需求不确定性对闭环供应链系统决策的影响分析

在由一个制造商与一个销售商组成的闭环供应链系统中,构建了市场需求不确定环境下废旧产品回收率为外生变量和零售商决策变量两种情形的闭环供应链系统决策模型,并通过数值仿真得到了各模型供应链成员的最优定价和订货策略以及最终利润,进一步分析了两种情形下市场需求不确定性对零售商、制造商决策行为及利润的影响,最后对两种情形进行了对比分析.     

钢铁企业订货系统模型的VENSIM建模与仿真实现

订货模型存在于大多数的公司中,用以根据销售情况组织公司生产.在简单生产-分配系统模型基础上,构建了钢铁企业零售商订货模型;运用系统动力学方法分析了该订货模型,并采用了阶跃函数描述了复杂变化的零售商销售状况,通过VENSIM建模与仿真分析,找出了零售商销售-订单-库存的相互关系;结果表明,钢铁企业生产-分配系统的特征与采用阶跃函数得出的结果非常相似,对钢铁企业零售商的订货策略具有指导意义.     

随机提前期随机需求条件下的二级库存模型

考虑由一个分销中心和若干零售商组成的两级分销系统 ,假设分销中心和零售商实行连续性盘点、( R,Q) 订货策略 ,分销中心的交货时间是常量 ,而零售商处的提前期为随机变量 ,且需求服从独立的复合泊松过程 .以整个系统平均成本最小为目标函数 ,由此得到最优的订货策略 ,并且给出数值例子及敏感性分析 .     

供应商免费送货条件下零售商的订货策略选择研究

电子商务交易中越来越多的供应商采用免费送货策略.以由供应商训零售商组成的两阶段供应链为对象,分别从从量免费送货和从价免费送货两种角度用数值分析方法讨论零售商的订货策略选择,得出零售商在面对供应商免费送货条件时会采取的订货策略.研究结果对零售商制定订货策略具有实际帮助.     

订货批量对牛鞭效应的影响分析

对于一个采用连续检查(s,S)订货策略的两级供应链,本文运用Markov链理论,通过对正则平稳Markov链行为特征的分析发现,即使不考虑需求预测方法,供货短缺,提前期和价格波动等因素,仅仅零售商的订货批量就能够产生牛鞭效应.研究结果表明,在需求分布和订货批量的变化范围满足一些条件时,减小订货批量有助于减弱牛鞭效应,从而提高供应链的运作效率.     

基于供应商视角的提前订货协调研究

本文针对一个供应商和零售商组成的供应链系统,主要研究零售商如何设计提前订货折扣来吸引顾客提前订货;而供应商如何根据市场需求的不确定性,设计零售商提前订货资金的付款时间等问题。在供应商-Stackelberg框架下,以期望利润为目标,建立了相应的决策模型。通过对模型的分析求解,给出了不同市场环境下供应商要求零售商支付提前订货资金的策略。最后,用算例验证了模型和理论分析的可行性。     

需求信息泄露条件下供应链零售商订货策略研究

针对供应链订货过程中的需求信息泄露现象,通过对比供应商在泄露信息和不泄露信息情况下的收益,研究了供应链需求信息泄露的产生机理。建立了需求信息泄露条件下订货过程中零售商之间的信号博弈模型,通过模型求解得到了分离均衡和混同均衡的存在条件,并对不同均衡状态下零售商的最优订货策略和最优收益进行了分析。在此基础上,进一步讨论了零售商的订货策略选择问题。最后,通过一个数值算例说明了有关结论的有效性。     

基于(Q,R)补货的双渠道供应链库存模型研究

制造商在引入网络直销渠道后,需有效协调与零售商之间的物流冲突.以销售单一产品,只有一个制造商和零售商,且制造商的补货策略为(Q,R)策略,零售商的补货策略为一对一策略的双渠道供应链系统作为研究对象,在综合考虑消费者渠道偏好和由于销售渠道缺货所引起的消费者转移的前提下,结合Markov过程理论建立供应链系统库存模型,针对制造商和零售商的库存决策进行分析.分析表明,制造商存在R~*及Q~*,零售商存在库存水平S~*使供应链系统的收益最大.MATLAB仿真表明了该结论的有效性.     

关于信用期内含缺陷产品EOQ模型

考虑到在实际中供应链上游供应商提供给下游零售商的信用支付期通常为一个订货周期,建立了缺陷率服从一定分布的缺陷产品在信用支付策略下的最优订货批量模型.模型中允许缺货发生并且以最大期望利润为目标函数,通过分析得到模型最优解.最后给出仿真实验,并且分析了模型参数变化对最优解的影响.     

