需求不确定情况下的多周期生产批量决策

针对在市场需求不确定的情况下的中小制造型企业的生产批量决策优化问题进行研究,根据多周期生产情况下需求的不确定性,综合考虑缺货成本、库存成本和期初库存等因素,以多生产周期的总利润最大化为目标,建立生产批量决策模型,通过优化分析,得出其利润最大化下的最优生产批量,并通过敏感性分析讨论最优批量与多周期生产条件下的不确定需求等影响因素之间的关系.     

随机需求下考虑半成品库存的多周期生产决策优化

为了更好地应对需求的不确定性,在需求实现之前,企业既可以生产成品直接满足需求,亦可生产部分半成品,在观察到实际需求之后短时间内迅速完成剩余生产环节以满足需求。未加工的半成品和未售出的成品可用于满足后续周期的需求。作为一种提高生产灵活性的手段,分阶段生产的方式会产生更高的成本。企业需要在成本和灵活性之间作出权衡,优化生产决策。模型通过动态规划的方法,研究需求不确定情况下考虑半成品库存的多周期生产决策问题,通过分析目标函数以及最优值函数的结构性质,推导出最优的多周期生产策略为修正的目标库存策略,并且分析了不同参数对最优策略的影响。     

联合订货下基于ø-散度的多产品库存鲁棒优化模型

针对多产品联合库存决策问题,在市场需求不确定条件下,建立了考虑联合订货成本的多产品库存鲁棒优化模型。针对不确定市场需求,采用一系列未知概率的离散情景进行描述,给出了基于最小最大准则的鲁棒对应模型,并证明了(s,S)库存策略的最优性。进一步,在仅知多产品市场需求历史数据基础上,采用基于ø-散度的数据驱动方法构建了满足一定置信度要求的关于未知需求概率分布的不确定集。在此基础上,为获得(s,S)库存策略的相关参数,运用拉格朗日对偶方法将所建模型等价转化为易于求解的数学规划问题。最后,通过数值计算分析了Kullback-Leibler散度和Cressie-Read散度以及不同的置信水平下的多产品库存绩效,并将其与真实分布下应用鲁棒库存策略得到的库存绩效进行对比。结果表明,需求分布信息的缺失虽然会导致一定的库存绩效损失,但损失值很小,表明基于文中方法得到的库存策略能够有效抑制需求不确定性扰动,具有良好的鲁棒性。     

一种不确定需求下库存定价模型的鲁棒优化方法

本文研究了不确定需求下一种多阶段定价与库存控制相协调的供应链模型,把扰动参数的不确定性处理为在已知集合内变化扰动. 针对不确定参数取值的几种集合情况下,应用鲁棒优化技术将不确定库存一定价模型转化为可求解的鲁棒模型. 最后进行了数值实验,对结果进行了分析.     

基于多Agent的Supply-Hub库存策略建模与仿真研究

Supply-Hub是一种能较好地解决装配式供应链协同运作问题的供应链运作模式,其库存参数设置对于平衡上游供应商与下游制造商之间的利益,吸弓l供应商加入Supply-Hub非常关键.建立了由两个供应不同零部件的供应商向制造商补货时的供应商多周期成本模型,采用多Agent仿真方法,研究了三种情形下(s,S)库存策略参数的变动对供应商及制造商的影响,以期为Supply-Hub运营商合理确定库存策略参数提供一定参考.     

基于多Agent的Supply-Hub库存策略建模与仿真研究北大核心

Supply-Hub是一种能较好地解决装配式供应链协同运作问题的供应链运作模式,其库存参数设置对于平衡上游供应商与下游制造商之间的利益,吸弓l供应商加入Supply-Hub非常关键.建立了由两个供应不同零部件的供应商向制造商补货时的供应商多周期成本模型,采用多Agent仿真方法,研究了三种情形下(s,S)库存策略参数的变动对供应商及制造商的影响,以期为Supply-Hub运营商合理确定库存策略参数提供一定参考.     

基于数量折扣合同和缺货损失的EOQ模型的改进

经济订购批量模型假定需求率、单位持有成本、订货成本为常数下得到总成本最低的订购批量,这些参数常数化的假设在现实中通常难以满足.假定需求和订货费为不确定的、库存成本包括年固定成本(与订货量无关)和年可变成本(与订货量有关),用三角模糊数表示年需求和订货费,通过引入数量折扣合同来量化单位产品进价,分别在不允许缺货和考虑缺货损失两种情况下得到最佳订货量.最后的算例表明了模型的合理性.     

医院医疗器械—麻醉包库存策略

麻醉包对于医院来说是一种重要的医疗物品,具有需求量大,一旦缺货则损失严重等特点,为此研究了黑龙江省某医院麻醉包的库存问题.以存储费用为标准来评价和优选库存策略,共建立了四大库存模型,分别是经济批量EOQ库存模型;需求离散条件下多周期有准备成本库存模型;需求服从正态分布条件下多周期有准备成本库存模型和蒙特卡罗仿真库存控制模型.其中,三个模型得出了结果,通过证明,一个模型不符合医院实际情况.通过比较上述三个模型求得的最优化库存策略与医院实际使用的库存策略,建议采取蒙特卡罗仿真库存控制模型求解得出的结果作为医院方最优库存策略.     

