考虑质量偏好的两阶段服务供应链质量控制策略

研究了一个集成商和一个提供商所组成的服务供应链的质量控制问题。考虑集成商的质量缺陷承诺和提供商的质量偏好,并将提供商的质量活动区分为质量改进和质量保持两个阶段,分别建立了服务供应链成员的效用函数,同时考虑服务供应链成员的竞争与合作,运用博弈理论,优化得到单阶段与两阶段时,集成商的最优服务价格和质量缺陷承诺,以及提供商的最优服务质量改进程度。最后,通过对比分析及数值仿真发现,考虑两阶段质量控制较单阶段情形,更有利于服务供应链较长期限内获得更多效用。其次,服务提供商质量偏好对服务供应链单阶段与两阶段下的最优效用和质量控制策略均产生一定影响。     

基于应急供应链的物流配送计划研究

本文研究了基于应急供应链的物流配送计划问题,针对节点失效采用了供应链扩容策略,建立了在有限资源约束下单目标成本优化数学模型,以实现供应链运行总成本最小。然后,本文用CPLEX软件求解模型得到供应链的扩容方案。数值仿真结果表明本文所提出的扩容方法的有效性和实用性。     

面向客户定制模式的供应链管理系统优化模型

建立供应链管理系统优化模型是构建高效率供应链以及发挥供应链优势的前提和基础 .本文建立了面向客户定制模式的集成的供应链管理系统优化模型 ,即一个多目标、具有约束的非线性混合规划模型 ,并提出了针对这种模型的求解思路 .通过对模型的仿真求解既可以优化选择供应链系统中涉及的相关协作企业 ,同时优化系统的订货、生产、库存策略 ,对构建高效率供应链管理系统具有重要意义 .     

制造商检验的最优质量水平及影响因素研究

为提高我国食品质量安全水平,对小型、分散的单个供应商和一个制造商构成的食品供应链中,制造商对原材料和最终食品进行质量检测的情况,建立了以供应商和制造商各自的质量水平为决策变量的优化模型,分析了最优质量水平的影响因素和影响效应.并依据结论提出相应建议,如提高消费者对优劣食品的识别能力,发挥制造商在供应链中的监督检验作用等.     

考虑港口物流能力的多目标海运煤炭供应链网络优化模型

海运煤炭供应链网络优化旨在提高供应链运作效率并降低成本.以煤矿、铁路装载点、港口和煤炭消费客户构成的海运煤炭供应链为研究对象,从供应链集成服务商的视角,提出了考虑港口物流能力的供应链网络优化问题.在最小化固定成本和运输成本这一单目标的基础上,考虑港口物流能力,增加了最大化港口加权煤炭流量的目标,以各节点的能力限制和流量平衡为约束,建立了海运煤炭供应链网络多目标混合整数规划模型.模型求解时,引入"关系成本系数"将多目标问题转化为单目标问题.算例研究表明,所建立的模型能够真实地反映海运煤炭供应链网络优化的决策环境,同时发现,港口物流能力评分和"关系成本系数"均会影响网络优化结果.     

模糊环境下可控提前期供应链库存优化的Stackelberg模型研究

为了有效地缩短提前期与降低库存成本,研究了模糊环境下可控提前期的供应链库存优化问题.利用三角形模糊数描述需求的不确定性,建立了一类模糊需求条件下可控提前期供应链库存优化的Stackelberg模型.利用三角形模糊数描述成本系数的不确定性,建立了模糊成本系数条件下可控提前期供应链库存优化的Stackelberg模型,并提出利用均值面积度量法来解模糊化.通过数值分析来验证两类模型的优化效果.     

