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This study introduces one modeling methodology that describes a broad range of multiple stage production planning issues, including multiple limited resources with setup times and joint fixed cost relationships. An existing production system is modeled in this fashion, creating a new set of 1350 highly generalized benchmark problems. A computational study is conducted with the 1350 benchmark problems introduced in this paper and 2100 benchmark problems, with more restrictive assumptions, from the existing literature. The relative merits of a decomposition‐based algorithm and a neighborhood search technique known as NIPPA, or the Non‐sequential Incremental Part Period Algorithm, are assessed. NIPPA is generally the more successful of the two techniques, although there are specific instances in which the decomposition‐based algorithm displayed a distinct advantage. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005 相似文献
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We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing. Given multiple demand scenarios together with associated probabilities, our aim is to identify a set of tools that is a good compromise for all these scenarios. More precisely, we formulate a mixed‐integer program in which expected value of the unmet demand is minimized subject to capacity and budget constraints. This is a difficult two‐stage stochastic mixed‐integer program which cannot be solved to optimality in a reasonable amount of time. We instead propose a heuristic that can produce near‐optimal solutions. Our heuristic strengthens the linear programming relaxation of the formulation with cutting planes and performs limited enumeration. Analyses of the results in some real‐life situations are also presented. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005. 相似文献
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In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010 相似文献
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陈伟 《中国人民武装警察部队学院学报》2011,(12):37-38
简要阐述了石化企业设置火灾事故情况下事故应急水池的必要性,并对事故应急水池的计算方法进行分析,最后结合工程实例进行容量计算,对同类的设计提供一定的参考。 相似文献
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随着国家西部大开发战略的实施,新疆生产建设兵团(以下简称兵团)的经济发展速度开始加快,人口增长与兵团资源的矛盾也逐渐凸现,研究兵团人口承载力问题就迫在眉睫。本文以数量经济学的理论和思想为指导,主要采用灰色系统理论、Logistic模型等定量分析方法对兵团人口、经济、资源及可持续发展进行研究。 相似文献
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The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two-echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic production capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Finally, we extend the model to the two-stage two-ordering case and derive the optimal ordering policy by dynamic programming. We compare the single-ordering and two-ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic. We also explore the transparent supply chain and find that our main results still hold. 相似文献
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郑瑞林 《海军工程大学学报》1992,(3)
本文由热力学理论定量指出,只要两相平衡的饱和蒸气比热为负,饱和蒸气经绝热膨胀后成过饱和蒸气,并讨论了饱和蒸气比热为负的条件。 相似文献
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We consider a supply chain in which a retailer faces a stochastic demand, incurs backorder and inventory holding costs and uses a periodic review system to place orders from a manufacturer. The manufacturer must fill the entire order. The manufacturer incurs costs of overtime and undertime if the order deviates from the planned production capacity. We determine the optimal capacity for the manufacturer in case there is no coordination with the retailer as well as in case there is full coordination with the retailer. When there is no coordination the optimal capacity for the manufacturer is found by solving a newsvendor problem. When there is coordination, we present a dynamic programming formulation and establish that the optimal ordering policy for the retailer is characterized by two parameters. The optimal coordinated capacity for the manufacturer can then be obtained by solving a nonlinear programming problem. We present an efficient exact algorithm and a heuristic algorithm for computing the manufacturer's capacity. We discuss the impact of coordination on the supply chain cost as well as on the manufacturer's capacity. We also identify the situations in which coordination is most beneficial. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008 相似文献