油库选址决策的二层优化模型研究

油库物流设施的选址决策属于战略管理层的决策,它是整个物流系统运作的基础.油库的选址决策是库存和运输决策的基础,库存决策和运输决策则是在整个油品配送系统规划框架内对油库选址决策的完善和延伸.如何进行科学的油库选址是企业所关心的问题.本文将在考虑影响选址的定性成本因素以及运营过程中的非成本因素、定性和定量相结合的基础上利用多层决策理论,将油库选址问题归结为一个二层优化模型,并提出了确定理想选址区域和可行点的相关方法的计算方法.数值实验表明该算法是可行的.本文的结论对实际决策问题具有一定的借鉴作用.     

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.     

Profit maximization in an inventory system with time-varying demand,partial backordering and discrete inventory cycle

In this paper, an inventory problem where the inventory cycle must be an integer multiple of a known basic period is considered. Furthermore, the demand rate in each basic period is a power time-dependent function. Shortages are allowed but, taking necessities or interests of the customers into account, only a fixed proportion of the demand during the stock-out period is satisfied with the arrival of the next replenishment. The costs related to the management of the inventory system are the ordering cost, the purchasing cost, the holding cost, the backordering cost and the lost sale cost. The problem is to determine the best inventory policy that maximizes the profit per unit time, which is the difference between the income obtained from the sales of the product and the sum of the previous costs. The modeling of the inventory problem leads to an integer nonlinear mathematical programming problem. To solve this problem, a new and efficient algorithm to calculate the optimal inventory cycle and the economic order quantity is proposed. Numerical examples are presented to illustrate how the algorithm works to determine the best inventory policies. A sensitivity analysis of the optimal policy with respect to some parameters of the inventory system is developed. Finally, conclusions and suggestions for future research lines are given.

     

Inventory Positioning in Multiple Product Supply Chains

In this paper we address the problem of inventory positioning, i.e., the determination of the supply chain node where inventory should be held, to minimize holding costs given a pre-specified order fill rate. A single-echelon inventory system with multiple products models the problem. The value of inventory is assumed to be an increasing function of the amount of processing performed at upstream nodes, while achieved fill-rates are dependent on the distance or time between the inventory storage and customer locations. We propose a novel analytical approach to solve the problem for the case of normally distributed demand that is based on iterative calculations of inventory holding costs at the various potential inventory locations.     

Modeling inventory routing problems in supply chains of high consumption products

Given a distribution center and a set of sales-points with their demand rates, the objective of the inventory routing problem (IRP) is to determine a distribution plan that minimizes fleet operating and average total distribution and inventory holding costs without causing a stock-out at any of the sales-points during a given planning horizon. We propose a new model for the long-term IRP when demand rates are stable and economic order quantity-like policies are used to manage inventories of the sales-points. The proposed model extends the concept of vehicle routes (tours) to vehicle multi-tours. To solve the nonlinear mixed integer formulation of this problem, a column generation based approximation method is suggested. The resulting sub-problems are solved using a savings-based approximation method. The approach is tested on randomly generated problems with different settings of some critical factors to compare our model using multi-tours as basic constructs to the model using simple tours as basic constructs.     

Transient and steady-state analysis of a manufacturing system with setup changes

This paper deals with the optimal scheduling of a one-machine two-product manufacturing system with setup, operating in a continuous time dynamic environment. The machine is reliable. A known constant setup time is incurred when switching over from a part to the other. Each part has specified constant processing time and constant demand rate, as well as an infinite supply of raw material. The problem is formulated as a production flow control problem. The objective is to minimize the sum of the backlog and inventory costs incurred over a finite planning horizon. The global optimal solution, expressed as an optimal feedback control law, provides the optimal production rate and setup switching epochs as a function of the state of the system (backlog and inventory levels). For the steady-state, the optimal cyclic schedule (Limit Cycle) is determined. This is equivalent to solving a one-machine two-product Lot Scheduling Problem. To solve the transient case, the system's state space is partitioned into mutually exclusive regions such that with each region is associated an optimal control policy. A novel algorithm (Direction Sweeping Algorithm) is developed to obtain the optimal state trajectory (optimal policy that minimizes the sum of inventory and backlog costs) for this last case.     

Incorporating inventory and routing costs in strategic location models

We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and each DC maintains a certain amount of safety stock in order to achieve a certain service level for the customers it serves. The objective is to minimize the total cost that includes location costs and inventory costs at the DCs, and distribution costs in the supply chain. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. By exploring the structure of the problem, we find a low-order polynomial algorithm for the nonlinear integer programming problem that must be solved in solving the Lagrangian relaxation sub-problems. We present computational results for several instances of the problem with sizes ranging from 40 to 320 customers. Our results show the benefits of having an integrated supply chain design framework that includes location, inventory, and routing decisions in the same optimization model.     

