基于投产点库存的随机综合生产计划模型研究

以企业基于投产点法的生产与库存控制策略为研究起点,分析了随机需求条件下生产系统服务水平与库存水平的数量关系,并将产品在计划期间的平均库存量引入综合生产计划模型。模型中计划期产量是与产品需求具有相同分布的随机变量,模型的优化目标是通过确定最佳的投产库存量和生产系统服务水平,求得相应的计划期产量区间。提出了模型的计算机辅助求解算法,并采用案例分析验证了模型的有效性。     

考虑替代具有两类产品返回的随机回收库存管理

本文考察了离散时间下两类产品的回收系统的库存管理.对具有确定量的外部需求、库存容量无限、缺货有惩罚及产品处理为收益情况下,建立了马尔可夫决策模型.然后利用马尔可夫决策理论,分析了模型的性质,得到了区间有限情况下总费用最少的最优决策方法.     

一种考虑原材料库存成本的变质物品EPQ模型

在库存管理研究中,单独实现产成品或原材料库存成本最小,往往无法获得整个库存系统的最优控制策略.本文提出了一种同时考虑产成品和原材料库存成本的变质物品EPQ集成模型.运用迭代寻优法得到模型最优解,得出计划期内最优原材料订购次数,原材料订购周期内的最优生产次数和最优服务水平,并对主要参数进行了灵敏度分析,为生产制造企业的库存管理提供了决策依据.     

混合分销渠道结构下的供应链库存策略优化

结合合约设计分析混合分销渠道结构下的两级供应链库存策略优化问题,是我们研究的重点。首先,通过构建模型,比较分析了线性转移支付合约和收益共享合约在实现供应链协调及收益分配方面的功能差异;其次,通过数值分析探讨了市场需求不确定性下模型的变化规律;最后得出管理启示,线性转移支付合约模型在实现供应链库存协调和Pareto改进上功能显著,却不能合理分配供需双方的收益,随着市场需求风险增加,生产商将提高零售商最低销售量,零售商将增加最优库存量;收益共享舍约在合理分配供需双方收益上功能显著,但供应链整体收益处于次优状态,生产商分享的收益越多,零售商的最优库存量随市场需求风险增加而递减的速度就越快。     

带学习效应的双渠道供应链库存策略研究

针对具有学习行为的双渠道供应链问题,本文研究了两种分销渠道并存下的最优库存策略。有限计划期内,分销商通过传统销售和在线销售来满足下游顾客的需求。两种分销渠道下的销售单价为时变不减线性函数,当系统中各周期的生产订购固定成本以一定的概率具有学习效应行为时,分别建立了非变质产品生产存贮问题的混合整数约束优化模型以及易变质产品存贮问题的无约束混合整数优化模型,所建立模型的目标为极大化分销商总利润函数。对于这两类模型,通过分析其最优解的性质,利用将生产订购次数松弛为连续变量的技巧证明了最优解存在的唯一性。给出了最优策略的求解方法并比较了两类模型最优利润函数值的大小。最后通过数值算例对上述模型进行了验证,数值结果表明当供应链系统中存在学习效应行为时,该系统能够获得更多的利润。     

考虑故障停机的生产控制与维修计划联合决策模型

通过生产控制与维修计划协同决策,降低生产成本。首先描述生产过程,分析各项费用。其次,建立了考虑生产过程失控、故障率和故障停时间的生产过程控制、生产计划优化和维修管理联合优化决策的模型。通过模型求解,联合制定出生产过程检查策略、生产计划（经济生产批量、生产批次）以及维修计划（PM间隔期）,实现单位时间内总费用最低的目标。再次,案例研究,分析生产过程失控、故障率和故障停机时间对于经济生产批量、生产过程检查策略和生产系统维修计划的影响。该模型从理论上解决了生产过程控制、生产计划优化和维修管理联合优化决策难题,对于指导企业制定生产计划和生产系统的检修计划,进而提高产品质量、降低生产成本、确保准时交货都具有指导意义和实用价值。     

流程工业库存优化控制问题

库存对于企业生产的稳定生产和经济运行非常重要,本文讨论了即时制造(JIT)思想,并认为在我国目前的技术条件下完全实现JIT理论上的目标还有困难.真正实现零库存的时机还不成熟,但是我们应该可以将实现零可避免库存作为目标.根据流程工业的库存问题和一般离散制造业的显著不同,本文研究了流程工业的生产特点,基于工艺过程的仿真,将库存视为异类特殊的流程生产单元,集成于化工生产过程的超结构中,提出了一种通用的流程工业库存优化模型.更进一步针对一个氯碱企业中氯乙烯厂的库存问题进行了仿真分析,对所提出的流程工业库存优化模型进行了验证,结果表明该模型是可行的.     

