物料价格随时间波动及完工期随机的MTO订购决策

对于订单式中小生产企业,物料供应是否及时以及生产进度是否按计划进行是影响订单交货期的关键因素。实际中,某些产品的物料价格随时间连续波动并具有规律性,而生产过程中由于插单多、工人流动性大、设备故障、质量返工等,生产完工期有一定的随机性。对于物料订购决策,传统方法主要考虑订单的投产日期以及物料采购成本最小化,而未考虑订单完工期的随机性特点。基于企业内部供应链集成及总成本最小化的系统思想,综合考虑物料价格的变化趋势以及随机生产完工期的统计规律,分别建立单一物料或多种物料订购情形下总生产成本(包括订购成本、制造成本、延迟完工或提早完工成本等)与订购日期的关系模型,并对其最优解进行讨论、分析与求解。当物料种类单一时,采用极值法对模型极小值点的存在性进行讨论;当物料种类较多时,构建了有约束条件的多决策变量非线性优化模型,并采用最优化理论中的拉格朗日乘子法(即PHR算法)进行求解。通过实例分析,得出的结论是物料最佳订购时间点与物料初始价格、价格波动周期、波动幅度、单位延迟或提早完工成本等因素相关。上述采购与生产集成的思想、模型构建的思路以及求解方法等可供有关人员参考。     

部分延迟订购的Weibull分布变质品库存策略研究

研究需求依赖于即时库存、变质服从Weibull分布、部分延迟订购且延迟订购率与等待时间相关的变质品库存优化策略.当需求依赖于库存水平时,利润最大化问题不能简化为成本最小化问题,所以以利润最大化为目标构建库存模型,并证明了最优解的存在性和唯一性,同时给出应用牛顿迭代法求解模型的具体步骤.最后,通过算例给出了模型的应用并对参数的灵敏度进行了分析.结果表明:延迟订购阻力、缺货成本或机会成本的增加会降低系统的利润同时要求提升系统的服务水平;系统的平均利润对购买成本比较敏感,而服务水平对库存持有成本非常敏感.     

联合订货区间值EOQ模型及变权Shapley值成本分摊方法

研究多个销售商企业组成联盟向一个供应商订购同种商品的联合订货问题。考虑到实际问题中很难预测到精确的需求，本文用区间表示单位时间需求量，研究允许缺货的销售商企业联合订货区间值EOQ模型，其中缺货完全回补。以联合订货平均成本最小为目标，结合连续有序加权集结算子求解出联合订货的周期、区间值订货量和区间值平均成本。定义变权Shapley值，给出区间值合作博弈的区间值变权Shapley值的求解方法，得出区间值变权Shapley值的表达式可直接利用相关联盟值的左、右端点计算得到。考虑联盟和局中人的相对重要性，结合需求率确定合成权重，提出基于区间值变权Shapley值的联合订货成本分摊方法。利用数值算例验证模型和方法的有效性。本文可为解决联合订货成本分摊问题提供决策参考。     

需求双向替代与生产转换下动态批量决策及预测时阈研究

本文研究了存在需求双向替代和生产转换情形下的两产品动态批量决策和预测时阈问题。构建了包含时变的生产转换成本、替代成本以及两产品的生产成本和库存成本在内的成本最小化模型。在最优解性质的基础上，设计出前向动态规划算法求解问题。利用边际成本分析方法得出两产品生产点及再生点的单调性特征，并建立了相应的两产品再生集，最后给出求解预测时阈和相应的决策时阈的充分条件。运用数值实验分析了需求单向替代和双向替代情形下预测时阈的相对大小、同一周期最多发生一次生产转换与可以发生多次转换情形下预测时阈的相对大小，需求增长性与波动性对预测时阈的影响以及生产转换成本与替代成本同时变动时对预测时阈的影响。发现需求双向替代将增加预测时阈的长度，而同一周期可以发生多次生产转换将降低预测时阈的长度。预测时阈随着生产转换成本的增加而显著递增，随着需求增长参数和需求波动参数以及替代成本的增加而递减。     

合作竞争下的供应商数量优化问题研究

从买主单一零部件采购总成本角度出发,建立了一个在合作竞争环境下确定零部件最优 供应商数量的模型. 该模型描述了影响零部件总采购成本的各种因素,包括市场环境、产品特 征、价格稳定程度、边际利润率、买主与供应商的相对讨价还价能力、合作竞争程度与合作成本 等. 通过对模型的求解与仿真分析,研究了这些因素对买主应选择的最优供应商数量的影响.     

