不确定需求与产出供应链回购契约决策

针对市场需求的不确定性和供应商产出产品合格率的不确定性,提出供应商产出量小于销售商订购量时需再生产满足销售商的订购,以及供应商生产过量和回购的产品通过折扣市场出售,建立以供应链期望利润为目标函数,以回购单价为决策变量的供应链回购契约决策模型.通过模型的分析,证明了模型存在最优的期望利润,给出了最优回购单价满足的方程;同时,也证明了最优订购量,最优生产量和销售商最优期望利润是回购单价的增函数.最后,通过算例验证了模型的有效性.     

带有随机产出和广告投入的Newsboy模型

首次将随机产出和广告投入同时引入到Newsboy模型中,分别在乘积形式随机产出和加和形式随机产出情况下,建立带有广告费用的最优决策模型,通过分析得出如下结论:在乘积随机产出情况下,随着广告费用的不断增加,最优计划生产量在快速增加;而在加和形式随机产出情况下,随着广告费用不断增加时,最优计划生产量也在不断增加,但其增加的速率较小。最后,指出乘积形式的随机产出适用于刚上市的新产品,而加和形式的随机产出适用于品牌产品。     

需求分布未知且提前期可控的联合库存决策

在提前期内需求为自由分布且提前期依赖于订购批量和生产率的条件下,建立了同时考虑供需双方成本的联合库存决策模型,运用最小最大准则分析了模型最优解的存在性,设计了高效的最优解搜索算法.通过数值算例说明算法的有效性,分析了获取需求分布信息的代价与收益;并与供需独立的决策模型比较,表明了联合库存决策可以显著地降低供应链成本.     

不确定需求下再制造系统回收、生产与定价策略研究

以单一制造商为研究对象,在需求不确定且回收量依赖于需求量的环境下,构建基于期望利润最大化的数学模型,给出最优的回收率、新品和再制造品生产量及销售价格,并通过解析和数值仿真分析了市场规模、单位节约成本、回收成本的规模参数和需求不确定性对最优策略的影响.结果表明:市场规模增大,销售价格、回收率、生产量和再制造量都随之增大,而新品生产量先增大后减小;单位节约成本越大,回收成本的规模参数越小时,进行再制造就越有利;需求不确定性增加时,进行再制造更有利,可以降低由需求不确定性所造成的损失.     

具有同构产品市场竞争条件的两类企业市场进入及退出分析

本文研究两类企业在市场上进行竞争 ,生产量及企业数量达到均衡时市场进入与退出的问题 .假设市场上只有两类生产技术管理条件不同的企业 ,而一类企业的所有特性都相同 ,每个企业按最大利润组织生产 ,给定市场需求函数 ,可以建立起企业竞争的均衡模型 .通过变量替换 ,将均衡生产量及均衡利润表示为企业数量的函数 ,按利润不小于零为原则 ,可以构造出市场进入与退出的博弈模型 .本文还给出了企业市场进入与退出均衡状态判断的定理 ,并以一种线性需求函数与成本函数为特例 ,通过具体的演示 ,表述了模型解释客观现象的能力 .     

产出为Cobb-Dauglas函数的 Holmstrom-Milgrom委托代理模型

Holmstrom-Milgrom模型是在委托代理理论一般化模型的基础上提出的一个简化模型,引入代理人的能力水平,将产出函数从一维的线性函数扩展到二维的非线性Cobb-Dauglas生产函数,分析求解了新的模型.与原模型相比,新的最优解中代理人的努力程度有所提高,风险成本有所降低,委托人的期望收益有所改善.     

一类跳扩散需求存贮系统(s,S)库存控制策略研究

考虑的是连续检查库存,需求为一个常时间函数和-个复合Poison跳扩散随机过程的和的存贮系统最优库存控制问题.基于期望折扣成本最小建立了无穷时间区间具有固定订购成本的最优库存模型,确定可采用(s,S)策略进行库存控制,给出了最优(s,S)策略的充要条件--HJB方程Ⅰ、Ⅱ.我们采用"猜测"的方法确定了最优(s,S)策略对应的值函数形式,建立了确定库存参数的最优化模型.     

基于制造商资金有约束的替代产品的最优生产决策

考虑一个单周期的生产决策模型,在该模型中有一个制造商生产两种可替代的产品.面对随机的市场需求,制造商要在需求到来之前制定出两种产品的生产决策来最大化自己的期望利润.在制造商的资金有、无约束两种情形下,证明了制造商的收益函数的期望是关于两种产品生产数量的凹函数,探讨了资金的约束以及产品的替代给制造商的生产决策所带来的影响,给出了最优生产数量的若干性质.另外,针对需求分布为均匀分布的特殊情形给出了制造商最优生产决策的简单表达形式.     

