PI: Larry Snyder
Co-PI(s): N/A
University: Lehigh University
Industry partner: Dow
This project studies complex, multi-echelon supply chains. Chemical industry supply chains are typically much more complex—both in scale and in type—than models that exist in the academic literature. Our project is motivated by the need to capture much more of the realities of such supply chains in an environment that allows users both to experiment with the supply chain itself and to test approaches for optimizing inventories and other elements. We recently began a collaboration with our industrial partner, Dow, the aim of which is to develop an open-source simulation package capable of simulating complex supply chains, as well as a benchmark dataset that is synthetic but similar to chemical supply chains. PITA funding will allow us to expand the scope of this project to explore a third important component, namely, models for optimizing inventories within the supply chain. The project thus has three main components: (1) a simulation package that can handle large, complex supply chains in a reasonable amount of computation time; (2) one or more benchmark datasets that are representative of typical chemical-industry supply chains; and (3) an exploration of heuristics for multi-echelon inventory optimization (MEIO), using the simulation and benchmark datasets developed in tasks (1) and (2). Tasks (1) and (2) are part of the existing project and will be conducted primarily by an ISE Ph.D. student (one student is currently working on the project but will graduate in the spring; a second will be hired in the spring to take over the project). Task (3) will be supported by PITA and conducted also by the Ph.D. student, with support from an undergraduate student, who will also conduct testing and numerical experiments, and who will interact both with Dow and with the Lehigh team. The results of all three tasks will be made open source so that other researchers and practitioners can benefit from them.