基于灰色理论的政府市政工程采购评标研究

政府工程采购在政府采购中的份额达60%以上,考虑政府工程采购过程中评标的重要性及评价过程本身具有的不确定性和复杂性,提出了构建基于灰色理论的政府工程采购灰靶评标模型。将决策目标下的各个指标值集结为目标综合效果评价值,分别定义正、负靶心,计算各评价对象到正、负靶心的距离,通过空间投影分析计算各方案的综合靶心距,再以综合靶心距最小准则构建目标规划模型,得到各个目标的权重。构建的灰靶评标模型扩展了灰靶决策方法的应用领域,是对政府工程采购评标方法的一次有益尝试。实例分析验证了该模型在政府市政工程采购评标实际应用中的有效性和科学性。     

两个灰函数商的极限运算定理

本文在文[1]、[2]的基础上,研究了两灰函数商的极限运算定理。它是一般区间灰数的延伸。     

有理灰数的大小关系

本文在区间型灰数与信息型灰数的基础上。首先独立给出同心有理灰数的定义,然后给出几个有理灰数大小关系的定理及证明。使有理灰数的大小关系可作为实数大小关系的推广,从而使有理灰数集成为全序集。     

基于水平效应函数的模糊指派求解模型

结合模糊信息结构特征,提出了水平效应函数L(λ)的概念,建立了从整体上集中量化模糊数位置的、具有广泛可操作性的IL-度量方法及IL-度量在水平效应函数 L(λ)下的IDθ-不确定度,进而通过引进的零型模糊数推广了著名的匈牙利算法。最后通过一个算例验证了算法的有效性。并在此基础上,利用模糊信息的IDθ-不确定度分析了指派决策的可靠性程度。该讨论为某种意识下的不确定型决策奠定了理论基础,具有较强的可操作性。     

复有理灰数的运算及其性质

本文在区间灰数和信息型灰数推广的基础上,将有理灰数进行了推广,给出了复有理灰数的定义,并讨论了它的运算及其性质。     

经典灰数

在邓聚龙教授提出的层次型灰数和信息型灰数的基础上。本文首先给出经典灰数的概念。并研究了它的分类,运算和性质。     

两个灰距离空间(■d)(■.ρ)

本文在邓聚龙教授提出的层次型灰数和信息型灰数——经典有理灰数的基础上,用近代分析的方法,给出两种灰距离空间(/.d)和(/.p),并结合几何直观将两种灰距离进行了比较。     

关于“灰极限”的乘法性质

在经典有理灰数的基础上,定义了两个经典有理灰数的距离,进而建立灰极限的概念。本文在此意义下证明几个有关引理,并利用它证明微积分学中关于极限的乘法公式也同样成立。     

基于CCTWF的灰色评估模型及其应用研究——以水利工程管理现代化综合评价为例

为解决灰色聚类评估分析中基于三角白化权函数的灰色聚类评估方法所确定的灰类区间长度偏大,导致聚类系数计算复杂,评估结果缺乏稳定性的问题,对已有的端点三角白化权函数和中心点三角白化权函数进行改进,构建紧中心点三角白化权函数,通过对三类白化权函数的灰类交叉特性、聚类系数、灰类区间的划分、端点选取和聚类效果等方面进行比较分析研究,说明新方法的优越性,并以水利工程管理现代化综合评价为例进行对比分析,进一步验证基于紧中心点三角白化权函数的灰色聚类评价方法的可行性和有效性。结果表明:紧中心点三角白化权函数优于端点白化权函数和中心点三角白化权函数。     

模糊网络总工期隶属函数及路径关键度分析

为了求取模糊网络总工期隶属函数和路径关键度,以模糊扩展定理和模糊截集概念为基础,通过求取所有活动的模糊截集,将模糊网络转变为一系列不同α截集下的区间网络。根据模糊扩展定理可知,这些区间网络的工期范围即为模糊网络总工期的α截集,由此可以构建出模糊网络总工期隶属函数。依据Chanas提出的模糊网络路径关键度定义,利用区间网络关键路径与模糊网络关键度的关系,计算出所有路径的关键度。列举了一个活动持续时间为LR型模糊数的模糊网络总工期隶属函数和路径关键度运算实例。     

