基于共同补给期的直运型供应链库存协调模型及仿真

研究了二级直运供应链通过共同补给期协调库存的策略问题。在该供应链中，单一的供应商向面临随机需求的多个零售商提供单一产品。在提出的库存协调策略下，当零售商接受供应商提供的共同补给期时，供应商则提供给零售商一定的价格折扣。最后，通过数值仿真分析，验证了该策略可以降低供应商的成本，而零售商成本的增加则可以通过价格折扣来补偿，从而显著地降低了供应链的总成本。     

季节性商品两级供应链契约设计与协调模型研究综述

在季节性商品销售环境下,讨论了单节点成员两级供应链契约设计与协调模型研究状况。在此基础上,讨论了多供应商对单零售商、单供应商对多零售商、多供应商对多零售商等不同供应链结构下的契约设计与协调模型研究现状。综述了复杂决策环境对契约设计与供应链协调的影响。总结了目前的研究现状,并提出了今后的研究方向。     

基于RFID技术的易逝品供应链库存协调

针对易逝品供应链节点企业库存数据不准确的问题,考虑采用射频识别技术和不采用射频识别技术两种情形,建立了集中决策下供应链系统以及分散决策下零售商、供应商的订货（生产决策）模型;分析了分散决策下节点企业的最优策略。通过分析和比较采用射频识别技术前后的最优收益,确定了射频识别技术在供应链中的应用条件和应用策略;进一步建立了推进射频识别技术应用的供应链协调模型,给出了上下游企业达到协调的条件。最后通过算例分析,验证了射频识别技术应用策略与供应链协调策略的有效性。     

基于斯坦克尔伯格博弈的入厂物流利益协调问题

为探讨入厂物流的利益协调问题,提出了由一个制造商和两个零部件供应商组成的两阶供应链.以零部件供应商库存成本和制造商运输成本最小化为目标,以博弈论为基础,建立了由制造商为主方,零部件供应商为从方的两阶斯坦科尔伯格主从博弈模型.制造商采取共同订货期为基础订货和循环取货的方式,并通过相应激励来诱使供应商参与合作.该供应链协调策略实现了两阶供应链库存一配送的联合优化.通过求解斯坦克尔伯格博弈模型,得出了最优的共同订货期.订货期倍数和激励奖金的数值解.最后给出了算法和仿真分析.仿真分析讨论了相关参数变化对制造商成本、零部件供应商成本和系统总成本的影响.     

生产成本和需求同时扰动下供应链的协调

为提高供应链的运作效率,在市场需求为零售价格的线性函数假设下,考虑一个由单供应商单零售商组成的两级供应链,当干扰事件造成生产成本、市场规模和价格敏感系数同时扰动时,构建了一个两级供应链的博弈模型.研究了在集中化决策下供应链的最优应对措施,以及分散化决策下当供应商为Stackelberg博弈主导者情形时的供应链的协调机制.研究结果表明,在稳定情形时供应链制定的生产计划有一定的鲁棒性,但当扰动超过一定的范围时,供应链应该调整生产计划并设计新的契约才能达到协调.通过算例验证了相关结论.     

不确定需求下考虑自由退货的供应链协调

针对不确定需求下自由退货政策的供应链协调问题,构建了考虑自由退货与不考虑自由退货两种协调策略,分别建立了随机需求下由单个制造商和单个零售商构成两级供应链的分散决策模型,比较了两种协调策略最优解的实现条件,得出了在顾客需求的分布函数为增函数时,分散协调决策过程中不考虑自由退货的协调策略优于考虑自由退货的协调策略,并设计了回购合同机制,可实现自由退货下的供应链协调.通过数值算例分析研究了两种协调策略下参数的变化对供应链协调绩效的影响.     

基于斯坦克尔伯格博弈的订货提前期决策研究

研究了供应链中供应商和零售商两级间的订货提前期决策问题.考虑现实中常见而研究较少的供应商决定交货时间和零售商决定订货量的订货过程,在提前期管理成本由上下游分担的条件下,建立了基于斯坦克尔伯格博弈的订货提前期模型,对比分析了供应商先动和零售商先动情形下的提前期和订货批量决策,说明了先行公布提前期对降低供应商成本和缩短供应链中的提前期是有利的.最后,通过算例对模型和结论进行了验证.     

