电子市场环境下供应链运作的鲁棒优化模型

本文研究了电子市场环境下的供应链运作问题,提出了不确定环境下的鲁棒优化模型.这一研究的实质是在外界需求最差条件下,如何得到电子市场中供应链最优供应量的策略.文中采用区间方法,设计供应链运作的鲁棒最优策略.进一步,在电子市场不确定环境下,进行了鲁棒策略仿真工作,结果表明鲁棒策略能为决策者提供最坏情况下供应商提供产品数量的鲁棒解决方案.     

动态供应链网络的H_∞控制

牛鞭效应的抑制与消除问题是供应链管理的重要课题.针对具有多个生产商和分销商的供应链网络,建立具有需求不确定性的闭环供应链网络动态模型,利用鲁棒H∞控制策略和线性矩阵不等式方法,处理供应链网络的鲁棒运作问题.通过设计依赖于库存状态的反馈控制器,抑制闭环供应链网络中不确定性的影响,使得闭环供应链网络系统运作达到理想的总成本.最后,通过仿真计算验证所提出方法的有效性.     

动态供应链网络的 H ∞ 控制

牛鞭效应的抑制与消除问题是供应链管理的重要课题． 针对具有多个生产商和分销商的供应链网络,建立具有需求不确定性的闭环供应链网络动态模型,利用鲁棒 H∞控制策略和线性矩阵不等式方法,处理供应链网络的鲁棒运作问题． 通过设计依赖于库存状态的反馈控制器,抑制闭环供应链网络中不确定性的影响,使得闭环供应链网络系统运作达到理想的总成本． 最后,通过仿真计算验证所提出方法的有效性．     

需求不确定环境下多个零售商竞争的鲁棒随机优化模型

在需求不确定环境下构建了由一个制造商和多个零售商组成的供应链系统,考虑不同产品的可替代性,建立了多个零售商竞争的随机优化模型。利用鲁棒优化方法研究了需求不确定环境下多个零售商竞争的绝对鲁棒优化问题、偏差鲁棒优化问题和相对鲁棒优化问题。最后通过数值算例比较分析了不同产品替代率下的绝对鲁棒优化解、偏差鲁棒优化解及相对鲁棒优化解。     

不确定环境下两阶段应急供应链网络建模与优化求解

为制订有效的应急供应链网络规划方案,提升应急组织救援效率、实现资源物质合理配置,考虑供应和需求的不确定性,采用多运输方式联合的配送模式,以最小化网络响应时间、成本和碳排为优化目标,构建两阶段应急供应链混合整数规划模型,同时,基于鲁棒优化理论构建可调节鲁棒优化模型,增强网络应对不确定因素的能力,通过线性对偶理论对含不确定参数的约束进行转化;为提升模型的求解效果,提出一种优化布谷鸟搜索（optimize cuckoo search,OCS）算法,引入基准实例,以验证OCS算法求解多目标函数的优越性和适用性;最后,利用武汉新冠疫期期间应急物资配送数据,研究带有不确定参数的应急供应链网络决策问题,并通过敏感性分析证明鲁棒控制系数对不确定扰动的有效抑制作用。     

需求不确定环境下闭环供应链的鲁棒运作策略设计

建立了顾客需求不确定环境下一类同时具有再分销、再制造和再利用的闭环供应链动态运作的鲁棒优化模型。该闭环供应链由一个制造商和一个供应商构成,废旧产品的回收及对废旧产品的再处理均由制造商完成。闭环供应链的运作是动态的且满足诸如供应链成员之间协调、各成员运作收益最大等多个目标。采用具有已知概率的离散情景描述顾客需求的不确定性,利用基于情景分析的鲁棒线性优化方法建立供应链的运作模型。设计了一个数值算例,其结果验证了运作策略的解鲁棒性和模型鲁棒型。     

