Supply chain optimization under uncertainty

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Many of the challenges in supply chain planning systems stem from the presence of several sources of uncertainty such as demand, lead time and costs of services and operations to name a few. Failure to account for these uncertainties can result in a reduced service level, loss of demand and significant extra expenses to recover from unplanned outcomes. Our research focuses on the design of decision algorithms for supply chain systems that can incorporate uncertainty which can be extracted from historical data and can make use of data analytics in order to help the planners make risk informed decisions and proactively react to the uncertainty. The first research theme focuses on the integration of distribution network design and inventory optimization, two critical components in the supply chain strategic and tactical planning that have strong interactions. This research theme involves multiple solution methodologies for optimization under uncertainty together with enhancements from data mining techniques. The second research theme concerns the development of solution algorithms for stochastic combinatorial optimization, a very challenging problem in operations research, which can be applied to several logistics and transportation applications such as vehicle routing. The third research theme involves algorithmic designs for the dynamic production and inventory routing that can be used to deal with situations where important inputs such as demand is revealed in a dynamic fashion, as well as when there are immediate changes in the operational circumstances or inputs, and thus a revised solution must be quickly determined. We aim to 1) provide efficient interdisciplinary solution frameworks to deal with these challenging issues, 2) while focusing on practical and innovative applications that can lead to new research developments in the fields and yet 3) providing insights and implications across different solution methodologies for supply chain practitioners. We plan to work on some of the projects with industrial partners who will provide test instances and practical points of view for the scientific developments.