面向单产品随机需求的计划期产能决策

国内传统中小生产企业面临的市场需求复杂多变,其生存和发展受到极大挑战.在人工作业系统中,生产线的产能主要取决于工人的数量、掌握技能数及技能水平.在备货型生产模式下,对于只生产单产品的生产线,当计划期内产品需求随机波动性特征明显时,将导致产能与需求的不匹配.考虑工人的薪酬、培训成本,需求工时小于基本产能的库存成本,需求工时处于基本产能与最大产能之间的加班成本,需求工时超过最大产能的加班及外包成本,以员工多技能及各工序生产速率平衡为基础,对计划期初生产线所需的工人数量进行决策,构建总生产成本优化模型,采用极值法对最优解进行求解及分析.通过算例验证了模型的有效性,对员工薪酬、库存成本、加班成本、外包成本系数以及产品单件工时、需求均值及标准差等进行了敏感性分析,得出的结论是:产品单件总工时、需求均值对最优工人数量及总生产成本影响很大,其他各类成本系数对其也有一定影响.尽可能降低单件总工时、提高需求预测准确性等有利于企业控制生产成本.     

基于时间敏感需求及随机完工期的承诺交货期决策

在新的经济形势和高新技术指引下,产品更新换代的速度加快,基于时间的竞争成为供应链竞争的焦点.在制造商为核心的供应链中,产品需求除了与价格有关外,与承诺交货期也有一定关联,且部分客户愿意为快速交货而支付更高价格.当需求与时间及价格具有敏感性且实际完工期服从一定的随机分布时,建立利润最大化及服务水平约束的承诺交货期决策模型,并对模型进行讨论及优化分析.通过算例验证了模型的有效性,通过参数敏感性分析得出的结论是:当客户服务水平达到一定阈值时,最优承诺交货期将发生改变;价格及交货期敏感系数不影响承诺交货期,但影响产品需求及最终利润;最优承诺交货期与单位提早完工成本是反向变动的关系而与单位延迟完工成本是正向变动的关系;随着完工期均值及标准差的不断增大,最优承诺交货期呈上升趋势,利润、市场需求及价格不断下降.     

一类相依索赔离散风险模型的有限时间破产概率估计

本文研究一类具有相依索赔及重尾索赔噪声项的离散风险模型有限时间破产概率.在该模型中,索赔额服从具有独立同分布噪声项的单边线性过程;由保险公司的风险投资和无风险投资导致的随机折现因子与单边线性过程的噪声项相独立;保险公司的保费率是恒定的常数.当单边线性过程的噪声项服从重尾分布时,本文得到该离散风险模型有限时间破产概率的渐近估计.     

考虑一般投资收益和时间相依索赔情形下二维带扰动风险模型的有限时间破产概率渐近估计

考虑具有一般投资收益过程的二维带扰动保险风险模型,假定保险公司盈余的投资收益过程由右连左极随机过程刻画,且两种索赔额与索赔到达时间间隔服从S armanov相依结构.当索赔额分布属于正则变化尾分布族时,得到有限时间破产概率的渐近公式.当描述投资收益过程的右连左极过程分别取Lévy过程,Vasicek利率模型,Cox-Ingersoll-Ross(CIR)利率模型,Heston模型时,得到相应投资收益情形下破产概率的渐近公式.     

库存控制的随机优化(英文)

本文将随机扰动引入企业库存控制系统,并重点讨论了扰动引入后,库存投资与市场需求的相互作用,及对未来折现利润的相关影响.文章建立了关于库存控制管理的最优跨时决策模型.不同于其它文献中将市场需求及库存投资看作具有具体分布的随机变量,文章将两者设置为随机过程并分别服从不同的It型SDE.文章得出了三个命题及一些有意义的结果.     