基于定期处理的多品种逆向物流库存控制研究

针对含有逆向物流的多品种商品库存管理问题,把逆向物流纳入到整个库存当中来考虑,分析了库存控制的成本构成,在假设回收的物品经过修复后可以无差别回到销售领域的条件下,用订货范围代替传统订货点的方式,采用定期处理的方式来进行库存的控制,通过仿真手段得到成本最小的库存控制参数.     

Supply rationing in multi-echelon divergent systems

In this paper we determine a simple inventory control rule for multi-echelon distribution systems under periodic review without lot sizing. The primary focus is the two-echelon model with a stockless central depot, but several extensions (>2 echelons, central stock allowed) are discussed as well. The, control rule consists of two parameter sets: a set of rationing fractions at the central depot with the purpose to minimize inventory imbalance, and a set of order-up-to levels for the local stockpoints with the purpose to achieve a target service level (fill rate) per local stockpoint. The problem to calculate these control parameters is solved by decomposition in two subproblems. First the rationing fractions are determined such that the (approximate) expected system imbalance is minimized. Next the order-up-to levels are calculated such that the target fill rates are achieved. Numerical results show that this control rule, called Balanced Stock (BS) rationing, is accurate and more robust than the Consistent Appropriate Share (CAS) rationing polocy.     

Multi-item inventory control with full truckloads: A comparison of aggregate and individual order triggering

In this paper, we consider the stochastic joint replenishment problem in an environment where transportation costs are dominant and full truckloads or full container loads are required. One replenishment policy, taking into account capacity restrictions of the total order volume, is the so-called QS policy, where replenishment orders are placed to raise the individual inventory positions of all items to their order-up-to levels, whenever the aggregate inventory position drops below the reorder level. We first provide a method to compute the policy parameters of a QS policy such that item target service levels can be met, under the assumption that demand can be modeled as a compound renewal process. The approximation formulas are based on renewal theory and are tested in a simulation study which reveals good performance. Second, we compare the QS policy with a simple allocation policy where replenishment orders are triggered by the individual inventory positions of the items. At the moment when an individual inventory position drops below its item reorder level, a replenishment order is triggered and the total vehicle capacity is allocated to all items such that the expected elapsed time before the next replenishment order is maximized. In an extensive simulation study it is illustrated that the QS policy outperforms this allocation policy since it results in lower inventory levels for the same service level. Although both policies lead to similar performance if items are identical, it can differ substantially if the item characteristics vary.     

Lost-sales inventory systems with a service level criterion

Competitive retail environments are characterized by service levels and lost sales in case of excess demand. We contribute to research on lost-sales models with a service level criterion in multiple ways. First, we study the optimal replenishment policy for this type of inventory system as well as base-stock policies and (R, s, S) policies. Furthermore, we derive lower and upper bounds on the order-up-to level, and we propose efficient approximation procedures to determine the order-up-to level. The procedures find values of the inventory control variables that are close to the best (R, s, S) policy and comply to the service level restriction for most of the instances, with an average cost increase of 2.3% and 1.2% for the case without and with fixed order costs, respectively.     

Performance bound for myopic order-up-to inventory policies under stationary demand processes

We consider a single-item infinite-horizon inventory system operating in discrete time and study the performance of the popular myopic order-up-to policy when demand is driven by a general autoregressive moving average (ARMA) stationary process. We derive a suboptimality bound for a system that operates under full demand backlogging, linear holding and backordering costs, and a constant replenishment lead time. We illustrate our results for the case in which demand follows an ARMA(1,1) process, which includes two commonly used demand models, MA(1) and AR(1), as special cases.     