不确定需求环境下多级库存系统优化与协调模型研究

论文首先将模糊随机变量扩展为混合随机变量,并用来描述不确定需求.然后对多级单周期库存系统在模糊随机和混合随机需求环境下的优化与协调问题作了研究,提出了通过集成库存管理使库存系统整体收益期望值最大化的优化模型和协调合作伙伴利益的数量折扣契约模型,并根据遗传算法理论和计算机模拟技术设计了求解模型的智能算法.最后通过实例对模型进行了仿真分析.     

强化学习算法在供应链环境下的库存控制中的应用

以两级库存为例,提出了一种优化库存管理的方法.该方法包含3种技术马尔可夫决策过程、马尔可夫博弈和一种用于求解马尔可夫博弈的强化学习算法.具体操作过程是,将一个两级的库存系统通过马尔可夫博弈建模,再用马尔可夫博弈和强化学习的知识求解该模型,并得出该系统的优化解.     

供应商—零售商联合库存的供应链库存成本分析

联合库存是商业零售企业改善供应链库存管理的有效途径。本文通过对传统供应模式与联合库存模式下库存成本模型的比较分析,发现联合库存模式能够很好地降低整个供应链的库存成本;通过算例分析证明了联合库存降低供应链成本的现实意义。     

灰信息下关键设备生产缓冲Petri网模型研究

关键设备直接关系着产品的生产周期和质量,一定程度上可以认为是离散生产系统的"瓶颈"。因此,需要为关键设备设立一定的生产缓冲来保障其运行的持续性和平稳性。针对灰信息下的生产系统,创新性地定义灰色Petri网并设计其有效算法;在分析关键设备缓冲作用机理的基础上,针对灰信息下关键设备生产缓冲问题,构建关键设备生产缓冲的灰色Petri网模型,结合遗传算法进行仿真运算,探寻其最优解;以某产品生产线为案例,验证模型及算法的有效性和实用性,为生产系统关键设备管理工作提供了一种新的方法和思路。     

Optimal Production and Admission Policies in Make‐to‐Stock/Make‐to‐Order Manufacturing Systems

In this article, we study optimal production and admission control policies in manufacturing systems that produce two types of products: one type consists of identical items that are produced to stock, while the other has varying features and is produced to order. The model is motivated by applications from various industries, in particular, the automobile industry, where a part supplier receives orders from both an original equipment manufacturer and the aftermarket. The product for the original equipment manufacturer is produced to stock, it has higher priority, and its demands are fully accepted. The aftermarket product is produced to order, and its demands can be either accepted or rejected. We characterize the optimal production and admission policies with a partial‐linear structure, and using computational analysis, we provide insights into the benefits of the new policies. We also investigate the impact of production capacity, cost structure, and demand structure on system performance.     

Buffer stock under the effect of fluctuating demand

This paper deals with an inventory situation where the rate of demand for units stocked is known, but fluctuating over time. The focus of attention is on reserve stock. In an ideal world, where demand is known well in advance and where suppliers maintain scheduled shipping dates, there would be little need to hold any form of inventory other than a limited in-process stock. In practice however, supply interruptions are likely to occur. In this case an inventory acts as buffer to withstand variations between supply and demand. A mathematical model to determine the optimum reserve stock needed to minimize the expected total inventory cost of carrying the reserve inventory and cost of stockouts is developed.     

Joint modified block replacement and production/inventory control policy for a failure-prone manufacturing cell

This paper considers a joint preventive maintenance (PM) and production/inventory control policy of an unreliable single machine, mono-product manufacturing cell with stochastic non-negligible corrective and preventive delays. The production/inventory control policy, which is based on the hedging point policy (HPP), consists in building and maintaining a safety stock of finished products in order to respond to demand and to avoid shortages during maintenance actions. Without considering the impact of preventive and corrective actions on the overall performance of the production system, most authors working in the reliability and maintainability domains confirm that the age-based preventive maintenance policy (ARP) outperforms the classical block-replacement policy (BRP). In order to reduce wastage incurred by the classical BRP, we consider a modified block replacement policy (MBRP), which consists in canceling a preventive maintenance action if the time elapsed since the last maintenance action exceeds a specified time threshold. The main objective of this paper is to determine the joint optimal policy that minimizes the overall cost, which is composed of corrective and preventive maintenance costs as well as inventory holding and backlog costs. A simulation model mimicking the dynamic and stochastic behavior of the manufacturing cell, based on more realistic considerations of the real behavior of industrial manufacturing cells, is proposed. Based on simulation results, the joint optimal MBRP/HPP parameters are obtained through a numerical approach that combines design of experiment, analysis of variance and response surface methodologies. The joint optimal MBRP/HPP policy is compared to classical joint ARP/HPP and BRP/HPP optimal policies, and the results show that the proposed MBRP/HPP outperforms the latter. Sensitivity analyses are also carried out in order to confirm the superiority of the proposed MBRP/HPP, and it is observed that for practitioners, the proposed joint MBRP/HPP offers not only cost savings, but is also easy to manage, as compared to the ARP/HPP policy.     

