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Found 13 papers, 2 papers with code
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty.
Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study
Instead, we propose defining a core problem that restricts a rescheduling problem in response to a disturbance to only trains that need to be rescheduled, hence restricting the scope in both time and space.
Arc travel time and path choice model estimation subsumed
We propose a method for maximum likelihood estimation of path choice model parameters and arc travel time using data of different levels of granularity.
Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning
Our extensive empirical analysis is grounded in standardized families of problems derived from stochastic server location (SSLP) and stochastic multi knapsack (SMKP) problems available in the literature.
Minimizing Entropy to Discover Good Solutions to Recurrent Mixed Integer Programs
Current state-of-the-art solvers for mixed-integer programming (MIP) problems are designed to perform well on a wide range of problems.
Periodic Freight Demand Estimation for Large-scale Tactical Planning
In practice, demand is predicted by a time series forecasting model and the periodic demand is the average of those forecasts.
Reinforcement Learning for Freight Booking Control Problems
Routinely solving such operational problems when deploying reinforcement learning algorithms may be too time consuming.
Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?
This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keeping the system as before.
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
We propose a novel approach using supervised learning to obtain near-optimal primal solutions for two-stage stochastic integer programming (2SIP) problems with constraints in the first and second stages.
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 discrete optimization problem.
A tutorial on recursive models for analyzing and predicting path choice behavior
The problem at the heart of this tutorial consists in modeling the path choice behavior of network users.
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm.
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
We aim to predict at a high speed the expected TDOS associated with the second stage problem, conditionally on the first stage variables.