非正态总体控制图和预防维修策略耦合优化的研究

主要探讨非正态有偏总体的过程监控和预防维修耦合优化问题。假定设备故障率随时间递增,设备发生异常前在正常状态的停留时间服从威布尔分布,一旦发生异常将导致过程均值漂移。采用赋权方差法构造X控制图,将过程监控和预防维修策略联系起来,结合生产不合格品损失、抽样成本及维修成本等,构建综合损失模型,提出动态抽样方案、控制图参数和预防维修间隔的确定方法。最后对模型进行了灵敏度分析。     

一类具有校正隔离率随机SIQS模型的绝灭性与分布

该文探讨了一类具有校正隔离率的随机传染病模型,得到了该模型存在唯一的全局解.研究表明,当白噪声强度取较大值时,随机模型的解在无病平衡点附近是绝灭的,感染者的密度将指数衰减到零.当白噪声的强度较小时,随机模型的正解在地方病平衡点附近服从唯一的平稳分布.进而,若地方病平衡点是稳定的,在适当的条件下,该解渐近服从一个三维正态分布,且得到了均值与方差的表达式.最后,数值模拟图显示了该解的性质并对模型做出了合理的解释.     

突发事件下不对称信息供应链协调机制研究

针对一个由供应商和一个零售商构成的供应链,在零售商成本为私有信息条件下,假设产品的市场需求为零售价格的指数函数,研究如何协调供应链应对突发事件。首先,给出了对称信息下供应链协调模型;然后,研究了不对称信息下集权式与分权式供应链的协调机制;再次,在突发事件引起零售商成本分布函数扰动情况下,通过引入供应商由于可能需要调整生产计划而产生的偏差成本,研究了供应链的最优应对策略。 研究表明,供应链的最优生产计划、最优批发价格和最优零售价格均具有一定的鲁棒性,当突发事件造成零售商期望成本在一定范围内发生扰动时,三者可以保持不变,当零售商期望成本扰动超过一定范围内时,则需要对之加以调整,才能有效应对突发事件。最后,通过数值仿真验证了相关结论。     

非正态总体的质量控制图

<正> 产品质量特征的分布,除了正态分布外,还有非正态分布的.例如,象外圓直径和内孔直径一类的分布一般近似于正态分布,象同心度和椭圆度一类摆差的分布通常形成偏态分布.当总体不服从正态分布时,虽然样本平均值的分布比产品尺寸的分布更能符合正态分布,但在样本容量很小的情况下,平均值的分布不是近似正态的.这时必须应用非正态分布的模型配置控制限.非正态分布的理论模型可以从皮尔逊分布系或蒲尔分布系中进行选择.本文的目的是当总体服从皮尔逊 I 型分布时如何制订平均值、中位值、距差和极值控制图,并将这几个图的效率作比较.     

考虑随机投资收益和单边线性索赔的时间依赖更新风险模型的破产概率

本文主要研究一类考虑随机投资收益和相依索赔额的时间依赖的更新风险模型.在该模型中,保险投资收益服从指数Lévy过程,而索赔额服从具有独立同分布步长的单边线性过程.该单边线性过程的步长与索赔到达时间构成独立同分布的随机向量序列,并且该随机向量的分量之间具有运用步长关于索赔到达时间间隔的条件尾概率渐近性刻画的相依关系.当单边线性过程的步长服从重尾分布时,本文得到该更新风险模型破产概率在时间域内的一致渐近估计.     

Optimal replenishment policy for imperfect quality EMQ model with rework and backlogging

This paper derives the optimal replenishment policy for imperfect quality economic manufacturing quantity (EMQ) model with rework and backlogging. The classic EMQ model assumes that all items produced are of perfect quality. However, in real‐life manufacturing settings, generation of imperfect quality items is almost inevitable. In this study, a random defective rate is assumed. All items produced are inspected and the defective items are classified as scrap and repairable. A rework process is involved in each production run when regular manufacturing process ends, and a rate of failure in repair is also assumed. Unit disposal cost and unit repairing and holding costs are included in our mathematical modelling and analysis. The renewal reward theorem is employed in this study to cope with the variable cycle length. The optimal replenishment policy in terms of lot‐size and backlogging level that minimizes expected overall costs for the proposed imperfect quality EMQ model is derived. Special cases of the model are identified and discussed. Numerical example is provided to demonstrate its practical usage. Copyright © 2006 John Wiley & Sons, Ltd.     

