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.
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.
We propose a method for maximum likelihood estimation of path choice model parameters and arc travel time using data of different levels of granularity.
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.
Current state-of-the-art solvers for mixed-integer programming (MIP) problems are designed to perform well on a wide range of problems.
In practice, demand is predicted by a time series forecasting model and the periodic demand is the average of those forecasts.
Routinely solving such operational problems when deploying reinforcement learning algorithms may be too time consuming.
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.
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.
This paper is devoted to the prediction of solutions to a stochastic discrete optimization problem.
We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm.
We aim to predict at a high speed the expected TDOS associated with the second stage problem, conditionally on the first stage variables.