考虑需求信息更新的易逝品的订货策略

易逝品供应链管理的一个核心问题是，零售商如何根据制造商提供的订货条件进行合理订货。研究了考虑顾客需求信息更新的易逝品的零售商订货策略，制造商通过为零售商提供两次订货机会，以实现制造商和零售商的共赢。通过对开始时刻和顾客需求信息更新后的系统进行建模和讨论，得到了在两次订货条件下零售商应该采取的最优的订货与调整策略。     

随机需求多渠道供应链Stackelberg协调博弈分析

研究具有随机需求和涉及产品转移成本的,由一主导制造企业、电子零售商和传统零售商构成的多渠道供应链协调问题。通过建立Stackelberg博弈模型,分析了集中决策和分散决策模式下多渠道供应链中成员间的价格竞争和产品转移成本对企业生产计划与定价决策的影响;构建了多渠道供应链协调的激励惩罚机制。研究表明,通过该激励惩罚机制可使零售商的最优订货量与集中决策下的最优订货量相等,而通过与支付转移契约的组合运用可实现多渠道供应链的完全协调。     

收益分享契约对供应链性能的影响分析

为优化季节性商品两级供应链的性能,引入收益分享契约.供应商通过分享零售商的销售收入,可以有效地降低批发价,吸引零售商订购更多的商品,并且供应商通过调节批发价和收益分享因子可以在协调供应链的前提下任意划分供应链的总利润.讨论了收益分享契约在供应商管理库存模式下的应用,找到了优化供应链性能的决策集.进一步分析了批发价为0时供应链性能的优化策略,通过引人剩余补贴因子实现了在协凋供应链的同时任意分配供应链的总利润.最后用数值算例对结论进行了说明.     

模糊需求环境下供应链成员间的协调机制分析

为保证供应链成员获得利润,在模糊需求环境下,研究由单一供应商和零售商组成的两级供应链系统的协调机制问题。将市场需求视为模糊变量,建立不确定理论下基于可信性分布的分散决策、集中决策,以及收益共享契约和回购契约模型,并以三角模糊变量为例对模型进行优化,给出各种模型下的最优策略。最后,通过数值算例对模型中的参数进行求解,并对分析结论进行验证。研究结果表明,在模糊需求环境下,供应链系统中依然存在双重边际化效应,通过在协调机制中改变契约系数可以使供应链各成员的期望利润相比分散决策时有明显提高,并且当谈判双方达成纳什谈判解时,成员在收益共享契约下的期望利润等于其在回购契约下的期望利润。     

需求信息不对称下基于期权的供应链协作机制的研究

通过批发价格契约分析了信息不对称导致供应链效率下降的机理，运用信号博弈理论设计相应的期权契约，激励具有信息优势的零售商，通过其期权购买量向供应商传递真实的市场需求信息，并据此优化供应商的产能和价格决策，以及零售商的期权购买策略，最终达到供应链的协作。最后，对各决策模型和协调过程分别进行了详细的仿真分析，验证了期权契约在需求信息不对称条件下的供应链协调过程中的有效性，并探讨了进一步的研究方向。     

Synchronization in multi-echelon supply chain applying timing discount

Due to the increasing emphasis on the effective management of the supply chain, synchronization and cooperation issues between suppliers and retailers in decentralized multi-echelon inventory/distribution systems have gained much attention in the recent years. In this paper, we consider coordination issues of a distribution system composed of a manufacturer, a supplier (distributor), and several retailers. The supplier outsourcing a third party offers a timing discount to multiple retailers in order to synchronize the timing of their orders with the order cycle. It is also assumed that retailers are allowed to face stock outs. In this paper, a mathematical model is developed to analyze the problem. Results show that while synchronizing the supplier and the outsourcer enhances the supply chain efficiency, offering any type of timing discount by the supplier decreases the supply chain efficiency. It is also noted that having coordination between the manufacturer and the supplier has no impact on the supplier’s profit but may decrease the retailer’s costs. It is believed that our findings provide potential and significant managerial implications in the area of supply chain coordination when these systems are decentralized.     