A multistage stochastic programming model for the New Zealand dairy industry

This paper presents an application of multistage stochastic programming to a production planning problem for Fonterra, a leading company in the New Zealand dairy industry, taking into account uncertain milk supply, price–demand curves and contracting. We describe a model for Fonterra's supply chain, and a model for uncertain milk supply. We then present a multistage stochastic quadratic programming model and a decomposition algorithm to compute an optimal sales policy, which is tested in simulation against a deterministic policy.     

The impact of flexibility on operational supply chain planning

We study in this paper the effects of volume flexibility, delivery flexibility and operational decision flexibility in operational supply chain planning under uncertain demand. We use a rolling schedule to plan supply chain operations for a whole year. The planning horizon is 4 weeks with deterministic demand in the first week and predicted for the following 3 weeks. Using a case from the Norwegian meat industry, we compare the annual operating results of using a two-stage stochastic programming model to the deterministic expected value problem in order to discuss the impact of flexibility in the supply chain.     

Incorporating uncertainty into a supplier selection problem

Supplier selection is an important strategic supply chain design decision. Incorporating uncertainty of demand and supplier capacity into the optimization model results in a robust selection of suppliers. A two-stage stochastic programming (SP) model and a chance-constrained programming (CCP) model are developed to determine a minimal set of suppliers and optimal order quantities with consideration of business volume discounts. Both models include several objectives and strive to balance a small number of suppliers with the risk of not being able to meet demand. The SP model is scenario-based and uses penalty coefficients whereas the CCP model assumes a probability distribution and constrains the probability of not meeting demand. Both formulations improve on a deterministic mixed integer linear program and give the decision maker a more complete picture of tradeoffs between cost, system reliability and other factors. We present Pareto-optimal solutions for a sample problem to demonstrate the benefits of the SP and CCP models. In order to describe the tradeoffs between costs and risks in an analytical form, we use multi-parametric programming techniques to more completely analyze the alternative Pareto-optimal supplier selection solutions in the CCP model. This analysis gives insights into the robustness of the solutions with respect to number of suppliers, costs and probability of not meeting demand.     

Budget allocation for permanent and contingent capacity under stochastic demand

We develop a model of budget allocation for permanent and contingent workforce under stochastic demand. The level of permanent capacity is determined at the beginning of the horizon and is kept constant throughout, whereas the number of temporary workers to be hired must be decided in each period. Compared to existing budgeting models, this paper explicitly considers a budget constraint. Under the assumption of a restricted budget, the objective is to minimize capacity shortages. When over-expenditures are allowed, both budget deviations and shortage costs are to be minimized. The capacity shortage cost function is assumed to be either linear or quadratic with the amount of shortage, which corresponds to different market structures or different types of services. We thus examine four variants of the problem that we model and solve either approximately or to optimality when possible. A comprehensive experimental design is designed to analyze the behavior of our models when several levels of demand variability and parameter values are considered. The parameters consist of the initial budget level, the unit cost of temporary workers and the budget deviation penalty/reward rates. Varying these parameters produce several trade-offs between permanent and temporary workforce levels, and between capacity shortages and budget deviations. Numerical results also show that the quadratic cost function leads to smooth and moderate capacity shortages over the time periods, whereas all shortages are either avoided or accepted when the cost function is linear.     