The Influence of Quality Inspections on the Optimal Safety Stock Level

Due to yields of less than 50% during the production of curved glass for the displays on their new cell phone series, Samsung has to deal with higher than expected production costs of several million dollars. Where there is random yield, production costs as well as holding costs can be reduced by introducing quality inspections, in which defective items are discarded before further production. To achieve the greatest cost savings, it is important to determine the optimal number and positions of these inspections across the production process which, due to several influencing parameters, is not simple. We show how the positions of inspection within a production process influence the safety stock level that is required to buffer against uncertainties due to demand and yield randomness. Our approach is the first one, combining decisions about the number and positions of inspections with inventory control strategies in a warehouse. We achieve a maximum safety stock reduction of more than 30% in our examples, which can be even larger depending on the parameter setting. For a company like Intel, reporting inventories for finished goods of nearly 1.5 billion dollars in the 2014 annual report, this allows for significant savings.     

The effects of transportation lead-time and safety stock in international co-operative knockdown production systems

Abstract

Abstract. In international co-operative knockdown production systems (ICKDPS), much longer transportation lead-time than production lead-time to procure component parts is required, because the parts are generally containerized by ship. In this paper, in order to determine an economical buffer stock level for a more reliable ICKDPS, a pull-type ordering system which considers the backorder for compensating for shortage of component parts is developed for the ICKDPS as a three-stage production, inventory and transportation system with the concept of 'just-in-time’ production systems. In addition, the effects of transportation lead-time and buffer stock level on the quantity of backlogs and the probability of requirement are clarified by simulation.     

Effects of maintenance policies on the productivity of flexible manufacturing cells

Flexible manufacturing cells (FMCs) often operate with increasing failure rate due to extensive utilization and wear-outs of equipment. While maintenance plans can eliminate wear-out failures, random failures are still unavoidable. This paper discusses a procedure that combines simulation and analytical models to analyze the effects of corrective, preventive, and opportunistic maintenance policies on productivity of a flexible manufacturing cell. The production output rate of an FMC, which is a function of availability, is determined under different maintenance policies and mean time between failures.     

考虑质量相关性需求与完全预防性维修的EPQ决策

在经典EPQ模型基础上，考虑质量相关性需求和周期性完全预防性维修策略，以单位时间利润最大化为目标，构建经济生产批量的决策模型。在企业单位时间利润模型的构建中，结合质量相关性需求以及设备退化这一实际问题，进行动态预防性维修成本、恢复成本、次品修复成本以及产品需求率的构建。考虑目标函数的复杂性，本文采用遗传算法对模型进行数值求解，并通过与不考虑质量相关性需求的EPQ决策的比较分析，验证其合理性。     

Release of maintenance jobs in a decentralized multi-stage production/maintenance system with continuous condition monitoring

Condition-based maintenance is analyzed for multi-stage production with a separate maintenance department. It is assumed that the conditions of the machines deteriorate as a function of multiple production parameters and that the task of maintenance is to keep up predefined operational availabilities of the individual machines. In this context the problem of determining the optimal machine condition that triggers the release of a preventive maintenance job and the problem of scheduling maintenance jobs at the maintenance department arise. Existing approaches to solve these problems either assume a monolithic production/maintenance system or concentrate on a decentralized system in which the information flow and resource transfer do not cause delays. With our paper we aim at (1) deriving a triggering mechanism that is able to cope with relaxed assumptions and at (2) developing specific priority rules for scheduling maintenance jobs. Therefore, in this paper a specific continuous condition monitoring and a suitable information exchange protocol are developed, factors determining the release situation are operationalized, impacts of choosing the triggering conditions are identified and the components of specific priority rules for scheduling maintenance jobs are clearly elaborated. Finally the performance of the resulting solution approach is analyzed by simulations. Thereby, relevant characteristics of the production/maintenance system, the maintenance task and relevant priority rules are varied systematically. This research contributes answers to the questions on how the exchange of local information can be structured, the parameters of condition-based maintenance can be set and on what maintenance-specific priority rules can be applied in case of incomplete information about deterioration in a decentralized multistage production/maintenance system.     

Optimal maintenance intervals for a multi-component system

In the manufacturing industry, preventive maintenance (PM) is carried out to minimise the probability of plant unexpected breakdown. Planned PM is preferred as disruption to operation is then minimised. Suggested PM intervals are normally prepared by the original equipment manufacturers (OEMs), however due to the multifaceted relationship between operating context and production requirement for different plants, it is unlikely that these suggested intervals as prescribed by the OEMs are optimal. Reliability, budget and breakdown outages cost are some of the critical factors that will affect the calculation of optimal maintenance intervals. Maintenance managers are required to determine optimal maintenance intervals with the above different requirements set by management. In this paper three models are proposed to calculate optimal maintenance intervals for multi-component system in a factory subjected to minimum required reliability, maximum allowable budget and minimum total cost. Numerical examples are provided to illustrate the application and usefulness of the proposed models.