单水箱液位控制系统的模糊推理建模及仿真

首先,依据实际背景,利用单水箱实验系统设计了几种典型的液位控制实验;然后,将模糊推理建模法应用于实际的单水箱液位控制系统中,推导得到其数学模型,即变系数线性微分方程组;最后,利用仿真实验检验了这些数学模型的准确性。结果表明这种建模方法比较精确,充分考虑了实际系统的非线性特性。     

Minimizing the Total Cost in an Integrated Vendor—Managed Inventory System

In this paper we consider a complex production-distribution system, where a facility produces (or orders from an external supplier) several items which are distributed to a set of retailers by a fleet of vehicles. We consider Vendor-Managed Inventory (VMI) policies, in which the facility knows the inventory levels of the retailers and takes care of their replenishment policies. The production (or ordering) policy, the retailers replenishment policies and the transportation policy have to be determined so as to minimize the total system cost. The cost includes the fixed and variable production costs at the facility, the inventory costs at the facility and at the retailers and the transportation costs, that is the fixed costs of the vehicles and the traveling costs. We study two different types of VMI policies: The order-up-to level policy, in which the order-up-to level quantity is shipped to each retailer whenever served (i.e. the quantity delivered to each retailer is such that the maximum level of the inventory at the retailer is reached) and the fill-fill-dump policy, in which the order-up-to level quantity is shipped to all but the last retailer on each delivery route, while the quantity delivered to the last retailer is the minimum between the order-up-to level quantity and the residual transportation capacity of the vehicle. We propose two different decompositions of the problem and optimal or heuristic procedures for the solution of the subproblems. We show that, for reasonable initial values of the variables, the order in which the subproblems are solved does not influence the final solution. We will first solve the distribution subproblem and then the production subproblem. The computational results show that the fill-fill-dump policy reduces the average cost with respect to the order-up-to level policy and that one of the decompositions is more effective. Moreover, we compare the VMI policies with the more traditional Retailer-Managed Inventory (RMI) policy and show that the VMI policies significantly reduce the average cost with respect to the RMI policy.     

多机电力系统直接自适应输出反馈模糊控制

针对多机电力系统励磁控制模型,考虑电力系统的状态不完全可测及多变量、非线性等特点,以T-S模糊逻辑系统直接逼近控制器,设计出基于状态观测器的直接自适应输出反馈模糊控制器,并通过李亚普诺夫函数进行了稳定性证明.算法具有很好的鲁棒性和动态性能,仿真结果表明所设计的控制器能够快速有效地改善系统在大干扰下的暂态稳定性.     

Stability analysis of sampled-data fuzzy controller for nonlinear systems based on switching T–S fuzzy model

This paper investigates the system stability of a sampled-data fuzzy-model-based control system, formed by a nonlinear plant and a sampled-data fuzzy controller connected in a closed loop. The sampled-data fuzzy controller has an advantage that it can be implemented using a microcontroller or a digital computer to lower the implementation cost and time. However, discontinuity introduced by the sampling activity complicates the system dynamics and makes the stability analysis difficult compared with the pure continuous-time fuzzy control systems. Moreover, the favourable property of the continuous-time fuzzy control systems which is able to relax the stability analysis result vanishes in the sampled-data fuzzy control systems. A Lyapunov-based approach is employed to derive the LMI-based stability conditions to guarantee the system stability. To facilitate the stability analysis, a switching fuzzy model consisting of some local fuzzy models is employed to represent the nonlinear plant to be controlled. The comparatively less strong nonlinearity of each local fuzzy model eases the satisfaction of the stability conditions. Furthermore, membership functions of both fuzzy model and sampled-data fuzzy controller are considered to alleviate the conservativeness of the stability analysis result. A simulation example is given to illustrate the merits of the proposed approach.     

Analysis of supply contracts with minimum total order quantity commitments and non-stationary demands

In this paper we address a buyer–supplier arrangement of particular importance: total order quantity commitment (TOQC). Under the TOQC contract that has been negotiated between the buyer and the supplier, the former agrees to an obligation to procure a certain quantity of an item from the latter over the predetermined period of time (additional quantity can be obtained maybe at a different price). The optimal inventory replenishment policy is shown to be dual order-up-to levels under a given TOQC, and the optimal TOQC is also demonstrated to be mathematically straightforward to obtain.     

Inventory models with minimal service level constraints

This paper investigates inventory models in which the stockout cost is replaced by a minimal service level constraint (SLC) that requires a certain level of service to be met in every period. The minimal service level approach has the virtue of simplifying the computation of an optimal ordering policy, because the optimal reorder level is solely determined by the minimal SLC and demand distributions. It is found that above a certain “critical” service level, the optimal (s,S) policy “collapses” to a simple base-stock or order-up-to level policy, which is independent on the cost parameters. This shows the minimal SLC models to be qualitatively different from their shortage cost counterparts. We also demonstrate that the “imputed shortage cost” transforming a minimal SLC model to a shortage cost model does not generally exist. The minimal SLC approach is extended to models with negligible set-up costs. The optimality of myopic base-stock policies is established under mild conditions.