离散模糊需求报童问题的可信性模型研究

基于可信性理论,建立了确定离散模糊需求报童问题订货量的期望成本与利润模型,并与基于可能性理论的质心特征值分析模型进行了比较.数值研究结果表明:1)对应每一模型的最小模糊成本和最大利润的订货量不一致,且模糊期望模型与质心特征值模型确定的订货量不同;2)对应不同订货量,模糊可能性成本、利润之和及期望成本、利润之和均不为固定常数.由于在模糊环境下,与概率测度对应的模糊量描述是可信性测度,所以,相比而言,离散模糊需求报童问题的模糊期望值模型较模糊可能性模型好.     

具有非期望产出的随机DEA模型研究

为解决含有不确定信息的非期望产出效率评价问题,建立了一个非期望产出的随机DEA模型.该模型将非期望产出作为负期望产出进行处理,引入了期望效率值、显著性水平来刻画随机问题,并通过机会约束规划的相关知识将模型转化为确定形式.对模型的最优值的相关性质进行了探讨,说明最优值与期望效率值、显著性水平之间的关系.最后给出数值实验说明该模型的有效性.     

Output learning and duality in joint production programs

Summary Output learning is incorporated into a short-run static cost-minimizing model of the multiproduct, multifactor firm which employs a fixed-coefficients technology. The firm's output processes or activities are ultimately specified as functions of the activity variables themselves, thus rentering a generalization to a concave program. A Lagrange dual formulation is then used to obtain the indirect cost objective. Given that this optimal cost function is differentiable and satisfies a regularity condition, its price derivatives serve as input demand functions while its derivatives with respect to the minimum output requirements yield a set of (implicit) marginal costs or dual variables.     

A production planning model for an unreliable production facility: Case of finite horizon and single demand

We study a two-level inventory system that is subject to failures and repairs. The objective is to minimize the expected total cost so as to determine the production plan for a single quantity demand. The expected total cost consists of the inventory carrying costs for finished and unfinished items, the backlog cost for not meeting the demand due-date, and the planning costs associated with the ordering schedule of unfinished items. The production plan consists of the optimal number of lot sizes, the optimal size for each lot, the optimal ordering schedule for unfinished items, and the optimal due-date to be assigned to the demand. To gain insight, we solve special cases and use their results to device an efficient solution approach for the main model. The models are solved to optimality and the solution is either obtained in closed form or through very efficient algorithms.     

Lot-sizing two-echelon assembly systems with random yields and rigid demand

We consider a two-echelon assembly system producing a single final product for which the demand is known. The first echelon consists of several parallel stages, whereas the second echelon consists of a single assembly stage. We assume that the yield at each stage is random and that demand needs to be satisfied in its entirety; thus, several production runs may be required. A production policy should specify, for each possible configuration of intermediate inventories, on which stage to produce next and the lot size to be processed. The objective is to minimize the expected total of setup and variable production costs.We prove that the expected cost of any production policy can be calculated by solving a finite set of linear equations whose solution is unique. The result is general in that it applies to any yield distribution. We also develop efficient algorithms leading to heuristic solutions with high precision and, as an example, provide numerical results for binomial yields.     

CAPACITY AND PRICE OF NONRENEWABLE RESOURCE DEPOSITS

This paper develops a model to determine the planned rate of production from a nonrenewable resource deposit and applies the model to the U.S. copper industry to estimate changes in aggregate planned copper production as a function of copper price expectations. Standard investment theory is applied in the model to examine the choice of the planned rate of extraction from limited volume resource deposits. A setting is analyzed in which the total production volume from any site is fixed by the size of the resource deposit at that site. Within this setting, the planned rate of resource extraction is a fully endogenous solution of the model. Properties of the model are investigated to determine changes in the planned rate of production caused by variation in operating cost, investment cost and output price parameters. The planned rate of production from any deposit is found to increase at a decreasing rate, given incremental increases in the output price, ceteris paribus. The finding is illustrated with data describing mined and yet-to-be mined copper deposits in the United States. A close match is observed between the calculated production rate for mined copper deposits and the mine capacity (rate of production) chosen by the owners of those deposits, given plausible assumptions about mine owner price expectations. The planned rate of production is aggregated across all deposits and plotted as a function of output price. An aggregate medium run copper supply curve is obtained, indicating the elasticity of planned production to changes in the expected long run output price of copper. This empirical example suggests that doubling the output price from $1 to $2 per pound copper, would triple aggregate planned copper production in the United States.     

