缩短供应链多阶响应周期的一个判断模型

首先介绍了供应链多阶响应周期的概念以及缩短供应链多阶响应周期的重要性，接着以案例形式具体分析了缩短供应链多阶响应周期的可能性；然后以调研资料为基础，对供应链多阶结构、供应链多阶响应周期的结构和供应链多阶响应成本结构分别进行了定义和分析。主要分析了缩短供应链多阶响应周期的条件，明确提出了缩短多种产品供应链多阶响应周期判断模型的一般形式。     

一种稳态库存链的灰色辨别方法

定义了供应链节点企业及整个供应链的“库存变异系数”，并且建立了供应链库存变异的灰色系统模型和“稳态库存链”的判别方法。     

供应链节点企业的非零和合作博弈建模方法

针对供应链的特点,运用博弈论的方法分析了供应链上节点企业成员的行为.确认该博弈过程属于非零和博弈、动态博弈和不完全信息博弈,并由此建立供应链上节点企业非零和合作博弈模型.     

目前供应链信息流存在的问题及改进

分析了现有供应链中的信息流存在的问题及其对供应链的影响。这些问题包括信息延滞、信号失真,然后针对总理2提出了改进的供应链模型。分析了信息流在该模型中的流程,阐述了供应链上各节点企业在改进后的模型中功能的变化;从成本,柔性、响应速度、简洁和集成5个角度还说明了改进模型的预期效果,最后阐明了实施此模型可能遇到的问题和困难。     

基于缩短供应链响应周期的运作模式研究

通过分析总结推动式和拉动式供应链的特点及对用户需求响应时间的变化，阐明基于延迟策略的推-拉结合式供应链的综合优势，提出利用延迟和压缩提前期的方法缩短供应链的响应周期；论述了对于不同生产类型企业为缩短响应时间可以采取不同的供应链运作模式；构建了基于延迟策略的模块化供应链模型，描述了该模型的运作流程以及实施中应注意的问题。     

基于随机需求的两级多期供应链网络规划模型

本文以三级供应链网络为例,研究了具有随机因素的,两级多期决策的供应链网络优化问题.优化的具体内容包括两个方面:供应链节点的选址和相应配送系统的设计.文章首先在现有研究成果的基础上,将模型设计拓展到多期决策领域.接下来,针对因素的随机性问题,应用了蒙特卡罗方法对随机因素进行了模拟和采样.最后,模型的目标函数不仅考虑了最小成本,同时还考虑了顾客满意度.对多目标模型的处理采用了e条件限制法.用这种方法的得到的供应链网络优化结果是一系列帕累托最优解,它使得决策者可以按照自己的偏好做出最后选择.     

《企业供应链的优化》

该书从企业供应链优化的角度，全面系统研究了企业供应链的结构、企业供应链的层级、企业供应链的构成要素、企业供应链节点关系，提出了对企业供应链进行优化的策略和方法，解决了企业供应链管理过程中的结构优化问题、层级优化问题、要素优化问题、供应链流程与供应链关系优化问题。该书既是一部源于企业又能指导企业的方法论著作，也是一部学术专著，引用和分析了大量企业供应链管理的案例，于例中求证基本规则，于理中评点典型案例，相得益彰，通俗易懂。适合从事企业管理、物流管理的企业管理人员，以及企业管理、物流管理等专业的研究生和本科生。[第一段]     

基于供应链管理的多周期合作型随机库存控制模型与仿真

在供应接管理的背景下，本文建立了一个具有不确定需求的多周期合作型供应链库存控制模型，并利用计算机仿真的方法对该模型进行了求解。     

供应链管理环境下的单周期库存控制建模及优化

传统的库存控制理论已经不能够适应供应链管理的要求。在建立了供应链库存成本的组成模型的基础上对供应链管理中的单周期库存控制过程进行了深入的分析，建立了相应的数学模型，求出了最优解。     

面向供应链合作的信任维系协调体系的研究

为了保证节点企业合作中信任协调的合作关系,以有效地实现企业经营目标,从信任的视角提出了一个包括目标层、合作层、信任协调层、支撑层在内的供应链合作(SCC)信任维系协调的解决思路.把节点企业内、外部的供应链运作、合作行为、业务目标集成起来,给出了整体框架模型,并对基于企业目标的信任协调机制、信任金字塔和实现协调层的具体方式等关键问题进行了探讨.信任协调体系框架模型对供应链合作运作提供了一定的参照.     

