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SUMMARY:Seminar by Prof. Wolfram Wiesemann\, Imperial College London
DTSTART:20181214T160000
DTEND:20181214T173000
DTSTAMP:20260406T050644Z
UID:ffc490bd28110e39548798094fe89fb4e02ab09e68da497026540cf7
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Wolfram Wiesemann\, Imperial College London\n"Distributi
 onally Robust Chance Constrained Vehicle Routing"\n\nAbstract:\nWe study a
  variant of the capacitated vehicle routing problem (CVRP)\, which asks fo
 r the cost-optimal delivery of a single product to geographically disperse
 d customers through a fleet of capacity-constrained vehicles. Contrary to
  the classical CVRP\, which assumes that the customer demands are determi
 nistic\, we model the demands as a random vector whose distribution is onl
 y known to belong to an ambiguity set. Moreover\, we require the delivery
  schedule to be feasible with a probability of at least 1−ε\, where ε
  characterizes the risk tolerance of the decision maker. We argue that the
  emerging distributionally robust CVRP can be solved efficiently with mode
 rn branch-and-cut algorithms if and only if the ambiguity set satisfies a
  subadditivity condition. We then show that this subadditivity condition h
 olds for a large class of moment ambiguity sets. We derive efficient cut g
 eneration schemes for ambiguity sets that specify the support as well as 
 (bounds on) the first and second moments of the customer demands. Our nume
 rical results indicate that the distributionally robust CVRP has favorable
  scaling properties and can often be solved in runtimes comparable to tho
 se of the deterministic CVRP.\n 
LOCATION:ODY 4 03 https://plan.epfl.ch/?room==ODY%204%2003
STATUS:CONFIRMED
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