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IEOR Seminar by Prof. S. Raghavan

Title: The Driver-Aide Problem: Coordinated Logistics for Last-Mile Delivery

Speaker: Prof. S. Raghavan, University of Maryland. 

Day, Date, Time: Wednesday, 23rd November, 2022, 10 30 am to 11 30 am. 

Venue: Seminar Hall, Second floor, IEOR Building. 

Abstract: Last-mile delivery is a critical component of logistics networks accounting for approximately 30-35% of costs. As delivery volumes have increased, truck route times have become unsustainably long. To address this issue, many logistics companies, including FedEx and UPS, have resorted to using a “Driver-Aide” to assist with deliveries. In the “Driver-Aide Problem”, a truck is equipped with both a “driver” and an “aide”. The aide can assist the driver in two ways. As a “Jumper”, the aide works with the driver in preparing and delivering packages, thus reducing the service time at a given stop. As a “Helper”, the aide can independently work at a location delivering packages, while the driver leaves to deliver packages at other locations and then returns. Given a set of delivery locations, travel times, service times, and the jumper's savings, the goal is to determine both the delivery route and the most effective way to use the aide (e.g., sometimes as a jumper and sometimes as a helper) to minimize the total delivery time. We model this problem as an integer program with an exponential number of variables and an exponential number of constraints, and propose a branch-cut-and-price approach for solving it. Our computational experiments are based on simulated instances built on real-world data provided by an industrial partner. More importantly, our results characterize the conditions in which this novel operation mode can lead to significant savings in terms of both completion time and cost. Our computational results show that the driver-aide with both jumper and helper modes is most effective when there are denser service regions and when the truck's speed is higher (>= 10 MPH). Coupled with an economic analysis, we come up with rules of thumb that could be used in practice. We find that the service delivery routes with greater than 50% of the time devoted to delivery (as opposed to driving) are the ones that provide the greatest benefit. These routes are characterized by a high density of delivery locations.

Speaker Bio: Dr. Raghavan is passionate about using quantitative methods for better decision making. He enjoys teaching business-analytics courses, and is a recipient of many teaching awards. These include (i) the INFORMS Prize for the Teaching of OR/MS Practice, (ii) the Legg-Mason Teaching Innovation award at the Smith School, and (iii) several Smith School Distinguished Teaching Awards. His research interests and activities cover a broad domain including---computational marketing, healthcare operations, information systems, market design, network analytics, optimization, supply chain management, and urban logistics. He has published on a wide variety of topics and numerous academic outlets such as Computers & Operations Research, Decision Sciences, Discrete Applied Mathematics, INFORMS Journal on Computing, Management Science, Networks, Operations Research, and Transportation Science. He holds two patents, and has won numerous awards for his work. These include (i) the Dantzig award for the best doctoral dissertation, (ii) the INFORMS Computing Society Prize (twice); once for innovative contributions to the field of data mining, and a second time for his contributions to public sector auction design, (iii) the Glover-Klingman Prize for the best paper in the journal Networks, (iv) the Management Science Strategic Innovation Prize by the European Operations Research Society, (v) the INFORMS Telecommunications Section Best Paper Award, (vi) 2nd Prize in the INFORMS Junior Faculty Paper Competition, (vii) Finalist for the European Operations Research Society Excellence in Practice Award, and (viii) Finalist for the Wagner Prize for Excellence in Operations Research Practice. Prior to joining the Smith School he led the Optimization Group at US WEST Advanced Technologies.