. (1995) for solving stochastic optimization problems. The effect of DMUs on the product possibility set is calculated using the Monte Carlo method in order to extract weights for feasibility and optimality factors in the goal programming model. The approach proposed is illustrated and verified by a case study of an engineering company.