A marginal cost analysis for prevetive replacement policies

Some well known preventive replacement policies are each characterized by a single parameter which governs the sequence of planned replacements. In this paper we define and compute for any one of these policies the marginal cost of a planned replacement in terms of the policy parameter. This function is used to obtain the equation of optimality, with respect to two common objective functions: the expected cost per unit time and the expected total discounted costs, and to investigate the existence of a unique finite solution. Moreover, the marginal cost notion provides reasoning to the mathematical results of the models and thereby clarifies the effect of the various components of the model on these results. This better insight into models is perhaps the most important benefit of a marginal cost analysis.     

A Simple Model for Computing the Trade-Offs between Plant Capacity and Operating Strategies

A method is presented for determining the optimal capacity of a production system which encounters a strong seasonal demand for its output. A model is developed for analysing the trade-offs between plant capacity and different production strategies. The production strategies are computed by linear programming for a range of plant capacities and a given demand pattern. The annual capital costs of the plant are calculated as a function of plant capacity and the two costs are added in order to determine the plant capacity corresponding to the least cost.     

Manufacturing delivery performance for supply chain management

This paper considers single-stage make-to-order production systems. We focus on (1) modeling the appropriate expected costs under a variety of modeling assumptions and (2) characterizing the optimal policies. Our approach to solving the problem is to derive the distribution of actual completion times of the process for individual orders and to compare it to the corresponding quoted due dates in order to obtain the expected total costs. We then show the convexity of the objective cost function for determining the decision variable(s), the planned customer order leadtime.     

A periodic review production and maintenance model with random demand,deteriorating equipment,and binomial yield

In many environments, product yield is heavily influenced by equipment condition. Despite this fact, previous research has either focused on the issue of maintenance, ignoring the effect of equipment condition on yield, or has focused on the issue of production, omitting the possibility of actively changing the machine state. We formulate a Markov decision process model of a single-stage production system in which demand is random. The product yield has a binomial distribution that depends on the equipment condition, which deteriorates over time. The objective is to choose simultaneously the equipment maintenance schedule as well as the quantity to produce in a way that minimizes the sum of expected production, backorder, and holding costs. After proving some results about the structural properties of the optimal policy, numerical problems are used to compare this method to the typical approach of solving the maintenance and production problems sequentially. The results show that the simultaneous solution provides substantial gains over the sequential approach. In the cases studied, the proposed method resulted in an average cost savings of approximately 18%.     

Supply chain models for an assembly system with preprocessing of raw materials

In this paper, we investigate the material procurement and delivery policy in a production system where raw materials enter into the assembly line from two different flow channels. The system encompasses batch production process in which the finished product demand is approximately constant for an infinite planning horizon. Two distinct types of raw materials are passed through the assembly line before to convert them into the finished product. Of the two types of raw materials, one type requires preprocessing inside the facility before the assembly operation and other group is fed straightway in the assembly line. The conversion factors are assigned to raw materials to quantify the raw material batch size required. To analyze such a system, we formulate a nonlinear cost function to aggregate all the costs of the inventories, ordering, shipping and deliveries. An algorithm using the branch and bound concept is provided to find the best integer values of the optimal solutions. The result shows that the optimal procurement and delivery policy minimizes the expected total cost of the model. Using a test problem, the inventory requirements at each stage of production and their corresponding costs are calculated. From the analysis, it is shown that the rate and direction change of total cost is turned to positive when delivery rates per batch reaches close to the optimal value and the minimum cost is achieved at the optimal delivery rate. Also, it is shown that total incremental cost is monotonically increasing, if the finished product batch size is increased, and if, inventory cost rates are increased. We examine a set of numerical examples that reveal the insights into the procurement-delivery policy and the performance of such an assembly type inventory model.     

Designing a new computational approach of partial backlogging on the economic production quantity model for deteriorating items with non-linear holding cost under inflationary conditions

This paper discusses the production inventory model over an infinite time horizon. Here we consider demand as a function of stock and time. Deterioration is a function of time and time-varying production. Our objective is to minimize the total cost which is a function of set up cost, holding cost, shortage cost, and opportunity cost due to lost sales. The traditional costs such as purchasing cost, shortage cost and opportunity cost due to lost sales are kept constant. We consider holding cost to be a non-linear function of time. Shortages are allowed and are partially backlogged. Here, time durations are the decision variables. Numerical examples are given to illustrate the model.