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VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Dynamic Discretization Discovery
DTSTART:20180427T121500
DTEND:20180427T131500
DTSTAMP:20260408T033551Z
UID:e5fb201768f86404f76df7d503690b11c6018c459c55e2dba0a0e644
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Michael Hewitt\, Associate Professor\, Director\, Informati
 on Systems and Supply Chain Management Dept\, Quinlan School of Business\,
  Loyola University\, Chicago\, USA\nTime-expanded networks are a useful to
 ol from both modeling and computational perspectives. In terms of modeling
 \, they enable a natural method for representing decisions that have both 
 a geographic and temporal component. In terms of computation\, they yield 
 stronger integer programming formulations than those that represent time w
 ith continuous variables\, which in turn require less time to solve. A dra
 wback to the use of time-expanded networks is that they require time to be
  discretized.  While finer discretizations yield more precise representat
 ions of time\, they also lead to larger optimization models which may then
  require too much time to solve. However\, this trade-off is primarily a f
 unction of choosing a discretization in a static and a priori manner. In t
 his talk\, we will present a method that generates time expanded networks 
 in an iterative and dynamic fashion in the context of solving an optimizat
 ion model that prescribe actions in both time and space. We will illustrat
 e the use of this method on two classical problems seen in transportation 
 and logistics: (1) the Service Network Design problem\, which can be used 
 to model the routing of goods between cities\, and\, (2) the Traveling Sal
 esman Problem with Time Windows\, which can be used to model the routing o
 f goods within a city.\n \nBio:\nDr. Hewitt is an Associate Professor in 
 the Information Systems and Supply Chain Management Department in the Quin
 lan School of Business at Loyola University Chicago\, where he also serves
  as the Director of graduate programs in Supply Chain Management. His rese
 arch includes developing quantitative models of decisions found in the tra
 nsportation and supply chain management domains\, particularly in freight 
 transportation and home delivery.  His work has assisted the decision-mak
 ing of companies such as Exxon Mobil\, Saia Motor Freight\, and Yellow Roa
 dway. He has expanded his area of expertise to include workforce planning\
 , including working on multi-disciplinary projects at the intersection of 
 operations management and cognitive psychology. His research has been fund
 ed by agencies such as the National Science Foundation\, the Material Hand
 ling Institute\, and the New York State Health Foundation. Before entering
  the PhD program at Georgia Tech\, Dr. Hewitt worked as a software enginee
 r\, contributing to the development of software to support consumer set-to
 p boxes and LED signs in mass transit stations.
LOCATION:GC B3 30 https://plan.epfl.ch/?room=GCB330
STATUS:CONFIRMED
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