供应链模型及其优化研究的现状与进展

这是一篇关于供应链模型及其优化分析工作的综述性文章．本文综述了生产运作 管理和供应链管理模型分析．关于供应链的局部情况，评述了生产运作中库存、生产销售、 库存销售、质量控制、财务等问题的模型及其优化应用；关于供应链整体情况，评述了集成 化、供应合同、信息价值、产品管理和国际运作等问题的模型及其优化应用．最后，提出了 供应链模型及其优化分析工作进一步研究的问题．     

一种考虑相关需求的多级库存控制优化模型

在技术驱动的全球化市场,供应链管理已经成为企业和学者共同面对的重要课题。库存控制问题则是企业供应链管理中一个很重要的环节。该文研究了在供应链管理的环境下,由K个配送中心,每个配送中心由Nk个零售商组成的配送网络的库存控制问题。凭着对实际配送系统的观察和分析,在综合考虑了同一产品在不同零售点需求的相关性,以及同一产品在不同周期需求相关性的情况下,以最小化库存成本和运输成本之和为目标,一定的服务水平为约束,建立了多级库存控制优化模型。     

连锁零售供应链多级库存协同决策研究

针对连锁零售供应链多级库存资源的动态优化配置问题,提出了在上层对库存策略和下层对物流分配方案协同寻优的多级库存双层规划模型。借鉴细粒度模型遗传算法的遗传操作具有局部性的特点,模拟微观群体交互作用的局部性,基于细粒度模型遗传算法的Agent群体行为优化算法和基于复杂适应系统涌现机理的协同决策机制,进行连锁零售供应链多级库存协同决策研究。通过算例实验对模型的有效性进行了验证。仿真实验结果表明,通过连锁零售供应链微观个体Agent的群体行为优化,从系统工程的角度,实现了连锁零售供应链多级库存的动态资源优化配置和信息共享,降低了多级库存管理与运营的总成本。     

串行供应链最优控制优化模型

最优控制策略的确定是供应链成败的关键因素之一,为此对串行供应链库存控制策略进行研究．首先提出了基于非线性整数规划与推拉控制通用模型相结合的串行供应链库存控制优化模型;然后利用遗传算法与仿真分析相结合的方法确定最优控制策略．实例仿真验证了该方法的有效性．     

时滞和参数不确定的供应链动态模型及其鲁棒H∞控制

研究了不确定环境下的供应链运作问题，并建立了具有生产时滞、成本参数和需求不确定性的供应链动态模型．分析了供应链的鲁棒运作，采用鲁棒H∞控制策略和线性矩阵不等式（LMI）算法处理供应链系统鲁棒运作问题．借助供应链库存状态的静态反馈控制，使供应链动态系统达到抑制不确定性干扰的作用，并使供应链运作达到理想总成本．最后，通过仿真计算验证了所得结果．     

自适应控制对多级库存延迟问题的优化研究

针对在随机需求下交货延迟所导致供应链多级库存系统库存积压、缺货和牛鞭效应等问题,建立了基于自适应控制算法的多级库存动态优化模型。通过泰勒展开和拉布拉斯变换建立了基于APIOBPCS策略考虑延迟的动态多级库存控制模型;由Lyapunov渐进稳定性定理设计了一种适用于多级库存的模型参考自适应控制算法,其中以无交货延迟的参考库存模型作为目标,通过调节线性补偿函数和自适应控制率,逐渐缩小实际库存模型与参考库存模型间的输出误差,以此削弱交货延迟对多级库存模型的影响;通过实证数据验证了模型参考自适应控制对一个三级供应链库存系统的动态优化效果。仿真结果表明,自适应控制下的无信息共享多级APIOBPCS库存系统缺货全部归零,牛鞭效应下降40.7%。在不增加企业运营投入的前提下,通过自适应控制算法,优化资源配置,动态削弱了交货延迟对多级库存的影响,提升了供应链运营效率。     

基于MRP汽车零部件生产计划系统研究与实现

为了提高汽车零部件生产效率，确保按时完成客户订单，将物料需求计划（MRP）技术应用于汽车零部件生产计划系统中．首先给出了汽车零部件行业的供应链结构，提出了基于MRP的对产品进行分组并对产品结构进行层次化分解的汽车零部件生产计划模式．在此基础上，详细介绍了利用客户订单、产品结构信息、工作日历以及库存信息等来生成零部件生产计划和所需物料的采购计划的实现过程，并对实例进行了分析．该系统已经应用于汽车零部件生产企业中．     

基于免疫遗传算法的供应链库存协同优化研究

在全球经济一体化发展的大背景下,企业之间的竞争方式发生了本质上的改变,即已由原来单个企业之间的竞争变为企业间所在供应链之间的竞争。在这种竞争方式下,企业逐步向追求整个供应链利益的最大化转变。针对供应链整体协同运作来有效控制库存的问题,建立了多级库存供应链模型,运用改进的免疫遗传算法对所建的供应链多级库存模型进行优化求解;并以西部某实木家具公司的P1和P2产品为例,结合所建立的多级库存成本优化模型和相应的算法设计,运用Matlab数学仿真软件仿真求解。     