模糊信息下多种类应急物资多周期分配优化模型

考虑灾害的突发性、信息获取的不完全性以及应急救援的紧迫性，引入区间数描述应急物资供给与需求的模糊不确定性，引入三角模糊数刻画路网容量受限情况下每周期的最大物资运输总量，综合考虑灾害、灾区、灾民以及物资等多重因素，引入延迟系数，以应急物资分配的总延迟时间最小化和总系统损失最小化为目标，构建模糊信息条件下考虑多需求点、多配送中心、多物资、多周期、多目标的应急物资动态分配优化决策模型，分析了区间目标函数、区间模糊与三角模糊约束条件的清晰化方法，采用基于二维欧式距离客观赋权模糊算法求解模型，并以青海玉树地震为例对所提出模型的有效性和可行性进行验证。结果表明：所提出的模型能够最大程度地权衡延迟时间与系统损失，形成多周期最优的物资分配方案；现实多周期应急物资分配，时间并不是唯一考虑的因素，需要综合考虑不同应急周期的灾情、灾区、灾民和物资等多种因素对系统总损失造成的影响；重视时间偏好系数，可能使系统总损失增大，表明单一考虑时间偏好系数和损失偏好系数均具有片面性，应该把握选择"度"，发挥二者结合的相互促进作用；物资分配方案基于决策者偏好，并考虑每周期不同需求点的易损性、重要性、需求紧急性以及各类应急物资的重要性与时效性差异参数，有利于提高多周期决策的柔性和现实适用性。     

两期不确定性需求下的供应链供需博弈

由一个供应商和一个零售商组成的系统,系统成员进行两期博彝,每期初,零售商订购,每期末,销售剩余产品剩余价值为零;零售商所面临的两期不确定性需求相关或不相关,当两期需求相关时,系统成员可以通过第一期销售情况更新第二期不确定性需求的分布.基于此模型,首先,探讨了供应商提供的回购合同的条件以及最优的回购合同参数.其次,通过数值模拟计算,得出零售商和供应链系统偏好于采用更新信息,增加收益,而供应商获取更多的更新信息使得自身收益减少.最后,若在不确定性需求相关下,供应商愿意提供回购合同,那么在不确定性需求不相关下,供应商也愿意提供回购合同,还指出,学习不确定性需求分布最佳的方式是通过回购合同,而不是批发价格合同.     

基于可控提前期的随机寄售库存模型

本文通过假定需求提前期为随机且可控的,将寄售库存（Consignment Stock,CS）模型拓展到随机情形。本文首先将库存成本分为与财务相关的成本和与储存相关的成本两部分,得出CS方式下买卖双方的联合期望总成本公式,然后将订货量、订货点、提前期、运送次数作为决策变量,求得系统的最优参数设置及最小总成本。文章最后提出一个算法,并通过仿真的形式表明无论在确定还是随机环境中,CS方式的总成本都可能优于集成化库存方式。     

需求替代的两产品动态批量最优预测时阈研究

研究了存在需求单向替代的两产品动态批量决策的最优预测时阈问题.构建了包含替代成本、生产转换成本和库存成本在内的成本最小化模型,分析得出在只存在3类再生点(Ⅰ类、Ⅱ类和Ⅲ类)情形下的再生点单调性特征.同时,设计出了多项式时间的前向动态规划算法.运用数值试验分析了最优预测时阈与生产转换成本、替代成本、需求特征(需求增长性和...     

基于遗传算法的有折扣多产品采购多供应商选择问题求解

针对传统供应商选择过程大多只面向单一供应商进行选择的局限性,本文以质量、价格、交货期和交货提前期为评价指标,以最小化综合评价值为目标,纳入供应商供货能力和价格折扣,建立面向多供应商采购多产品条件下供应商选择的数学模型,并提出基于改进遗传算法的模型求解方法。实证分析验证了本文提出的求解方法的有效性。     

Inventory management with variable lead-time dependent procurement cost

A reduction in the inventory replenishment lead-time allows reducing safety stock requirements and improving customer service. However, it might be accompanied by increased procurement costs because of premium charges imposed by suppliers, or higher transportation costs. This paper studies a single-stage variable lead-time inventory system with lead-time dependent procurement cost. Selection of the lead-time value represents finding the trade-off between benefits of lead-time reduction and increase in the procurement cost. A model for joint optimization of inventory and procurement costs is developed. Numerical studies are conducted to identify conditions under which lead-time reduction is favorable compared to procuring at the lowest cost.     