服务大规模定制模式下的供应链调度优化

为了提升服务大规模定制(SMC)模式下供应链系统的运作柔性,应对客户较强的多样化需求特征,本文在对服务定制特征分析、服务阶段界定以及服务规模效应探讨的基础上,指出SCM模式下的供应链调度问题是一个典型的随机需求与随机资源约束的多目标动态优化问题。研究了SMC模式下供应链调度的优化目标与约束条件,建立了完整的随机多目标动态调度优化数学模型。基于SMC运作的特点,运用改进的蚁群算法对调度问题进行了求解。最后,通过实例分析了模型及算法的可行性、有效性及适用性。     

基于Kriging模型的供应链优化设计

供应链优化的目的之一是确定使得总成本最小的最佳运作水平.供应链系统是复杂的动态系统,由于库存系统的复杂性和供应链本身的不确定性,利用传统优化方法往往需要耗费一定的计算成本和经济成本.而元模型则能以简单的数学表达式较精确地刻画仿真系统的输入输出关系,为研究者分析复杂系统提供了一种分析方法.针对供应链优化问题,给出了一种基于计算机试验设计中的元模型一Kriging模型的供应链优化方法,并通过一个三级供应链问题对所提方法进行了实证研究.研究结果表明了所提方法的有效性和可用性,为供应链优化提供了一种新的研究思路.     

复杂动态供应链计划调度中的协作成员选择决策

为了通过合理的协作成员选择决策及任务配给来处理复杂动态供应链计划调度中的不确定性瓶颈问题,本文从复杂动态供应链计划调度模式的基本特征入手,重点分析了成员协作过程中协作期望收益与协作风险的定量判定问题,研究了如何将协作期望收益与协作风险引入协作成员的选择决策过程。在此基础上,建立了复杂动态供应链计划调度的协作成员选择决策优化数学模型并建立了改进的蚁群寻优算法进行求解。最后,通过算例分析验证了决策方法和算法的可行性、有效性与实际可操作性。     

基于期权契约的供应链协调研究

供应链协调与优化一直都是供应链管理的核心议题之一.为分析供应链竞合背景下的供应链协调问题,验证期权契约对供应链的协调效果,文章在分析供应链基本运作的基础上,利用优化理论和方法构建了无契约协调时供应链的基准模型和期权契约下的供应链模型,通过有无契约时供应链及供应链成员利润的对比,分析说明期权契约的协调效果,通过不同决策模式下供应链及供应链成员利润分析,给出期权契约完全协调供应链的条件.在此基础上,进一步引入供应商间和零售商间的跨链补货,验证跨链补货策略对于提高供应链成员利润的有效性.     

目标回扣契约下供应链对突发事件的协调应对

本文的目的就是要研究当突发事件造成需求的市场分布发生变化时,供应链如何运用目标回扣契约进行协调应对.通过设置目标回扣契约参数,分别获得了需求突发下,集权供应链和分权供应链协调的条件.并且,研究结果表明,当突发事件造成需求的市场规模变化不是很大时,分权供应链仅通过调整目标回扣契约就能实现供应链的协调应对;当突发事件造成市场规模变化很大时,则需要采取调整目标回扣契约和改变生产计划两种策略来协调应对.这些结论,为供应链应急管理提供了一种新的应对策略.     

Integrated multi-site aggregate production-pricing planning in a two-echelon supply chain with multiple demand classes

This paper proposes a production and differential pricing decision model in a two-echelon supply chain that involves a demand from two or more market segments. In this framework, the retailer is allowed to set different prices during the planning horizon. While integrated production-marketing management has been a key research issue in supply chain management for a long time, little attention has been given to set prices and marketing expenditures in integrated multi-site (parallel) manufacturing systems and multiple demand classes. Generally, the presence of multiple demand classes induced by different market segments may impose demand leakage and then change production plan and ordering policies throughout the supply chain system. To tackle this problem, this paper develops a novel approach in order to provide an optimal aggregate production and marketing plan by interconnecting the sales channels of the retailer and demand. A non-linear model is established to determine optimal price differentiation, marketing expenditures and production plans of manufacturing sites in a multi-period, multi-product and multi-sale channels production planning problem by maximizing total profit of the supply chain. To handle the model and obtain solutions, we propose an efficient analytical model based upon convex hulls. Finally, we apply the proposed procedure to a clothing company in order to show usefulness and significance of the model and solution method.     

