Weibull分布变质物品库存模型研究

研究了变质物品在考虑资金时间价值时的经济订货批量问题.假定物品的变质率服从两参数的Weibull分布,物品的需求率与库存水平有关,且为库存水平的线性函数,计划时域内进行多次订货,订货时间间隔相等,允许缺货且短缺量完全拖后,以库存系统的总费用最小为目标函数,建立了变质物品在存货依赖性需求下考虑资金时间价值时的最优订货批量模型,分析了模型存在唯一的最优解的必要条件,并且给出了在该条件下求解模型最优解的算法,最后给出了一个计算实例.     

基于延期支付的非立即变质物品的库存模型

一类短质期变质产品,如生鲜蔬菜、水果等,增加其库存展示量能为顾客提供更多的拣选机会,从而刺激顾客增加购买量.对此,在供应商给予零售商固定延期支付期限且在综合分析已有变质物品的库存模型的基础上,建立了一个更能准确反映当前存货水平和需求率的库存模型.模型中以零售商的年总费用最小为目标讨论了模型的最优解,通过模型的分析求解,得到零售商的最优订货周期及最优订货量的简单判定方法.通过具体算例,结合灵敏度分析,分别分析了物品的固定保鲜期、物品的变质率及供应商给与零售商的延期支付期限对零售商最优订货策略的影响.     

具有有效期易变质品的最优补货策略研究

易变质物品库存管理策略一直是实务界与学术界高度关注的问题,其中具有有效期易变质物品的最优补货策略是研究中的难点。本文研究了有限销售时域内具有有效期易变质物品的库存补货策略,在物品的需求率依赖于物品的库存水平,且物品有效期已知的假设下,本文建立了一个确定易变质物品最优补货策略的优化模型,并给出了具体的求解算法。文章最后给出了数值算例,并对模型中的相关参数作了敏感性分析。     

基于多折扣价格报价的库存与运输整合优化模型

对一个分销中心、多个销售商的两级供应链,在不允许缺货且需求随机的情形下,考虑常见的多折扣价格报价形式,建立库存与运输整合优化模型,并设计出"两阶段法"求解模型.第1阶段分别求解各销售商的最优订货量和最优折扣,称为配货阶段;第2阶段称为送货阶段,即将前一阶段得到的各销售商的最优订货量分为整车量与零担量.借鉴VMI策略对销售商采取整车直接发运与零担量联合运输相结合的方式,得到最优补货量及补货所需最佳车辆数.给出算例,表明该方法可进一步降低库存与运输费用.     

梯形需求缺货部分拖后的变质物品EOQ模型

研究了变质物品在一般梯形需求率下和缺货部分拖后时的库存模型.假定变质物品的需求为一般形式的梯形需求率函数,允许缺货且缺货部分拖后,缺货拖后率是到达下一周期补货等待时间的凹函数,物品的变质率为一般形式的Weibull时变函数,根据一般梯形需求函数不同的参数取值,建立了不同情形下变质物品的库存决策模型,给出了每种情形下模型的最优订货策略.研究结果表明,库存系统最优库存减少为零的时刻不依赖于具体的需求参数,但库存系统的最优订货策略却依赖于具体的需求参数.     

一类变质物品的最优存贮策略

文章研究了一类变质性物品的最优存贮问题，假定需求率是一个变量，且依赖于库存水平，在瞬时到货和常数变质率的前提下，就允许缺货情形，建立了库存模型，模型解的存在性和唯一性，找到全局最优解，就得到了最优订货策略的求解方法。     

变质物品在存货影响销售率且需求和采购价均为时变时的EOQ模型

在需求和采购价均为时变函数的EOQ模型基础上,考虑物品变质和存货影响销售率现象对库存管理补充策略的影响,建立了相应的EOQ模型,由模型的最优解分析知,库存系统的总成本函数在给定条件下是服务水平的凸函数。运用Mathematica 4．1版软件进行数值仿真和灵敏度分析,表明最优解存在且对库存系统的各种参数具有不同的敏感性。     

短缺量拖后率与价格折扣相关变质品VMI模型

考虑短缺量拖后率与订货商给予顾客的价格折扣正线性相关,提出一种需求指数时变的变质物品供应商管理库存模型,研究了供应链最优库存策略。数值仿真和主要参数灵敏度分析表明,订货商在缺货期间向顾客提供价格折扣有利于降低丢单损失和系统库存总成本;当需求增长因子变化时,VMI系统应保持库存控制策略不变;当变质系数和拖后率上限变大时,VMI系统则应保持订货商补货次数和价格折扣不变,同时适当降低订货商服务水平。     