Capacity flexibility allocation in an outsourced supply chain with reservation

We consider a contract manufacturer that serves a limited number of outsourcers (customers) on a single capacitated production line. The outsourcers have different levels of demand uncertainty and the contract manufacturer faces the question how to allocate the contractual capacity flexibility in an optimal way. The contractual capacity flexibility is a contract parameter that sets the amount of demand the contract manufacturer is obliged to accept from the outsourcers. We develop a hierarchical model that consists of two decision levels. At the tactical level, the contract manufacturer allocates the capacity flexibility to the different outsourcers by maximizing the expected profit. Offering more flexibility to the more uncertain outsourcer generates higher expected revenue, but also increases the expected penalty costs. The allocated capacity flexibilities (determined at the tactical level) are input parameters to the lower decision level, where the operational planning decisions are made and actual demands are observed. We perform a numerical study by solving the two-level hierarchical planning problem iteratively. We first solve the higher level problem, which has been formulated as an integer program, and then perform a simulation study, where we solve a mathematical programming model in a rolling horizon setting to measure the operational performance of the system. The simulation results reveal that when the acceptance decision is made (given the allocated capacity flexibility decision), priority is given to the less uncertain outsourcer, whereas when the orders are placed, priority is given to the most uncertain outsourcer. Our insights are helpful for contract manufacturers when having contract negotiations with the outsourcers. Moreover, we show that hierarchical integration and anticipation are required, especially for cases with high penalty cost and tight capacities.     

Simultaneous control of production, repair/replacement and preventive maintenance of deteriorating manufacturing systems

This paper presents a method to find the optimal production, repair/replacement and preventive maintenance policies for a degraded manufacturing system. The system is subject to random machine failures and repairs. The status of the system is deemed to degrade with repair activities. When a failure occurs, the machine is either repaired or replaced, and a replacement action renews the machine, while a repair action brings it to a degraded operational state, with the next repair time increasing as the number of repairs increases as well. A preventive maintenance action is considered in order to improve the reliability of the machine, thereby reducing the amount of disruptions caused by machine failures. The decision variables are the production rate, the preventive maintenance rate and the repair/replacement switching policy upon machine failure. The objective of the study is to find the decision variables that minimize the overall cost, including repair, replacement, preventive maintenance, inventory holding and backlog costs over an infinite planning horizon. The proposed model is based on a semi-Markov decision process, and the stochastic dynamic programming method is used to obtain the optimality conditions. A numerical example is given to illustrate the proposed model, and a sensitivity analysis is considered in order to confirm the structure of the control policy and to illustrate the usefulness of the proposed approach.     

Supplier selection-order allocation: A two-stage multiple criteria dynamic programming approach

This paper proposes a two-stage multiple criteria dynamic programming approach for two of the most critical tasks in supply chain management, namely, supplier selection and order allocation. In the first stage, to address multiple decision criteria in supplier ranking, the analytic hierarchy process (AHP) is employed. In the second stage, supplier ranks are fed into an order allocation model that aims at maximizing a utility function for the firm as well as minimizing the total supply chain costs, subject to constraints on demand, capacity, and inventory levels. A dynamic programming approach is crafted to solve the proposed bi-objective model.     

A state space augmentation algorithm for the replenishment cycle inventory policy

In this work we propose an efficient dynamic programming approach for computing replenishment cycle policy parameters under non-stationary stochastic demand and service level constraints. The replenishment cycle policy is a popular inventory control policy typically employed for dampening planning instability. The approach proposed in this work achieves a significant computational efficiency and it can solve any relevant size instance in trivial time. Our method exploits the well known concept of state space relaxation. A filtering procedure and an augmenting procedure for the state space graph are proposed. Starting from a relaxed state space graph our method tries to remove provably suboptimal arcs and states (filtering) and then it tries to efficiently build up (augmenting) a reduced state space graph representing the original problem. Our experimental results show that the filtering procedure and the augmenting procedure often generate a small filtered state space graph, which can be easily processed using dynamic programming in order to produce a solution for the original problem.     

大型工程项目的投保决策方法

保险是应对工程项目风险的一种重要的措施。一个投保决策是否成功。首先取决于保险人是否选择得当。选择保险人是一个多指标决策问题。通常采用特定方式来描述具有不确定的决策信息。章提出了一种基于区间数的TOPSIS方法。应用于保险人的选择。来解决工程项目投保决策中选择保险人的问题。并通过一个案例验证了该方法的可行性。