Information management strategies and supply chain performance under demand disruptions

The operations management literature presents inadequate comprehensive understanding on information management strategies of mitigating supply chain disruption risks. By using control theory modelling and simulation, this study compares the disruption mitigation effects of three information management strategies. From the aspect of stability, the existing stability boundaries are revised by a new method in a two-echelon case. It shows that supply chains (SC) with popular information management strategies are not evidently more stable than traditional ones. From the aspect of disruption recovery time, an innovative two-echelon swiftest response problem under these information management strategies is formulated and solved. Results show that a collaborative planning, forecasting and replenishment (CPFR) SC with complete SC information performs the best. However, in a later operational risk mitigation test, an information sharing (IS) SC with partial information has the smallest bullwhip effect. From the aspect of demand amplification and frequency response, an innovative frequency–response plot of order amplification is proposed in a time-continuous SC with moving average forecasts. It implies the best frequency response for concurrently mitigating both operational and disruption risks coming from a CPFR SC. But for a certain SC structure there is still a balance between mitigating bullwhip effect and quick response. Moreover, it also implies that anti-bullwhip should exist in a certain condition, as realised in our numerical experiments.     

Effects of information transparency and cooperation on supply chain performance: a simulation study

This research explores the effect of information transparency and cooperation among the front nodes of supply chains. Although published works in the supply chain (SC) domain underline the need for information transparency and cooperation for competitiveness of firms, the majority among these are conceptual or empirical. As such, the domain requires fresh effort towards analytical and simulation based research for validation. In the present work, simulation is used as the research methodology. The simulation platform is developed in ARENA®, which is based on simulation language SIMAN. It has the capability to model multi-product, multi-echelon supply chains. Cooperation is depicted in the form of demand pooling and accelerated delivery of consignments from cooperating SC nodes, when stock-out contingency occurs at a particular node. The modelling also explores the effects of full, partial and zero level of information transparency on the SC performance. The simulation results suggest that these initiatives improve SC service time performance. It is also observed that more advantages from cooperation and information transparency are obtainable when the SC is leaner. The studies also highlight that such SC initiative that motivates local optimisation at the nodes is counter-productive to SC wide performance.     

基于缩短物流多阶响应周期的LRP模型研究

建立了以缩短物流多阶响应周期和成本为优化目标的多目标设施定位-运输路线安排问题(LRP)模型。在模型的构建过程中，加入了对不同运输方式选择的变量，以满足用户对不同运输速度和成本的要求，并在成本计算中考虑了订货成本和分拣中心的固定成本。     

基于微分求积法及V-变换的大规模动力系统快速数值计算方法

针对大规模动力系统动态响应的数值计算,传统的微分求积法通常在时间域上逐步离散、整体求解,存在“维数灾”问题。在多级高阶时域微分求积法的基础上,提出了基于V-变换的大规模动力系统动态响应的快速数值计算方法。利用微分求积法的加权系数矩阵满足V-变换这一重要特性,将离散后的雅可比矩阵方程进行解耦分块,推导形成了多级分块递推计算方法。数值算例表明,即使采用相当于Newmark方法2s倍的步长,微分求积法的计算精度仍比Newmark方法要高出2~3个数量级。进一步对3个不同规模的算例系统进行了测试,结果表明：相对于传统的数值计算方法,多级分块递推计算方法可以获得较大的加速比,能够显著提高大规模动力系统动态响应的计算效率。     

Applying ant colony optimization for load balancing on grid

Grid computing is comprised of many distributed nodes in order to compute and analyze a set of distributed data. To improve the processing performance, an appropriate load-balancing algorithm is required to equally distribute loads among the grid’s nodes. In this article, an algorithm based on ant colony optimization is proposed to deal with load-balancing problems. In this approach, when an ant reaches a node, the ant’s table and the node’s table exchange their information and update each other. In order to move to the most appropriate node, the ant selects the next node from the current node’s table according to the nodes’ loads and their CPU rates. This process is continued until the ant passes the predefined steps. The experimental results show that while implementing the proposed algorithm to the grid environment, increasing the number of jobs and their length has insignificant impact on the system response time.     