A transient stochastic simulation–optimization model for operational fuel planning in-theater

Army fuel planners are responsible for developing daily loading plans that specify which tankers to load, with what fuel, and where to send the loaded tankers. The tools used to accomplish this task are custom built spreadsheets which require large amounts of time and effort to use, update, and keep free of errors. This research presents a transient stochastic simulation–optimization model of the in-theater bulk fuel supply chain, where the simulation model is used to simulate the performance of the fuel supply chain under a particular fuel distribution policy and the optimization portion is used to update the policy so that it results in the performance desired by the Army fuel planner. The fuel distribution policy can then be used to derive the daily loading plan. Due to the multi-objective nature of the problem, the set of policies that form the efficient frontier are all candidate policies for the Army fuel planner to select from. Results of experimentation with a wide variety of supply chain scenarios indicate that, for a given supply chain scenario, the optimization portion of the model identifies a set of fuel distribution policies that address the objectives of the Army fuel planner. In addition, the simulation–optimization model comfortably solves the largest supply chain scenarios the Army fuel planner would reasonably be expected to encounter.     

The warehouse-inventory-transportation problem for supply chains

Warehouses play a vital role in mitigating variations in supply and demand, and in providing value-added services in a supply chain. However, our observation of supply chain practice reveals that warehousing decisions are not included when developing a distribution plan for the supply chain. This lack of integration has resulted in a substantial variation in workload (42–220%) at our industry partner’s warehouse costing them millions of dollars. To address this real-world challenge, we introduce the warehouse-inventory-transportation problem (WITP) of determining an optimal distribution plan from vendors to customers via one or more warehouses in order to minimize the total distribution cost. We present a nonlinear integer programming model for the WITP considering supply chains with multiple vendors, stores, products, and time-periods, and one warehouse. The model also considers worker congestion at the warehouse that could affect worker productivity. A heuristic based on iterative local search is developed to solve industry-sized problems with up to 500 stores and 1000 products. Our experiments indicate that the distribution plans obtained via the WITP, as compared to a sequential approach, result in a substantial reduction in workload variance at the warehouse, while considerably reducing the total distribution cost. These plans, however, are sensitive to aisle configuration and technology at the warehouse, and the level and productivity of temporary workers.     

The research on optimization of auto supply chain network robust model under macroeconomic fluctuations

Considering the uncertainty of the macroeconomic environment, the robust optimization method is studied for constructing and designing the automotive supply chain network, and based on the definition of robust solution a robust optimization model is built for integrated supply chain network design that consists of supplier selection problem and facility location–distribution problem. The tabu search algorithm is proposed for supply chain node configuration, analyzing the influence of the level of uncertainty on robust results, and by comparing the performance of supply chain network design through the stochastic programming model and robustness optimize model, on this basis, determining the rational layout of supply chain network under macroeconomic fluctuations. At last the contrastive test result validates that the performance of tabu search algorithm is outstanding on convergence and computational time. Meanwhile it is indicated that the robust optimization model can reduce investment risks effectively when it is applied to supply chain network design.     

A practical solution approach for the cyclic inventory routing problem

Vendor managed inventory (VMI) is an example of effective cooperation and partnering practices between up- and downstream stages in a supply chain. In VMI, the supplier takes the responsibility for replenishing his customers’ inventories based on their consumption data, with the aim of optimizing the over all distribution and inventory costs throughout the supply chain. This paper discusses the challenging optimization problem that arises in this context, known as the inventory routing problem (IRP). The objective of this IRP problem is to determine a distribution plan that minimizes average distribution and inventory costs without causing any stock-out at the customers. Deterministic constant customer demand rates are assumed and therefore, a long-term cyclical approach is adopted, integrating fleet sizing, vehicle routing, and inventory management. Further, realistic side-constraints such as limited storage capacities, driving time restrictions and constant replenishment intervals are taken into account. A heuristic solution approach is proposed, analyzed and evaluated against a comparable state-of-the-art heuristic.     

