天然肠衣搭配的优化模型

首先,采用假设检验法,建立双目标整数规划模型;其次,通过引入权重,将双目标整数规划转换为单目标整数规划;最后利用Lingo软件进行求解,对给定的肠衣原料进行搭配,得到总捆数为191捆,剩余肠衣25根。     

Optimum allocation in multivariate stratified random sampling: stochastic matrix mathematical programming

The allocation problem for multivariate stratified random sampling as a problem of stochastic matrix integer mathematical programming is considered, minimizing the estimated covariance matrix of estimated means subject to fixed cost or fixed total sample size. With these aims the asymptotic normality of sample covariance matrices for each strata is established. Some alternative approaches are suggested for its solution. An example is solved by applying the proposed techniques.     

Stochastic DEA Models With Different Types of Input-Output Disturbances

This paper presents stochasticmodels in data envelopment analysis (DEA) for the possibilityof variations in inputs and outputs. Efficiency measure of adecision making unit (DMU) is defined via joint probabilisticcomparisons of inputs and outputs with other DMUs and can becharacterized by solving a chance constrained programming problem.By utilizing the theory of chance constrained programming, deterministicequivalents are obtained for both situations of multivariatesymmetric random disturbances and a single random factor in theproduction relationships. The linear deterministic equivalentand its dual form are obtained via the goal programming theoryunder the assumption of the single random factor. An analysisof stochastic variable returns to scale is developed using theidea of stochastic supporting hyperplanes. The relationshipsof our stochastic DEA models with some conventional DEA modelsare also discussed.     

数学规划在测绘工作中的应用

本文在介绍数学规划的相关方法包括线性规划、灵敏度分析、整数规划的基础上,从一个例子出发,讨论了在测绘生产工作中,在不同的条件下,数学规划方法的应用。     

Stochastic Programming

This report presents an approach to stochastic programming. It treats mainly the difficulties arising in formulating the problem and the possibilities to derive a deterministic problem by which it can be replaced. In a sense the approach of this report unifies different viewpoints on stochastic programming problems as they have been published in the literature.     

供应商选择问题的随机机会约束规划模型及其混合遗传算法

在供应商的最大供应数量、质量水平、送达时间均为随机变量且预算给定的条件下,建立了供应商选择问题的随机机会约束整数目标规划模型.鉴于传统方法求解随机规划较为困难,给出了一种将随机模拟、神经元网络和遗传算法结合在一起的混合遗传算法并用多个算例进行了验证,结果表明该算法有较强的适应性.     

数量随机环境下托盘回收的逆向物流网络设计

托盘是提高物流效率的工具,通过租赁制回收方式建立起的托盘共用系统能显著提高物流系统的效率。通过分析托盘回收逆向物流网络结构,建立了基于随机理论的托盘再利用逆向物流网络的随机规划模型,并提出了相应的求解算法。     

集装箱空箱调运优化问题的研究

在分析集装箱空箱流转过程的基础上,提出建立基于海陆运整体成本最优的集装箱空箱调运随机性优化模型,并将粒子群算法与随机模拟技术相结合用于求解机会约束规划。最后通过对测试算例进行求解,验证了该模型和算法的合理性和有效性。     

带有模糊参数的城市生活垃圾逆向物流网络设计

在垃圾回收逆向物流网络中,回收的数量具有不确定性,根据这一特点,将各消费区域垃圾回收量看成模糊参数,提出了逆向物流网络的模糊约束机会规划模型,通过把模型中模糊机会约束清晰化,将模型转化为确定性的0—1整数规划模型,通过算例,针对不同的置信水平对模型进行了求解,为逆向物流网络设计提供了依据。     

多品种多周期再利用逆向物流网络设计

指出了不确定环境下研究多品种、多周期再利用逆向物流网络优化问题的重要性,建立了再制造逆向物流网络随机规划模型,并给出了期望值方法求解模型的步骤。通过实例数值仿真,证明了该模型的可行性,并揭示了回收率的波动对最优选址策略的影响。     

Inventory policies for humanitarian aid during hurricanes

In this work we present a stochastic programming model minimizing costs, to support the decision process of inventory policy which best satisfies the demand for food in shelters when hurricane winds are about to impact a town. In this model we consider perishable products as well as the first in first out (FIFO) system for their consumption. In order to make the model closer to reality ordering cost is time-varying and we add a penalty cost in case the shortage exceeds a known limit for two days in a row. Finally the cost to dispose of expired food is greater than the purchase cost of the product since throwing away food has ethical implications. Starting from a stochastic programming model, we present a procedure to transform it to a deterministic mixed integer programming model (MIP) with non-convex objective function over its entire domain, which closely states the situation in reality. Preliminary computational results and discussion are presented.     