组合折扣条件下带有可变提前期的库存策略

为了有效地分析随机需求条件下库存策略,在连续库存补货策略中考虑运输和采购的组合折扣且进货提前期为可变情形,建立订单量、再订货点和进货提前期优化决策模型,并设计模型求解的有效算法.最后通过算例,验证模型的有效性和实用性,并就考虑运输与不考虑运输条件下的库存策略进行了比较分析,就模型主要参数对订单量、再订货点、进货提前期和...     

价格不确定情形的原材料滚动采购策略模型

针对价格不确定情形的原材料采购问题,描述了原材料滚动采购策略的基本思想,在此基础上,针对原材料采购计划周期的每个采购时刻点,在未来多期原材料价格预测结果的基础上,综合考虑原材料成本、一次性采购费用和库存费用,构建了使采购综合成本最小的多阶段最优采购模型,并通过模型求解可确定当期的原材料最优采购量。通过B公司原材料镍采购的实例分析说明了给出采购策略模型的可行性和有效性。     

基于约束和邻域的炼钢连铸重调度算法

针对机器故障扰动,研究了炼钢连铸重调度问题及其求解算法。通过将机器故障映射为资源约束,建立了基于动态约束满足的炼钢连铸重调度模型,模型以最大化连浇量与调度方案相似度为目标。针对问题的模型及其特点,采用约束满足和邻域搜索相结合的混合算法对其进行了求解。仿真实验表明本文提出的模型和算法是有效的。     

Model selection and optimal sampling in high-throughput experimentation

The practical difficulties encountered in analyzing the kinetics of new reactions are considered from the viewpoint of the capabilities of state-of-the-art high-throughput systems. There are three problems. The first problem is that of model selection, i.e., choosing the correct reaction rate law. The second problem is how to obtain good estimates of the reaction parameters using only a small number of samples once a kinetic model is selected. The third problem is how to perform both functions using just one small set of measurements. To solve the first problem, we present an optimal sampling protocol to choose the correct kinetic model for a given reaction, based on T-optimal design. This protocol is then tested for the case of second-order and pseudo-first-order reactions using both experiments and computer simulations. To solve the second problem, we derive the information function for second-order reactions and use this function to find the optimal sampling points for estimating the kinetic constants. The third problem is further complicated by the fact that the optimal measurement times for determining the correct kinetic model differ from those needed to obtain good estimates of the kinetic constants. To solve this problem, we propose a Pareto optimal approach that can be tuned to give the set of best possible solutions for the two criteria. One important advantage of this approach is that it enables the integration of a priori knowledge into the workflow.     

产业集群内企业创新与模仿的博弈分析

针对产业集群内企业在研发中创新和模仿的策略选择问题,建立了产业集群内企业创新和模仿的博弈模型,分不同的时间区间计算出企业在创新和模仿策略下的收益,解出了企业创新选择的混合策略解,从而为产业集群内企业技术创新的战略选择提供了理论参考,并对进一步加强产业集群的创新动力提出了政策性建议。     

Optimisation of multi-stage JIT production-pricing decision: centralised and decentralised models and algorithms

This research studies the optimal decision for product pricing, production lot sizing in a multi-stage serial just-in-time production system with kanban-controlled policy. A decentralised decision model and a centralised decision model of this problem are formulated as a mixed-integer nonlinear programming problem. In order to solve the models, three algorithms are developed. The first one is an approximate procedure which solves the decentralised decision model; the second one is a proximate optimal procedure using two-phase search technique that solves the centralised decision model, and the third one is an approximate method using meta-heuristic technique which is used for both decentralised and centralised models. Numerical example shows that centralised decision can obtain higher economic benefit with lower cost and higher revenue and profit. Meanwhile, when demand is more price sensitive, centralised decision can achieve significant profit enhancement. Computational results attribute to different characteristics of the problem and solution superiority.     