考虑不确定履约率的全局鲁棒供应组合决策研究

生产和采购的全球化为供应链企业带来了更多的复杂性与不确定性。收集全面信息和使用有效的工具是控制决策风险、减少不确定性危害的有效途径。在供应商选择与订单分配管理中,供应商履约率是影响企业供应组合决策的主要因素,如何对其进行定量刻画是迫切需要解决的问题。已有的研究通常是由期望方法或鲁棒方法来刻画履约率。然而,期望履约率法往往忽略了履约率的波动性,从而造成需求损失;而鲁棒履约率法通常存在过于保守、退化过于迅速两个缺点,使企业承担不必要的成本。本文研究供应商选择与订单分配决策问题,使用全局鲁棒优化的两个不确定集合来刻画供应商履约率,同时给出了这一问题的确定性鲁棒表达和一种多项式时间求解算法。最后,通过仿真实验证实本文的全局鲁棒优化模型可克服期望模型和一般鲁棒模型的缺点。本文提出的算法能有效求解本文全局鲁棒优化等价确定性问题,为企业的供应商选择和订单分配决策提供更为灵活精确的辅助工具。     

供应链鲁棒性问题的研究进展

供应链鲁棒性问题的研究,对于全球化物流供应链、供应链风险运作及对国内企业安全等问题有理论价值和应用价值。分析了供应链系统的外部联接、突发事件和内部运作不确定性的2种表现形式;讨论了供应链鲁棒运作管理,特别考虑了供应链中供应、产品、需求和信息管理的鲁棒性;探讨了供应链鲁棒优化策略以及供应链鲁棒性研究的若干前瞻性问题。     

有限需求信息下基于回购契约的供应链鲁棒协调策略

研究了仅知需求均值和区间信息条件下,基于最小最大后悔值准则的供应链回购契约协调问题。针对未知需求具体分布形式的两级供应链系统,在回购契约框架下,建立了以鲁棒决策和最优决策下的供应链及其成员绩效之差为目标函数的供应链协调模型。在仅知需求区间和均值信息条件下,采用鲁棒优化方法求解了最小最大后悔值准则下的集成供应链鲁棒订货策略和分散供应链鲁棒契约协调策略及其绩效偏差。分析了不同服务水平和契约参数条件下,由于信息缺失而未能实现最优运作的供应链及其成员绩效损失情况。最后,进行了数值计算,验证了通过鲁棒优化方法得到的供应链回购契约协调策略的鲁棒性和有效性。结果表明,基于回购契约的供应链鲁棒协调策略能够有效抑制需求不确定性对系统及其成员运作绩效的影响,同仅知需求区间信息相比,额外获得需求均值信息能够有效改进供应链运作绩效。     

资本成本不确定的供应链的多目标运作鲁棒模型

考虑了由一个制造商和一个供应商构成的多产品、多阶段供应链在资本成本不确定条件下的运作情况。采用区间不确定性描述资本成本的波动,利用鲁棒线性规划方法,建立了供应链运作的多目标鲁棒优化模型,数值算例的结果表明,将鲁棒性运用于供应链的协调运作及供应链成员的目标利润最大的目标中去,能够减少资本成本的不确定性对目标值的影响,得到了资本成本不确定供应链在最坏条件下的最优鲁棒运作策略。     

Bounds of Relative Regret Limit in p‐Robust Supply Chain Network Design

This research studies the p‐robust supply chain network design with uncertain demand and cost scenarios. The optimal design integrates the supplier selection together with the facility location and capacity problem. We provide a new framework to obtain the relative regret limit, which is critical in the robust supply chain design but is assumed to be a known value in the existing literature. We obtain lower and upper bounds for relative regret limit and obtain a sequence of optimal solutions for series relative regret limits between the upper and lower bounds. An algorithm for p‐robust supply chain network design is provided. A series of numerical examples are designed to find the properties of the bottleneck scenarios. A scenario with low probability and a low optimal objective function value for the scenario has a greater chance of being a bottleneck. To focus only on the influence from the relative regret, we also introduce three separate new objective functions in p‐robust design. The proposed new theories and approaches provide a sequence of options for decision makers to reduce the marketing risks effectively in supply chain network design.     

