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.
