ISyE Department Seminar - Andrea Lodi

Event Details
  • Date/Time:
    • Wednesday April 10, 2019
      1:30 pm - 2:30 pm
  • Location: ISyE Main Room 228
  • Phone:
  • URL: ISyE Building Complex
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
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Summaries

Summary Sentence: Dealing with uncertainty in tactical planning by machine learning

Full Summary: In this talk, we propose a methodology to predict descriptions of solutions to discrete stochastic optimization problems in very short computing time. We approximate the solutions based on supervised learning and the training dataset consists of a large number of deterministic problems that have been solved independently (and offline). Uncertainty regarding a subset of the inputs is addressed through sampling and aggregation methods. Our motivating application concerns booking decisions of intermodal containers on doublestack trains. Under perfect information, this is the so-called load planning problem and it can be formulated by means of integer linear programming. However, the formulation cannot be used for the application at hand because of the restricted computational budget and unknown container weights. The results show that standard deep learning algorithms allow to predict descriptions of solutions with high accuracy in very short time (milliseconds or less). A careful comparison with alternative stochastic programming approaches is provided.

Dealing with uncertainty in tactical planning by machine learning.

In this talk, we propose a methodology to predict descriptions of solutions to discrete stochastic optimization problems in very short computing time. We approximate the solutions based on supervised learning and the training dataset consists of a large number of deterministic problems that have been solved independently (and offline). Uncertainty regarding a subset of the inputs is addressed through sampling and aggregation methods. Our motivating application concerns booking decisions of intermodal containers on doublestack trains. Under perfect information, this is the so-called load planning problem and it can be formulated by means of integer linear programming. However, the formulation cannot be used for the application at hand because of the restricted computational budget and unknown container weights. The results show that standard deep learning algorithms allow to predict descriptions of solutions with high accuracy in very short time (milliseconds or less). A careful comparison with alternative stochastic programming approaches is provided.

Additional Information

In Campus Calendar
Yes
Groups

H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

Invited Audience
Faculty/Staff, Postdoc, Public, Graduate students, Undergraduate students
Categories
Seminar/Lecture/Colloquium
Keywords
No keywords were submitted.
Status
  • Created By: sbryantturner3
  • Workflow Status: Published
  • Created On: Apr 1, 2019 - 9:03am
  • Last Updated: Apr 1, 2019 - 9:03am