A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand

Motivated by the challenges encountered in sawmill production planning, we study a multi-product, multi-period production planning problem with uncertainty in the quality of raw materials and consequently in processes yields, as well as uncertainty in products demands. As the demand and yield own different uncertain natures, they are modelled separately and then integrated. Demand uncertainty is considered as a dynamic stochastic data process during the planning horizon, which is modelled as a scenario tree. Each stage in the demand scenario tree corresponds to a cluster of time periods, for which the demand has a stationary behaviour. The uncertain yield is modelled as scenarios with stationary probability distributions during the planning horizon. Yield scenarios are then integrated in each node of the demand scenario tree, constituting a hybrid scenario tree. Based on the hybrid scenario tree for the uncertain yield and demand, a multi-stage stochastic programming (MSP) model is proposed which is full recourse for demand scenarios and simple recourse for yield scenarios. We conduct a case study with respect to a realistic scale sawmill. Numerical results indicate that the solution to the multi-stage stochastic model is far superior to the optimal solution to the mean-value deterministic and the two-stage stochastic models.     

Underestimating the bullwhip effect: a simulation study of the decomposability assumption

We investigate the assumption of decomposability as it pertains to modelling the bullwhip effect in multi-stage supply chains. Decomposing a multi-stage supply chain into a set of node pairs, each of which can be efficiently represented with a two-stage model, is a common modelling technique when analysing the bullwhip effect in supply chains. This approach depends on the validity of the decomposability assumption since most supply chains are coupled systems that are a logical fit for singular, or ‘monolithic’, multi-stage models. We utilise a simulation study to compare decomposition-based supply-chain models with monolithic models and determine if decomposition modelling significantly alters the predicted severity of the bullwhip effect. We find decomposition-based models exhibit a significantly lower level of bullwhip effect than monolithic models of the same supply chain. The systematic underestimation of the bullwhip effect by decomposition-based models indicates that the assumption of decomposability is flawed. Our study also confirms previous work showing the significant benefit of using actual, instead of approximate, lead-time demand information. We discuss implications for supply-chain modelling, supply-chain design, and data collection.     

A hybrid multi-stage predictive model for supply chain network collapse recovery analysis: a practical framework for effective supply chain network continuity management

In this paper, we present a multi-stage hybrid model for analysing a supply chain network (SCN) collapse recovery possibility. In the first stage of the model, we analyse the ability of an SCN to fulfil its customers required due date (RDD). As soon as the SCN defaults to timely fulfil their customers RDD, the second stage of the model is triggered to measure the collapse recovery possibility (CRP) of the SCN. Then, we calculate the final collapse recovery possibility (FCRP) of the SCN. Since the operation times, customer demand and external supply of raw material are uncertain, we use fuzzy triangular numbers to estimate the value of foregoing parameters. Consequently, we employ fuzzy program evaluation and review technique (FPERT) to calculate the completion time of SCN operations. In the third stage, for the critical elements of the SCN obtained from FPERT, the SC simulator is developed to provide a dynamic view of the SC and assesses the impact of decisions recommended by the SC fuzzy models on SC performance. Moreover, an empirical example is presented to validate the effectiveness of the proposed model. Finally, we conduct sensitivity analysis on the parameters employed in the model to analyse the behaviour of each parameter.     

缩短多阶响应周期的供应链新型计划模式

随着顾客需求多样性与市场竞争不确定性的日益加剧,缩短供应链多阶响应周期的计划模式已逐渐成为供应链计划模式研究中的重要领域。本文在对新经济环境中现有供应链计划模式的不足进行了分析的基础之上,提出了三种以缩短多阶响应周期为考虑要点的整体化的新型计划模式,并结合综合模式提出了一套基于响应周期的“链一阶一点一阶一链”的层级往复式计划编制方法,最后分析了该供应链计划实施过程中的运作要点。     

Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility

The paper examines the mediating effect of supply chain (SC) practices on the relationship between agile supply chain (ASC) strategy and SC performance. It further examines the moderating effect of information systems (IS) capability for agility on this mediated relationship. Using the theoretical lenses of complementarity and the information processing view of the supply chain, we hypothesise that strategic supplier partnership, customer relationship, postponement and lean practices, mediate the relationship between ASC strategy and SC performance. We further hypothesise that IS capability for agility moderates each of these mediated relationships. We empirically test the hypotheses using survey data from members of senior and executive management in the logistics/supply chain functions of 205 firms. The paper contributes to the literature on ASCs by theoretically explaining and empirically demonstrating how SC practices and IS capability for agility act together to effect a positive relationship between ASC strategy and supply chain performance.