Economic production quantity with the presence of imperfect quality and random machine breakdown and repair based on the artificial bee colony heuristic

This article considers a production-inventory system consisting of a single imperfect unreliable machine. The items manufactured by the system are either perfect items or imperfect items, which require a rework to be restored to perfect quality. The rework rate is permitted to be different from the production rate if the rework process is different from the main manufacturing process. The fraction of the number of imperfect items is random following a general distribution function. The time to failure of the machine is random, following a general distribution function. If the machine fails before the lot is completed, the production is interrupted and the machine repair is started immediately. A random machine repair time is assumed, with a general distribution function. Unlike a common assumption in the literature, after the repair of the machine is completed, the production resumes. During the machine repair, a shortage can occur. A single-variable expected average cost function is derived to find the optimal lot size. Because of the complexity in the model, the ABC heuristic is proposed and implemented to find a near optimal value for the lot size. The article also provides a sensitivity analysis of the model's key parameters. It has been observed that the lot interruption-resumption policy leads to smaller lot sizes.     

Control policies for single-stage production systems with perishable inventory and customer impatience

We consider problems of inventory and admission control for make-to-stock production systems with perishable inventory and impatient customers. Customers may balk upon arrival (refuse to place orders) and renege while waiting (withdraw delayed orders) during stockouts. Item lifetimes and customer patience times are random variables with general distributions. Processing, setup, and customer inter-arrival times are however assumed to be exponential random variables. In particular, the paper studies two models. In the first model, the system suspends its production when its stock reaches a safety level and can resume later without incurring any setup delay or cost. In the second model, the system incurs setup delays and setup costs; during stockouts, all arriving customers are informed about anticipated delays and either balk or place their orders but cannot withdraw them later. Using results from the queueing literature, we derive expressions for the system steady-state probabilities and performance measures, such as profit from sales and costs of inventory, setups, and delays in filling customer orders. We use these expressions to find optimal inventory and admission policies, and investigate the impact of product lifetimes and customer patience times on system performance.     

A production–inventory model in an imperfect production process

The paper develops a model to determine the optimal product reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process. The basic assumption of the classical Economic Manufacturing Quantity (EMQ) model is that all manufacturing items are of perfect quality. The assumption is not true in practice. Most of the production system produces perfect and imperfect quality items. In some cases the imperfect quality (non conforming) items are reworked at a cost to restore its quality to the original one. Rework cost may be reduced by improvements in product reliability (i.e., decreasing in product reliability parameter). Lower value of product reliability parameter results in increase development cost of production and also smaller quantity of nonconforming products. The unit production cost is a function of product reliability parameter and production rate. As a result, higher development cost increases unit production cost. The problem of optimal planning work and rework processes belongs to the broad field of production–inventory model which deals with all kinds of reuse processes in supply chains. These processes aim to recover defective product items in such a way that they meet the quality level of ‘good item’. The benefits from imperfect quality items are: regaining the material and value added on defective items and improving the environment protection. In this point of view, a model is introduced here to guide a firm/industry in addressing variable product reliability factor, variable unit production cost and dynamic production rate for time-varying demand. The paper provides an optimal control formulation of the problem and develops necessary and sufficient conditions for optimality of the dynamic variables. In this purpose, the Euler–Lagrange method is used to obtain optimal solutions for product reliability parameter and dynamic production rate. Finally, numerical examples are given to illustrate the proposed model.     

Determining the optimal run time for EPQ model with scrap,rework, and stochastic breakdowns

This paper is concerned with determination of optimal run time for an economic production quantity (EPQ) model with scrap, rework, and stochastic machine breakdowns. In real life manufacturing systems, generation of defective items and random breakdown of production equipment are inevitable. In this study, a portion of the defective items is considered to be scrap, while the other is assumed to be repairable. Total production-inventory cost functions are derived respectively for both EPQ models with breakdown (no-resumption policy is adopted) and without breakdown taking place. These cost functions are integrated and the renewal reward theorem is used to cope with the variable cycle length. Theorems on conditional convexity of the integrated overall costs and bounds of the production run time are proposed and proved. We conclude that the optimal run time falls within the range of bounds and it can be pinpointed by the use of the bisection method based on the intermediate value theorem. Numerical example is provided to demonstrate its practical usages.     