The evolution of family level sales forecasts into product level forecasts: Modeling and estimation

It is frequently the case that sales forecasts are available at the detailed product level for only a relatively short time horizon. For the rest of the forecast horizon, only aggregate sales forecasts at the product family level are available. The problem addressed in this paper is how to fit a forecast simulation model to a history of these aggregate and disaggregate forecasts. Our approach to develop such a model is to combine a forecast update model with a forecast disaggregation model. The forecast update model is called the Martingale model of forecast evolution. The parameters of the two models must be estimated from historical forecast data. It is this statistical parameter estimation problem that occupies the major part of our investigation. We recommend an estimation technique based on the method of moments.     

Minimizing work-in-process and material handling in the facilities layout problem

We consider the plant layout problem for a job shop environment. This problem is generally treated as the quadratic assignment problem with the objective of minimizing material handling costs. Here we investigate the relationship between material handling costs and average work-in-process. Under restrictive assumptions, an open queueing network model can be used to show that the problem of minimizing work-in-process reduces to the quadratic assignment problem. In this paper, we generalize these results through a simulation model, and develop a simple secondary measure which allows us to select the layout that minimizes average work-in-process levels from among solutions that are similar with respect to the objective function for the quadratic assignment problem.     

Dynamic rationing and ordering policies for multiple demand classes

In this paper, we study the dynamic rationing problem for multiple demand classes with Poisson demands. We first consider a multi-period problem with zero lead time and show that the optimal rationing and ordering policies are, respectively, the dynamic rationing policy and the base stock policy. We then extend this model to a non-zero lead time and show that there is no simple optimal structure for this extended problem. A myopic policy and a lower bound are proposed. The numerical results show that the dynamic rationing policy outperforms the static rationing policy and its performance is very close to the optimal policy under a wide range of operating conditions.     

Fleet management models and algorithms for an oil-tanker routing and scheduling problem

This paper explores models and algorithms for routing and scheduling ships in a maritime transportation system. The principal thrust of this research effort is focused on the Kuwait Petroleum Corporation (KPC) Problem. This problem is of great economic significance to the State of Kuwait, whose economy has been traditionally dominated to a large extent by the oil sector, and any enhancement in the existing ad-hoc scheduling procedure has the potential for significant savings. A mixed-integer programming model for the KPC problem is constructed in this paper. The resulting mathematical formulation is rather complex to solve due to the integrality conditions and the overwhelming size of the problem for a typical demand contract scenario. Consequently, an alternate aggregate model that retains the principal features of the KPC problem is formulated. The latter model is computationally far more tractable than the initial model, and a specialized rolling horizon heuristic is developed no solve it. The proposed heuristic procedure enables us to derive solutions for practical sized problems that could not be handled by directly solving even the aggregate model. The initial formulation is solved using CPLEX-4.0-MIP capabilities for a number of relatively small-sized test cases, whereas for larger problem instances, the aggregate formulation is solved using CPLEX-4.0-MIP in concert with the developed rolling horizon heuristic, and related results are reported. An ad-hoc routing procedure that is intended to simulate the current KPC scheduling practice is also described and implemented. The results demonstrate that the proposed approach substantially improves upon the results obtained using the current scheduling practice at KPC.     

Computational method for optimal machine scheduling problem with maintenance and production

This paper considers an optimal scheduling problem of maintenance and production for a machine. Firstly, the problem is formulated as a stochastic switched impulsive optimal control problem. However, there exists the stochastic disturbance in this model. Thus, it is difficult to solve the problem by conventional optimisation techniques. To overcome this difficulty, the stochastic switched impulsive optimal control problem is transformed into a deterministic switched impulsive optimal control problem with continuous state inequality constraints. Then, by combining a time-scaling transformation, a second-order smoothing technique and a penalty function method, an improved Newton algorithm is developed for solving this problem. Convergence results indicate that the algorithm is globally convergent with quadratic rate. Finally, two numerical examples are provided to illustrate the effectiveness of the developed algorithm.     

Makespan minimisation on parallel batch processing machines with non-identical job sizes and release dates

The problem we study in this paper arises from the washing step of hospital sterilisation services. Washers in the washing step are capable of handling more than one medical device set as long as their capacity is not exceeded. The medical device set sizes and arrival times to the sterilisation service may be different, but they all have the same washing duration. Thus, we model the washing step as a batch scheduling problem where medical device sets are treated as jobs with non-identical sizes and release dates, but equal processing times. The main findings we present in this paper are the following. First, we study two special cases for which polynomial algorithms are presented. We then develop a 2-approximation algorithm for the general problem. Finally, we develop a MILP model and compare it with another MILP model from the literature. Computational results show that our MILP model outperforms the model from the literature.