Stochastic optimization model for distribution and evacuation planning (A case study of Tehran earthquake)

In this study, a stochastic multi-objective mixed-integer mathematical programming is proposed for logistic distribution and evacuation planning during an earthquake. Decisions about the pre- and post-phases of the disaster are considered seamless. The decisions of the pre-disaster phase relate to the location of permanent relief distribution centers and the number of the commodities to be stored. The decisions of the second phase are to determine the optimal location for the establishment of temporary care centers to increase the speed of treating the injured people and the distribution of the commodities at the affected areas. Humanitarian and cost issues are considered in the proposed models through three objective functions. Several sets of constraints are also considered in the proposed model to make it flexible to handle real issues. Demands for food, blood, water, blanket, and tent are assumed to be probabilistic which are related to several complicated factors and modeled using a complicated network in this study. A simulation is setup to generate the probabilistic distribution of demands through several scenarios. The stochastic demands are assumed as inputs for the proposed stochastic multi-objective mixed integer mathematical programming model.The model is transformed to its deterministic equivalent using chance constraint programming approach. The equivalent deterministic model is solved using an efficient epsilon-constraint approach and an evolutionary algorithm, called non-dominated sorting genetic algorithm (NSGA-II). First several illustrative numerical examples are solved using both solution procedures. The performance of solution procedures is compared and the most efficient solution procedure, i.e., NSGA-II, is used to handle the case study of Tehran earthquake. The results are promising and show that the proposed model and the solution approach can handle the real case study in an efficient way.     

Stochastic programming in production planning: a case with none-simple recourse

Abstract  In modeling real world planning problems as optimization programs the assumption that all parameters are known with certainty is often more seriously violated than the assumption that the objective function and the constraints can be approximated sufficiently accurate by lineair functions. In this paper we discuss the concrete application of stochastic programming to a multiperiod production planning problem in which the demand for the products during the various periods is assumed stochastic with known probability distribution. Since the resulting stochastic program does not possess the property of "simple" recourse no direct use can be made of existing methods that have been proposed in literature for solving problems of this type.     

基于随机需求的物流配送中心选址离散模型研究

针对物流需求不确定情况下的物流配送中心选址问题,对传统模型进行改进,将随机需求变量引入离散型选址模型,利用随机规划理论和遗传算法对实例模型进行求解。结果显示物流需求不确定情况下的随机规划模型的求解结果比假设已知需求情况下的结果真实可信,所需物流费用较少。     

On the Technically Efficient Organization of an Industry: A Study of U.S. Airlines

In nonparametric analysis of production efficiency, the focus of attention is typically on the individual firm. In this article, we evaluate efficiency in resource utilization at the industry level. We propose an integer programming model to measure the extent of input saving possible if the observed output level of the industry is allocated to an optimal number of identical firms. This approach is applied to data for U.S. airlines covering the period 1970–84. Our results confirm that there were too few airlines prior to deregulation and suggest that there were too many firms during the early years of the post-deregulation era.     

Planning in an uncertain environment: Stochastic goal programming using the versatility criterion

This article presents a planning model that applies the versatility criterion to goal programming problems with uncertainty about the constraints which define the set of feasible decisions. Some of the constraint parameters are assumed to be stochastic variables with a joint normal distribution. The solution sought maximizes the probability of satisfying all the constraints. A nonlinear programming model is set out which can be solved by using numerical integration at every step. An illustrative example is provided which shows the possible application of the versatility model to land-use planning.     

Stochastic population forecasts and their uses

"The properties and uses of stochastic forecasts are discussed here. For linear stochastic projections, we show how the computation of forecast moments and the statistical distribution of forecasts depend on the multiplicative and autoregressive structure of the dynamics. Both scalar and vector projection methods are discussed, and their similarities are explored. Next we discuss the uses of stochastic forecasts, arguing that it is important to relate forecasts to the specific decision-making criteria of particular forecast users. The example of [the U.S. system of] Social Security is used to show how a dynamic programming approach may be used to explore alternative decisions in a probabilistic context."     

Panel estimators and the identification of firm-specific efficiency levels in parametric,semiparametric and nonparametric settings

The paper analyzes a number of competing approaches to modeling efficiency in panel studies. The specifications considered include the fixed effects stochastic frontier, the random effects stochastic frontier, the Hausman–Taylor random effects stochastic frontier, and the random and fixed effects stochastic frontier with an AR(1) error. I have summarized the foundations and properties of estimators that have appeared elsewhere and have described the model assumptions under which each of the estimators have been developed. I discuss parametric and nonparametric treatments of time varying efficiency including the Battese–Coelli estimator and linear programming approaches to efficiency measurement. Monte Carlo simulation is used to compare the various estimators and to assess their relative performances under a variety of misspecified settings. A brief illustration of the estimators is conducted using U.S. banking data.     

客专大站到发线运用随机机会约束模型与算法研究

在考虑到发线运用过程中不确定性因素的基础上，研究旅客列车停站时间和接续时间随机变动情况下客专大站到发线运用优化问题，以提高车站作业效率和方便旅客乘降为优化目标，建立了到发线运用的随机机会约束规划模型。通过将模型中的随机机会约束转化为相应的确定性等价形式，从而将随机规划模型转化为确定性模型，并设计了解决该问题的蚁群算法。算例表明该随机机会约束规划模型能取得可靠性更高的到发线运用计划。     

Hölder continuity of the policy function approximation in the value function approximation

We show that given a value function approximation V of a strongly concave stochastic dynamic programming problem (SDDP), the associated policy function approximation is Hölder continuous in V.