供应商具备多级生产率的供应链控制策略仿真

研究供应商具有多级生产率的供应链库存成本控制策略问题。在满足客户与销售商随机性订单需求的基础上,以供应链的整体运营成本最低为目标,运用Arena仿真平台对由供应商、销售商、客户构成的多级供应链库存系统进行了建模和仿真优化,制定合理的销售商的库存策略以及供应商的生产库存策略。为具备多级生产率的供应链系统控制策略提供了一种更科学合理的决策方法,对以后的类似研究具有一定的借鉴意义。     

价格折扣和运输折扣下供应链库存—运输优化模型

首先考虑了价格折扣和运输折扣前销售商和供应商各自利润最大的库存—运输模型，然后建立了价格折扣和运输折扣后满足帕雷托有效性，使供应链整体利润最大的库存—运输优化模型，并结合算例说明模型的有效性．     

Deteriorating item inventory model with shortage due to supplier in an integrated supply chain

The aim of this paper is to develop an inventory model for deteriorating items with a shortage occurring at the supplier involving a supply chain between the producer and buyer. A numerical example is used to illustrate the model and demonstrate that integrated decisions are more cost-effective compared with independent decisions from the supplier, producer or buyer. The optimal number of deliveries is derived with the minimal joint total cost from the integrated viewpoint. This study compares cases with and without shortages. A sensitivity analysis is given to explore the effect from a supplier shortage.     

An integrated vendor–buyer inventory model with backorder price discount and effective investment to reduce ordering cost

The single-vendor single-buyer integrated production inventory system has been an object of study for a long time, but little is known about the effect of investing in reducing ordering cost on the integrated inventory models with backorder price discount and variable lead time. The purpose of this article is to investigate in the continuous review model with backorder price discount and variable lead time to effectively increase investment and to reduce the joint expected annual total cost. The integrated strategy discussed here is one in which the buyer orders a quantity, then the vendor produces n times order quantity in each production cycle, in order to reduce setup cost. In addition, the buyer offers backorder price discounts to the customers that may motivate the customers’ desire for backorders, and buyer ordering cost can be reduced through effective investment. An integrated inventory model is established to find the optimal solutions of order quantity, ordering cost, backorder price discount, lead time, and the number of shipments from the vendor to the buyer in one production run, so that the joint expected annual total cost incurred has the minimum value. Furthermore, numerical examples are used to demonstrate the benefits of the model.     

An integrated production–distribution inventory system involving probabilistic defective and errors in quality inspection under variable setup cost

This paper presents a probabilistic defective vendor–buyer integrated inventory model with the consideration of quality inspection errors at the buyer's end and setup cost as function of capital investment. An integrated inventory model is established to find the optimal solutions of lot size, setup cost, and the total number of shipments from the vendor to the buyer in one production run, so that the joint expected total cost incurred has the minimum value. We consider three types of continuous probabilistic defective function to find the associated cost of the system. The expected total cost function is derived for each of these three distributions, its convexity is proved via differential calculus. An efficient iterative algorithm is designed to obtain the optimal solution of the model. The computational effort and time are small for the proposed algorithm and it is simple to implement. Numerical examples and sensitivity analysis are used to demonstrate the application and the performance of the proposed methodology. The computational results indicate that if we make decisions with the capital investment in reducing setup cost, it will help to lower the system cost, and we obtain a significant amount of savings to increase the competitive edge in business.     

Two-echelon supply chain inventory model with controllable lead time and service level constraint

This paper considers a two-echelon supply chain inventory problem consisting of a single-vendor and a single-buyer. In the system under study, a vendor produces a product in a batch production environment and supplies it to a buyer facing a stochastic demand, which is assumed to be normally distributed. Also, buyer’s lead time is controllable which can be shortened at an added cost and all shortages are backordered. A model has been formulated for an integrated vendor–buyer problem to jointly determine the optimal order quantity, lead time and the number of shipments from the vendor to the buyer during a production cycle while minimizing the total expected cost of the vendor–buyer integrated system. It is often difficult to estimate the shortage cost in inventory systems. Therefore, instead of having a shortage cost term in the objective function, a service level constraint (SLC) is included in the model that requires a certain proportion of demands to be met in each cycle. An efficient procedure has been suggested to find the bounds on number of shipments and then, an algorithm is developed to obtain the optimal solution of the proposed model. A numerical example is included to illustrate the algorithmic procedure and the effects of key parameters are studied to analyze the behavior of the model. Finally, the savings of buyer and vendor are investigated from implementation of joint optimization model over the model in which they minimize their own cost independently.     