Simultaneous coordination of capacity building and price decisions in a decentralized supply chain

In this paper, we investigate the simultaneous coordination of price and capacity building decisions in a dyadic supply chain. This problem is a combination of capacity reservation problem and pricing problem. While the coordination of supply chain with stochastic demand and fixed prices has received much attention in the literature, price-dependent and stochastic demand has been less considered. We study the latter case where a price-setting retailer faces a linear decreasing demand with respect to price. To capture the uncertainty of the demand, we add a stochastic variable to the demand function. In addition, we incorporate production rate and inventory cost on the supplier side. We propose Revenue Sharing Reservation Contract with Penalty (RSRP) as a coordination mechanism to align the price and capacity decisions. We then extend the model to include multiple retailers which are geographically dispersed. We next conduct a comprehensive numerical example with an extensive sensitivity analysis to understand the behavior and robustness of the supply chain under a RSRP, and finally, we draw some managerial implications.     

基于熵的直运型供应链库存协调研究

为解决不对称信息下直运型供应链库存协调问题，提出了一种基于信息熵的库存协调方法。首先，研究了由一个供应商和一个分销商组成的直运型供应链系统，给出了库存保管成本信息不对称下的库存协调补偿机制，并从供应商角度提出了优化模型。模型中含有一个与供应商所掌握信息量相关的参数。为选择恰当的模型参数，提出了运用极大熵准则，确定库存保管成本分布概率的基本方法。最后，给出了一个应用示例。计算结果表明，协调后供应商和分销商的利润都有一定程度的增加。     

Mitigating the bullwhip effect by reducing demand variance in the supply chain

To coordinate an agile manufacturing system with the high-level requirements of marketing services, supply chain management (SCM) plays an important role in management activities for enterprise globalization. In SCM, a phenomenon called the “bullwhip effect” has attracted considerable attention. Beyond the previous studies, we consider multiple retailers with one supplier in the supply chain and examine ways in which the supplier can reduce the total demand variance by adjusting retailers’ order sizes. The results reveal that the total demand variance can be reduced by using portfolio theory to adjust the order sizes based on the variance of order of the retailer. Therefore, the bullwhip effect can be partially reduced by our proposed method .     

"批对批"供应链在信息不对称下的协调机制

由一个供应商和一个分销商组成的二级供应链系统，当该系统面对单品种的确定性外部需求时，首先给出了能够激励自私的供应链成员实现供应链联合最优的合作协调机制；在分销商的存贮费为不对称信息的情况下，分析了上述机制的局限性，并运用激励理论，提出激励供应链成员诚实申报信息的新的协调机制，实现了供应链系统的联合最优化。     

大规模定制环境下供应链实施延迟生产的成本优化模型

为成功实施延迟生产以实现供应链成本的优化,大规模定制环境下供应链各节点企业需要紧密协作.针对一条包括制造商和供应商两个节点企业的供应链.建立了大规模定制环境下供应链实施延迟生产的成本优化模型.以最小化供应链生产成本为目标,研究了供应商对客户订单分离点决策的影响,以及大规模定制环境下供应链实施延迟生产时客户订单分离点的最佳位置,指出决策客户订单分离点时应考虑供应商成本.最后,对该生产成本优化模型进行了仿真.     

Multi-objective supply chain sourcing strategy design under risk using PSO and simulation

Sourcing strategy design in a supply chain is vital to gain competitive advantage. In recent years, supply chain risks are growing significantly and supplier failure is identified as one of the top supply chain risks. Researchers attempt to mitigate the negative impacts of supplier failure by applying strategies such as local versus global sourcing, single versus dual/multiple-sourcing, performance-based supply contracts, and optimizing the order allocation among suppliers. Global sourcing is a widely recognized strategy among firms, and it involves a trade-off between reliable, high-cost local suppliers and unreliable, low-cost offshore suppliers. The global sourcing is associated with the risks of exchange rate volatility, trade restrictions, longer lead time, and problems with supplier reliability. Sourcing strategy design considering price, exchange rate risks, and supplier delivery reliability is an important research topic and needs attention. In this work, a hybrid optimization and simulation approach is proposed to design the supply chain sourcing strategy. In the optimization approach, a multi-objective binary particle swarm algorithm is developed for minimizing the total cost and maximizing the supplier delivery reliability. Selected scenarios from the optimization results are modeled using Witness simulation software to evaluate the robustness of sourcing strategies under price, exchange rate and demand risks. The proposed approach is exemplified using a real-life case study of a plastic product manufacture in India.