Selection of supply portfolio under disruption risks

This paper deals with the optimal selection of supply portfolio in a make-to-order environment in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order to minimize total cost and mitigate the impact of disruption risks. The selection of suppliers and allocation of orders is based on price and quality of purchased parts and reliability of delivery. The two types of disruption scenarios are considered: scenarios with independent local disruptions of each supplier and scenarios with local and global disruptions that may result in all suppliers disruption simultaneously. The problem is formulated as a single- or bi-objective mixed integer program and a value-at-risk and conditional value-at-risk approach is applied to control the risk of supply disruptions. The proposed portfolio approach is capable of optimizing the supply portfolio by calculating value-at-risk of cost per part and minimizing expected worst-case cost per part simultaneously. Numerical examples are presented and some computational results are reported.     

Supply Chain Sourcing Under Asymmetric Information

We study a supply chain with two suppliers competing over a contract to supply components to a manufacturer. One of the suppliers is a big company for whom the manufacturer's business constitutes a small part of his business. The other supplier is a small company for whom the manufacturer's business constitutes a large portion of his business. We analyze the problem from the perspective of the big supplier and address the following questions: What is the optimal contracting strategy that the big supplier should follow? How does the information about the small supplier's production cost affect the profits and contracting decision? How does the existence of the small supplier affect profits? By studying various information scenarios regarding the small supplier's and the manufacturer's production cost, we show, for example, that the big supplier benefits when the small supplier keeps its production cost private. We quantify the value of information for the big supplier and the manufacturer. We also quantify the cost (value) of the alternative‐sourcing option for the big supplier (the manufacturer). We determine when an alternative‐sourcing option has more impact on profits than information. We conclude with extensions and numerical examples to shed light on how system parameters affect this supply chain.     

Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing

This paper presents a stochastic mixed integer programming approach to integrated supplier selection and customer order scheduling in the presence of supply chain disruption risks, for a single or dual sourcing strategy. The suppliers are assumed to be located in two different geographical regions: in the producer's region (domestic suppliers) and outside the producer's region (foreign suppliers). The supplies are subject to independent random local disruptions that are uniquely associated with a particular supplier and to random semi-global (regional) disruptions that may result in disruption of all suppliers in the same geographical region simultaneously. The domestic suppliers are relatively reliable but more expensive, while the foreign suppliers offer competitive prices, however material flows from these suppliers are more exposed to unexpected disruptions. Given a set of customer orders for products, the decision maker needs to decide which single supplier or which two different suppliers, one from each region, to select for purchasing parts required to complete the customer orders and how to schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The problem objective is either to minimize total cost or to maximize customer service level. The obtained combinatorial stochastic optimization problem will be formulated as a mixed integer program with conditional value-at-risk as a risk measure. The risk-neutral and risk-averse solutions that optimize, respectively average and worst-case performance of a supply chain are compared for a single and dual sourcing strategy and for the two different objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

On the fair optimization of cost and customer service level in a supply chain under disruption risks

This paper presents a new decision-making problem of a fair optimization with respect to the two equally important conflicting objective functions: cost and customer service level, in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers, and schedule the orders over the planning horizon, to equitably optimize expected cost and expected customer service level. The supplies of parts are subject to independent random local and regional disruptions. The fair decision-making aims at achieving the normalized expected cost and customer service level values as much close to each other as possible. The obtained combinatorial stochastic optimization problem is formulated as a stochastic mixed integer program with the ordered weighted averaging aggregation of the two conflicting objective functions. Numerical examples and computational results, in particular comparison with the weighted-sum aggregation of the two objective functions are presented and some managerial insights are reported. The findings indicate that for the minimum cost objective the cheapest supplier is usually selected, and for the maximum service level objective a subset of most reliable and most expensive suppliers is usually chosen, whereas the equitably efficient supply portfolio usually combines the most reliable and the cheapest suppliers. While the minimum cost objective function leads to the largest expected unfulfilled demand and the expected production schedule for the maximum service level follows the customer demand with the smallest expected unfulfilled demand, the equitably efficient solution ensures a reasonable value of expected unfulfilled demand.     