New lower bounds for the second variable Zagreb index

The aim of this paper is to obtain new sharp inequalities for a large family of topological indices, including the second variable Zagreb index \(M_2^{\alpha }\), and to characterize the set of extremal graphs with respect to them. Our main results provide lower bounds on this family of topological indices involving just the minimum and the maximum degree of the graph. These inequalities are new even for the Randi?, the second Zagreb and the modified Zagreb indices.     

On threshold BDDs and the optimal variable ordering problem

Many combinatorial optimization problems can be formulated as 0/1 integer programs (0/1 IPs). The investigation of the structure of these problems raises the following tasks: count or enumerate the feasible solutions and find an optimal solution according to a given linear objective function. All these tasks can be accomplished using binary decision diagrams (BDDs), a very popular and effective datastructure in computational logics and hardware verification. We present a novel approach for these tasks which consists of an output-sensitive algorithm for building a BDD for a linear constraint (a so-called threshold BDD) and a parallel AND operation on threshold BDDs. In particular our algorithm is capable of solving knapsack problems, subset sum problems and multidimensional knapsack problems. BDDs are represented as a directed acyclic graph. The size of a BDD is the number of nodes of its graph. It heavily depends on the chosen variable ordering. Finding the optimal variable ordering is an NP-hard problem. We derive a 0/1 IP for finding an optimal variable ordering of a threshold BDD. This 0/1 IP formulation provides the basis for the computation of the variable ordering spectrum of a threshold function. We introduce our new tool azove 2.0 as an enhancement to azove 1.1 which is a tool for counting and enumerating 0/1 points. Computational results on benchmarks from the literature show the strength of our new method.     

Asymptotic Properties of Weighted M-estimators for variable probability samples

I provide a systematic treatment of the asymptotic properties of weighted M-estimators under variable probability stratified sampling. The characterization of the sampling scheme and representation of the objective function allow for a straightforward analysis. Simple, consistent asymptotic variance matrix estimators are proposed for a large class of problems. When stratification is based on exogenous variables, I show that the unweighted M-estimator is more efficient than the weighted estimator under a generalized conditional information matrix equality. When population frequencies are known, a more efficient weighting is possible. I also show how the results carry over to multinomial sampling.     

高铁改变城市竞争

高铁的飞速发展将对中国城市之间，包括区域的发展将会产生深远影响，甚至有可能带来一次革命。     

基于GLM的贝叶斯变量与模型选择

针对非正态响应的部分因子试验,当筛选试验所涉及的因子数目较大时,提出了基于广义线性模型(generalized linear models,GLM)的贝叶斯变量与模型选择方法.首先,针对模型参数的不确定性,选择了经验贝叶斯先验.其次,在广义线性模型的线性预测器中对每个变量设置了二元变量指示器,并建立起变量指示器与模型指示器之间的转换关系.然后,利用变量指示器与模型指示器的后验概率来识别显著性因子与选择最佳模型.最后,以实际的工业案例说明此方法能够有效地识别非正态响应部分因子试验的显著性因子.     

On network two stages variable returns to scale Dea models

Multiplicative decomposition of stages indices is shown to be consistent with Vrs network technologies. It is also shown why the primal dual correspondence breaks for serial network Vrs models. Different Vrs models can be associated with alternative transfer pricing systems, within the network. Multiplicative decomposition implies marginal cost pricing across stages. While other pricing systems (full cost) correspond to some of the known non-multiplicatively decomposable Vrs models, proposed in the literature. Stages indices, therefore, respond not only to efficiency, but also to the network’s distributive criteria across stages. The distributive contents of stage indices provide the key element for a solution to the problem of measuring scale efficiency in network systems. Multiplicative decomposable Vrs models can be extended to more general network systems, containing both parallel and in series structures. The cost of this generalisation is that efficiency indices are referred to modified stages, that is to stages that include dummy processes. In perspective, these results contribute to show how organisational aspects, such as transfer pricing systems, could be modelled once network technologies are approached from the multiplier (ratio) side.     

LASSO variable selection in data envelopment analysis with small datasets

The curse of dimensionality problem arises when a limited number of observations are used to estimate a high-dimensional frontier, in particular, by data envelopment analysis (DEA). The study conducts a data generating process (DGP) to argue the typical “rule of thumb” used in DEA, e.g. the required number of observations should be at least larger than twice of the number of inputs and outputs, is ambiguous and will produce large deviations in estimating the technical efficiency. To address this issue, we propose a Least Absolute Shrinkage and Selection Operator (LASSO) variable selection technique, which is usually used in data science for extracting significant factors, and combine it in a sign-constrained convex nonparametric least squares (SCNLS), which can be regarded as DEA estimator. Simulation results demonstrate that the proposed LASSO-SCNLS method and its variants provide useful guidelines for the DEA with small datasets.