On a production-inventory system of deteriorating items subject to random machine breakdowns with a fixed repair time

This paper considers the impact of random machine breakdowns on the classical Economic Production Quantity (EPQ) model for a product subject to exponential decay and under a no-resumption (NR) inventory control policy. A product is manufactured in batches on a machine that is subject to random breakdowns in order to meet a constant demand over an infinite planning horizon. The product is assumed to have a significant rate of deterioration and time to deterioration is described by an exponential distribution. Also, the time-to-breakdown is a random variable following an exponential distribution. Under the NR policy, when a breakdown occurs during a production run, the run is immediately aborted. A new run will not be started until all available inventories are depleted. Corrective maintenance of the production system is carried out immediately after a breakdown and it takes a fixed period of time to complete such an activity. The objective is to determine the optimal production uptime that minimizes the expected total cost per unit time consisting of setup, corrective maintenance, inventory carrying, deterioration, and lost sales costs. A near optimal production uptime is derived under conditions of continuous review, deterministic demand, and no shortages.     

Supply chain scheduling in a collaborative manufacturing mode: model construction and algorithm design

In collaborative manufacturing, the supply chain scheduling problem becomes more complex according to both multiple product demands and multiple production modes. Aiming to obtain a reasonable solution to this complexity, we analyze the characteristics of collaborative manufacturing and design some elements, including production parameters, order parameters, and network parameters. We propose four general types of collaborative manufacturing networks and then construct a supply chain scheduling model composed of the processing costs, inventory costs, and two penalty costs of the early completion costs and tardiness costs. In our model, by considering the urgency of different orders, we design a delivery time window based on the least production time and slack time. Additionally, due to the merit of continuously processing orders belonging to the same product type, we design a production cost function by using a piecewise function. To solve our model efficiently, we present a hybrid ant colony optimization (HACO) algorithm. More specifically, the Monte Carlo algorithm is incorporated into our HACO algorithm to improve the solution quality. We also design a moving window award mechanism and dynamic pheromone update strategy to improve the search efficiency and solution performance. Computational tests are conducted to evaluate the performance of the proposed method.

     

Economic Production Lot Size Model with Variable Production Rate and Imperfect Quality

The classical economic production lot size (EPL) model assumes a constant production rate that is predetermined and inflexible, and perfect quality. Recent models have removed the assumption of perfect quality while maintaining the inflexible production rate assumption. Production rates in many cases, such as orders filled by a machine, can be changed. Moreover, unit production cost and process quality depend on the production rate. In this paper, we extend the EPL model to cases where the production rate is a decision variable. Unit production cost becomes a function of the production rate. Also, the quality of the production process deteriorates with increased production rate. We solve the proposed model for special cost and quality functions and illustrate the results with a numerical example. The results show that, for cases where increases in the production rate lead to a significant deterioration in quality, the optimal production rate may be smaller than the rate that minimizes unit production cost. For cases where quality is largely independent of the production rate, the optimal production rate may be larger than the rate that minimizes unit production cost.     

基于产品合格率随机分布的投产量模型优化

当产品合格率随机分布时,建立起以投产综合损失费(欠产再投产费,过量产出费和不合格处理费)期望值为目标函数,投产量为决策变量的投产量模型。结合产品合格率随机分布不同(均匀分布和正态分布)推导求解了模型的最优解,得出了产量与投产量的等式关系,据此可有效地指导生产。最后通过数值实例分析验证了模型的有效性。     

Human resources scheduling to improve the product quality according to exhaustion limit

The role of human resources in manufacturing systems is very significant, and without efficient human resources we encounter high-price products with low quality. To improve the efficiency of human resources, we need to provide an optimal working schedule for each worker in production period. In this paper, we proposed a mixed-integer nonlinear model to find the best working schedule based on product quality cost and workers reliability. In this model, if the worker’s exhaustion level reaches a specific limit, the worker can rest to increase his reliability level and an accommodator should work instead of him. Since the proposed model is NP-hard, we used an artificial immune system to provide the best working schedule. The results indicate that this model can provide efficient and effective human resources schedule in manufacturing systems.