Coordination policy for a two-stage supply chain considering quantity discounts and overlapped delivery with imperfect quality

Unlike the traditional integrated supplier–buyer coordination model, this research incorporates overlapped delivery and imperfect items into the production–distribution model. This model improves the observable fact that the system might experience shortage during the screening duration and also takes quantity discount into account. This approach has not been discussed in previous integrated supplier–buyer coordination models. The expected annual integrated total cost function is derived and properties and theorems are explored to help develop an algorithm. A solution procedure, free from the convexity associated with an algorithm is established to find the optimal solution. A numerical example is given to illustrate the proposed procedure and algorithm. A sensitivity analysis is made to investigate the effects of five important parameters (the inspect rate, the annual demand, the defective rate, the holding cost, and the receiving cost) on the optimal solution. Managerial insights are also discussed.     

Two-echelon fuzzy stochastic supply chain for the manufacturer–buyer integrated production–inventory system

This paper deals with two-echelon integrated procurement production model for the manufacturer and the buyer integrated inventory system. The manufacturer procures raw material from outside suppliers (not a part of supply chain) then proceed to convert it as finished product, and finally delivers to the buyer, who faces imprecise and uncertain, called fuzzy random demand of customers. The manufacturer and the buyer work under joint channel, in which a centralized decision maker makes all decisions to optimize the joint total relevant cost (JTRC) of entire supply chain. In this account, in one production cycle of the manufacturer we determine an optimal multi-ordering policy for the buyer. To be part of this, we first derive the JTRC in stochastic framework, and then extend it in fuzzy stochastic environment. In order to scalarize the fuzzy stochastic JTRC, we use an evaluation method wherein randomness is estimated by probabilistic expectation and fuzziness is estimated by possibilistic mean based on possibility evaluation measure. To derive the optimal policies for both parties, an algorithm is proposed. A numerical illustration addresses the situations of paddy procurement, conversion to rice and fulfillment of uncertain demand of rice. Furthermore, sensitivity of parameters is examined to illustrate the model and algorithm.     

Investing in lead-time variability reduction in a collaborative vendor–buyer supply chain model with stochastic lead time

This study deals with investing in lead-time variability reduction problems for the integrated vendor–buyer supply chain system with partial backlogging under stochastic lead time. We consider that lead time variability can be reduced through further investment; more specifically, a logarithmic investment function is used that allows investment to be made to reduce lead-time variability. By using the proposed supply chain model, considerable savings can be achieved to increase the competitive edge. The objective is to derive the optimal production/ordering strategy, and the best investment policy to minimize joint total cost. A computer code using the software, Mathematica, is developed to derive the optimal solution. Furthermore, we discuss the sensitivity of the optimal solution together with the changes of the values of the parameters associated with the model for decision-making. Various numerical examples are given to illustrate the results.     

Dynamic joint pricing and production policy for perishable products

A joint dynamic pricing and production problem for perishable products without shortages is considered. The demand rate is price‐dependent and time‐varying. This paper constructs an optimal control model to maximize the total profit under a general nonlinear production cost function. The feature of the optimal joint dynamic pricing and production policy is analyzed by solving the corresponding optimal control problem on the basis of improved Pontryagin's maximum principle. Then, an effective algorithm is designed to obtain the optimal joint policy. The case of the joint static optimal policy is also investigated and compared with the dynamic one. Finally, numerical examples are presented to illustrate the effectiveness of the proposed methods, and some managerial implications are provided for the management of perishable items.     

Supply chain coordination with defective items and quantity discount

This study develops an integrated inventory system involving defective items and quantity discount for optimal pricing and ordering strategies. The model analysed in this study is one in which the buyer orders a quantity, the vendor produces more than buyer's order quantity in order to reduce set-up cost, and then he/she offers an all-units quantity discount to the buyer. Our objective is to determine the optimal order quantity, retail price, mark-up rate, and the number of shipments per production run from the vendor to the buyer, so that the entire supply chain joint total profit incurred has a maximum value. Furthermore, an algorithm of finding the optimal solution is developed. Numerical examples are provided to illustrate the theoretical results.     

Developing a coordinated vendor–buyer model in two-stage supply chains with stochastic lead-times

This paper develops an approach to determine the optimal production and shipment policy for an integrated vendor–buyer problem. The vendor manufactures the product in batches at a finite rate and ships the output to the buyer. All shipments to the buyer are equal-sized batches. Despite previous papers in the literature, we assume that the supply lead-time between vendor and buyer is stochastic and shortage is also allowed. The objective is to minimize the expected total cost of both buyer and vendor. We derive the expected annual integrated total cost function and propose an analytic solution procedure to determine the optimal policy. To illustrate the significance of cost-reduction of the integrated approach in comparison with independent decisions by buyer or vendor, some numerical examples are also presented.