Dr. Chanaka Edirisinghe, Professor
Haslam College of Business
Heath Fellow
University of Tennessee
January 31, 2014, 2:30 – 3:30 PM
500 John D. Tickle Building
Dr. Chanaka Edirisinghe is a Professor of Management Science and Heath Endowed Fellow of Business and Engineering at UT. He is also the Director of the Financial Engineering Research Laboratory, director of the PhD program in Management Science, and a co-director of the Business Analytics Forum. His research is focused on stochastic optimization theory and practice and applications in financial analytics, project management, supply chain coordination, hydro-reservoir planning, and inventory routing. He has published his work in several top-tier journals such as Operations Research, Management Science, Mathematics of Operations Research, and Mathematical Programming, as well as Journal of Financial and Quantitative Analysis and Journal of Banking and Finance. He received the Citation of Excellence Award for his research by Emerald Management Reviews in 2009, and was the recipient of Sarah Alice and Tommy Bronson Outstanding Researcher Award at College of Business in 2010. Dr. Edirisinghe is a former Vice-Chair of the Financial Services Section of INFORMS, a former Vice-Chair of the Optimization Society of INFORMS, and the General Chair of INFORMS-2016 National Meeting.
Talk Abstract: This presentation is focused on the problem of inventory removal from various production sites that have limited storage capacity. Inability for timely removal forces expensive production shutdowns, or the products may become obsolete due to finite shelf-life. Continuous and finite production rates are considered and a fleet of vehicles need to be scheduled to transport the product from plants to a central storage. In order to avoid shutdowns (or product expiry), vehicles may have to make multiple visits to a given plant before returning to the depot. One operational objective is to achieve the highest possible rate of product retrieval at the depot, relative to the total travel time of the fleet. This problem is a variant (and generalization) of the standard inventory routing problem (IRP) or the pickup and delivery problem (PDP). The motivating application for this paper is barge scheduling for oil pickup from off-shore oil-producing platforms with limited holding capacity, where shutdowns are prohibitively expensive. A new “position-based” (PB) mixed-integer optimization model is developed, and it is fundamentally different from the standard node-arc or path formulations in the literature. Our approach leads to substantial flexibility in modeling multiple visits to a node using multiple vehicles, whilst controlling the number of binary variables. Consequently, solution of the PB model is significantly more efficient than the standard node-arc counterpart. Computational experience with the off-shore barge scheduling application is presented.