Dr. Pitu Mirchandani
Professor
Computing, Informatics, and Decision Systems Engineering
Arizona State University
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Dr. Mirchandani [UCLA, BS/MS in Engineering; MIT, SM (Aero and Astro) and ScD in Operations Research] is a professor of computing, Informatics, Decision Systems Engineering and the AVNET Chair for Supply Chain Networks. He is the director of the Advanced Traffic and Logistics Algorithms and Systems Laboratory (ATLAS), the chief scientist of the DHS Center for Accelerating Operations Efficiency, and a senior scientist at ASU's Global Institute of Sustainability. Mirchandani has extensive background in Optimization (including network optimization, AI, and complexity guaranteed heuristics), Predictive Analytics (including applied statistics, estimation methods and machine learning) and Stochastic Control. He has used his expertise to develop concepts of Dynamic Stochastic Networks and their management and control, and is interested in developing models and systems for making strategic/tactical/operational decisions in stochastic networked environments, with applications typically related to transportation and logistics. He is a Fellow of INFORMS and Fellow of IEEE.
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Abstract: Just as emergency public safety systems, such as ambulance, fire protection, and police, which are road-network based with associated traffic management protocols and logistics support systems, we have been developing complementary emergency systems that are airborne, primarily using helicopters, for emergency humanitarian assistance, for example search-and-rescue missions, delivery of emergency goods, and emergency medical assistance. Given the rapid developments in drone technologies, this talk argues that drones and associated logistical systems can cost-effectively address some problems of emergency delivery of medicine and other items to patients that are remote and not easily accessible via roads from medical facilities and medicine inventories. The general problem is as follows: after a disaster like a major hurricane and discovery of patient demands, how can we meet the resulting “To-Do” list, where each item may be perishable, (i.e., its utility decreases with time), may have a deadline for delivery, and may have list of possible substitutions from the available inventories? Item pick-ups by a drone (e.g., blood samples) may also be on the to-do list.
Given the large number of possible scenarios for the general problem, this talk addresses in detail only the following specific problem. We have delivery drones to serve remote demand points that need delivery of emergency medical supplies such as blood units. These demand points are reachable only by drones where each of the drones is constrained by a limited distance range to service a demand point. The drones operate out of mobile platforms which may be moved on usable roads. Each demand point requires a single package of product whose utility decreases with delivery time due to product perishability. Furthermore a customer’s demand may have a delivery time deadline. The main problem addressed is to locate p platforms, and their associated drones, so that the total disutility is minimized. We study two cases: (1) the one-day case where platforms are moved to the optimal location set to service the discovered demands, and (2) the two-day case where the platforms may be moved to other available locations on the night of the first day. Problem formulations and some computational experiments are discussed.
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