Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

by   Eric Larsen, et al.

This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical solutions to a given operational problem. In this context, the tactical solution is less detailed than the operational one but it has to be computed in very short time and under imperfect information. The problem is of importance in various applications where tactical and operational planning problems are interrelated and information about the operational problem is revealed over time. This is for instance the case in certain capacity planning and demand management systems. We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm. The training data set consists of a large number of deterministic (second stage) problems generated by controlled probabilistic sampling. The labels are computed based on solutions to the deterministic problems (solved independently and offline) employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application in load planning for rail transportation show that deep learning algorithms produce highly accurate predictions in very short computing time (milliseconds or less). The prediction accuracy is comparable to solutions computed by sample average approximation of the stochastic program.


page 1

page 2

page 3

page 4


Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning

The paper provides a methodological contribution at the intersection of ...

A language processing algorithm for predicting tactical solutions to an operational planning problem under uncertainty

This paper is devoted to the prediction of solutions to a stochastic dis...

Can Machine Learning Help in Solving Cargo Capacity Management Booking Control Problems?

Revenue management is important for carriers (e.g., airlines and railroa...

Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning

We propose a methodology at the nexus of operations research and machine...

Tablet-based Information System for Commercial Air-craft: Onboard Context-Sensitive Information System (OCSIS)

Pilots currently use paper-based documentation and electronic systems to...

Surgery Scheduling in Flexible Operating Rooms by using a Convex Surrogate Model of Second-Stage Costs

We study the elective surgery planning problem in a hospital with operat...

Exploring Deep Learning Approaches to Predict Person and Vehicle Trips: An Analysis of NHTS Data

Modern transportation planning relies heavily on accurate predictions of...

Please sign up or login with your details

Forgot password? Click here to reset