零售商主导的模糊供应链博弈——考虑销售努力的情形

以包含一个制造商与一个零售商的两级供应链为研究对象,考虑零售商居于主导地位并付出销售努力时供应链各成员的决策。结合经济环境的不确定性,将市场需求函数和制造商的制造成本、零售商的经营成本视为模糊变量;运用斯塔克尔伯格模型刻画零售商和制造商之间的博弈过程,并引入期望值模型、机会约束模型来解决最优决策问题;通过一个数值算例证明了供应链博弈模型的有效性。研究表明,在均衡结果中,考虑到零售商所承担的销售努力成本,其总体期望值利润相比于制造商而言偏低,但单位产品边际利润较高。原因在于,零售商的主导地位使其不仅通过销售努力提高产品销量,提高了供应链的整体利润,同时也能够采用压低批发价格的方式,使自身在供应链中获取更多收益。     

A two-phase heuristic approach to the bike repositioning problem

An approach to overcome the bike imbalance problem is to transfer excess bikes to branches with bike shortages. This study develops a constrained mathematical model to deal with a multi-vehicle bike-repositioning problem, and aims to minimize the sum of transportation and unmet demand costs over a planning horizon through bike-transfer strategies under a minimum service requirement. A two-phase heuristic based on linear programming was proposed to solve the problem and produce compromising solutions. In the first phase, the paper developed a linear programming model to quickly develop decisions related to bike inventory, unloading, and loading for all stations for each time slot. In the second phase, this paper proposed an iterative approach through two parameter sensitive mathematical models to sequentially reduce the problem scale to develop decisions related to bike transfers. Computational results show that the proposed approach is superior to a CPLEX optimizer and a hybrid heuristic based on a genetic algorithm. The proposed approach was used to analyze the bicycle system in Taiwan. The impacts of various system parameters on the system were also investigated.     

Revised multi-choice goal programming for multi-period,multi-stage inventory controlled supply chain model with popup stores in Guerrilla marketing

In this paper, we consider a supply chain network design problem with popup stores which can be opened for a few weeks or months before closing seasonally in a marketplace. The proposed model is multi-period and multi-stage with multi-choice goals under inventory management constraints and formulated by 0–1 mixed integer linear programming. The design tasks of the problem involve the choice of the popup stores to be opened and the distribution network design to satisfy the demand with three multi-choice goals. The first goal is minimization of the sum of transportation costs in all stages; the second is to minimization of set up costs of popup stores; and the third goal is minimization of inventory holding and backordering costs. Revised multi-choice goal programming approach is applied to solve this mixed integer linear programming model. Also, we provide a real-world industrial case to demonstrate how the proposed model works.     

Integrated location and two-echelon inventory network design under uncertainty

In this paper, we propose a two-stage stochastic model to address the design of an integrated location and two-echelon inventory network under uncertainty. The central issue in this problem is to design and operate an effective and efficient multi-echelon supply chain distribution network and to minimize the expected system-wide cost of warehouse location, the allocation of warehouses to retailers, transportation, and two-echelon inventory over an infinite planning horizon. We structure this problem as a two-stage nonlinear discrete optimization problem. The first stage decides the warehouses to open and the second decides the warehouse-retailer assignments and two-echelon inventory replenishment strategies. Our modeling strategy incorporates various probable scenarios in the integrated multi-echelon supply chain distribution network design to identify solutions that minimize the first stage costs plus the expected second stage costs. The two-echelon inventory cost considerations result in a nonlinear objective which we linearize with an exponential number of variables. We solve the problem using column generation. Our computational study indicates that our approach can solve practical problems of moderate-size with up to twenty warehouse candidate locations, eighty retailers, and ten scenarios efficiently.     

考虑多仓库的共享单车重新配置问题研究

共享单车是我国大力提倡的低碳交通出行模式,加快共享单车发展是解决最后一公里、城市拥堵和环境污染等问题的重要途径。由于人们停放共享单车的无规律性,使得共享单车系统中各车桩的单车库存量存在不平衡。如何合理的对车桩中的单车进行重新调配,来满足用户的需求,是相关企业亟待解决的问题。共享单车的调配路线优化是优化车桩库存量的重要手段之一。本文研究多仓库条件下的货车调配路线优化问题,建立了一个混合整数非线性规划模型。不同于传统的路径优化问题的研究大多是以成本或时间为目标,本文采用基于车桩库存量的非线性惩罚函数来表示用户需求,从而使得所研究的问题是一个凸函数优化问题。为了简化本文的问题,将目标函数分段线性化。基于车桩网络的特点,设计了变邻域搜索算法,以及构建初始解的贪婪算法。最后,以某共享单车公司为例,进行算例分析,来说明模型和算法的合理性和有效性。