非对称信息下供应链批发价格契约应对突发事件

针对两个制造商与一个零售商组成的二级供应链,建立了制造商一方与零售商联盟,制造商另一方与之竞争、零售商类型为其私人信息下的博弈模型,从系统优化的角度研究了供应链系统批发价格如何应对突发事件.首先证明稳定条件下,零售商成本信息为非对称信息时,批发价格契约能实现集中控制与分散控制下-该供应链的协调.当突发事件造成零售商的成本分布变化时,通过求解突发事件下供应链系统最优利润的Karush-Kuhn-Tucker(KKT)条件,提出了供应链应对突发事件的最优策略,进一步证明了改进的批发价格契约可以协调突发事件下分散化决策的供应链系统.数值仿真表明,当零售商的成本在一个较小的范围内扰动时,原有契约机制具有一定的鲁棒性,而当扰动较大时,需要对原来的生产计划进行调整.     

时间约束下鲜活农产品供应链应急协调数量折扣契约研究

本文的目的就是针对一个由生产商和一个零售商构成的鲜活农产品供应链,在考虑损耗和新鲜度的影响下,研究数量折扣契约如何协调供应链应对突发事件。 首先,给出了正常情况下供应链协调模型;然后,在突发事件引起市场需求、零售商订货成本和产品运输时间同时扰动情况下,讨论了突发事件对集权、分权供应链的影响。 研究表明,集权供应链具有一定的鲁棒性,当突发事件造成市场需求、零售商订货成本和产品运输时间在一定范围内同时发生扰动时,其生产计划不需要调整,只需通过调整产品价格即可使集权供应链达到协调;对于分权供应链,根据三因素同时扰动的不同程度,通过调整数量折扣契约参数,可以实现对供应链的协调。 最后给出数值算例验证了相关结论。     

Optimizing the supply chain configuration for make-to-order manufacturing

We consider a make-to-order (MTO) manufacturer who has won multiple contracts with specified quantities to be delivered by certain due dates. Before production starts, the company must configure its supply chain and make sourcing decisions. It also needs to plan the starting time for each production task under limited availability of resources such as machines and workforce. We develop a model for simultaneously optimizing such sourcing and planning decisions while exploiting their tradeoffs. The resulting multi-mode resource-constrained project scheduling problem (MMRCPSP) with a nonlinear objective function is NP-complete. To efficiently solve it, a hybrid Benders decomposition (HBD) algorithm combining the strengths of both mathematical programming and constraint programming is developed. The HBD exploits the structure of the model formulation and decomposes it into a relaxed master problem handled by mixed-integer nonlinear programming (MINLP), and a scheduling feasibility sub-problem handled by constraint programming (CP). Cuts are iteratively generated by solving the feasibility sub-problem and added back to the relaxed master problem, until an optimal solution is found or infeasibility is proved. Computational experiments are conducted to examine performance of the model and algorithm. Insights about optimal configuration of MTO supply chains are drawn and discussed.     

基于改进的粒子群算法求解供应链网络均衡问题

传统的供应链求解方法为投影法,针对其要对投影进行计算,十分复杂的缺点,提出用改进的粒子群算法求解供应链均衡问题,利用动态异步调整学习因子来有效的提高了算法搜索能力与精度。本文介绍了供应链网络均衡问题转变为无约束优化问题的方法,然后用改进的粒子群优化算法进行求解。通过四个数值算例,将实验结果与标准粒子群算法、蜂群算法、学习因子同步变化的粒子群算法进行比较,验证了改进的粒子群优化算法在解决供应链网络均衡问题中的有效性与优越性,为供应链网络求解提供了一种新的方法。