Planning, Coordination, and Control of Supply Chains

提出的工作的主要研究目标是开发一种方法，为分散但协调的供应链管理和控制提供实用有效的框架。为此，建议解决一系列关键挑战：(i)动态建模交货期；（ii）为供应链客户提供服务质量（QoS）保障；（iii）通过采用需求和容量随机模型准确地模拟不确定性，这些模型捕获了这些过程中固有的强时间依赖性；（iv）有效分配资源以管理交通挤塞。为了达到这些目标，建议的工作将借鉴随机系统理论、仿真、动态优化和最优控制。为了处理计算复杂性，计划利用具有不同范围和功能的决策之间的时间尺度分解，并使用分析近似来估计感兴趣的复杂数量（例如，大偏差技术）。如果成功，这项研究将有助于信息和生产系统工程，以及它们在制造和分销行业中使生产力显著增长的潜力。这将导致供应链显著缩短交货时间，采用库存控制算法，能够保证理想的服务水平，并最大限度地减少预期的库存成本。此外，该研究将有助于随机加工网络中的调度和路由，从而使供应链各阶段的操作更加精简。拟议的工作还将：(i)有助于培养研究生和加强波士顿大学制造工程研究生课程；（ii）利用美国国家科学基金会颁发给波士顿大学的科学大使奖来接触高中生。最后，除了通常的传播建议工作成果的方法（出版物、会议报告、邀请讲座等）之外，pi计划利用他们与最近成立的BU信息和系统工程中心（CISE）的联系来与附属公司合作。

Abstract The primary research objective of the proposed work is to develop methodology that enables a practical and effective framework for decentralized, yet coordinated, management and control of supply chains. To that end, it is proposed to address a host of key challenges: (i) model lead-times dynamically; (ii) provide Quality-of-Service (QoS) guarantees to supply chain customers; (iii) model uncertainty accurately by adopting demand and capacity stochastic models that capture strong temporal dependencies inherent in these processes; and (iv) employ efficient resource allocation in order to manage congestion. To meet these objectives the proposed work will draw upon stochastic system theory, simulation, dynamic optimization, and optimal control. To deal with computational complexities it is planned to exploit the time scale decomposition among decisions with different scope and functionality, and employ analytical approximations to estimate complex quantities of interest (e.g., large deviations techniques). If successful, this research will contribute to information and production systems engineering, and their potential to enable significant productivity growth in the manufacturing and distribution industries. It will lead to supply chains with significantly reduced lead-times that employ inventory control algorithms able to guarantee desirable service levels and minimize expected inventory costs. Moreover, the research will make contributions to the scheduling and routing in stochastic processing networks, leading to more streamlined operations at the various stages of the supply chain. The proposed work will also: (i) contribute to the training of graduate students and the enhancement of the graduate curriculum in Manufacturing Engineering at Boston University (BU); and (ii) leverage an NSF science ambassador award to BU to reach out to high school students.Finally, and in addition to the usual means of disseminating the outcomes of the proposed work (publications, presentations at conferences, invited lectures, etc.), the PIs plan to use their association with the recently established BU Center for Information and Systems Engineering (CISE) to work with affiliated companies.