分销网络优化模型及算法研究

为了优化网络结构,寻求最佳配送策略,最终找出成本最小的供应链,针对需求拖动式供应链中,多供应商、多产品、多客户分销配送网络的优化设计问题,在考虑需求分配的情况下,提出了分销配送网络的优化模型。为了求解优化模型,提出了基于混合遗传算法求解混合0-1整数规划问题的算法,它是用遗传算法搜索0-1变量的最优解,将其他算法融入遗传算法中,对非0-1变量进行求解的一种算法。最后通过两个算例进行了仿真实验,初步验证了优化模型和算法的有效性。     

Supply chain management: a supplier perspective

A new way of researching into supply chain management is introduced by adopting a supplier perspective. Details are given of mixed integer linear planning and simulation models. The planning model takes into consideration firm and forecast orders (customer's forecast purchasing orders) and the behaviour of the supplier's suppliers and suppliers' subcontractors. The simulation model takes into account the dynamic behaviour of the supply chain and includes the planning production behaviour of a supplying company based on the planning model. The supplier manager can use the simulation model to determine what kind of parameters most affect company performances and then propose new management rules. Quantitative results that prove the benefits of integrating forecast orders for an aeronautic supplier have been provided.     

随机订单干扰下考虑合并决策的供应链网络调度研究

为满足客户个性化需求的快速响应，企业需具备柔性的外部供应链网络结构，以协同方式共同完成产品生产。本文考虑具有交互特征的多个不同类型协同供应链网络，构建生产成本、库存成本、等待成本以及订单延期交货成本最小化的目标函数，并设计合并决策判断变量构建同类订单在相同协同企业处的开始时间约束。此外，模型中考虑确定订单以及随机订单两种类型订单，并设计随机订单在区间时间段中离散时间点的到达概率。为获取协同供应链网络生产调度优化策略，基于随机订单到达与否的场景构建四个子决策模型，并进一步设计判断提前安排随机订单协同生产和不提前安排随机订单协同生产不同调度策略下成本差异的主决策模型。仿真结果表明合并决策在带来生产成本效益的同时也引起了部分订单的延期交货，且不同类型的协同供应链网络对随机订单的抗干扰能力存在一定程度的差异。     

Supplier Behavior in Capacity Investment Competition: An Experimental Study

Many manufacturers ensure supply capacity by using more than one supplier and sharing their capacity investment costs via supplier development programs. Their suppliers face competitive pressure from peers despite the reduced capacity investment cost. Although standard game theory makes clear prediction that cost sharing increases the suppliers' capacity choice and supply chain profit, the complex decision environment of capacity competition makes it interesting to test whether the theory predictions are robust and, if not, whether systematic deviations occur. We present a laboratory experiment study. The experiment data show that supplier subjects invested in higher capacities than what our theoretical analysis predicted, resulting in profit loss for the supply chain. Our econometric analysis indicates that the subjects are bounded rational and their concern for relative standing may be the potential driver of capacity over‐investment. Based on the experimental findings, we study a modified cost‐sharing mechanism that adapts to the behavioral biases. Its performance is validated in a second experiment.     

Re-positioning the supplier: an SME perspective

As manufactured products become more complex, their design and manufacturing demands more and more resource which is being increasingly shared between the supply/value chain members. Competition is being typified less by firm versus firm and more by supply chain versus supply chain. The final assemblers are simplifying their supply chain to reduce logistics complexity and provide best value to the final customer. To suppliers, particularly SMEs, this gives rise to both opportunities for increasing 'added value', as well as threats of being dropped from rationalized supplier bases. Historically, SMEs have been sandwiched between large customers and suppliers and have always reacted to their superior bargaining power. Now, if the SMEs want to survive and grow, they have to adopt a more proactive stance to re-position themselves in response to these supply chain trends. In this paper, we present a technique for re-positioning the supplier in terms of'physical value added' and competency/practice-based 'differentiation'. We use the proposed approach to see how one supplier has successfully changed his business and another has developed plans to change, in line with the trends in their industry structure.