142 research outputs found
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. The goal of the algorithm is
to predict a "representative scenario" (RS) for the problem such that,
deterministically solving the 2SIP with the random realization equal to the RS,
gives a near-optimal solution to the original 2SIP. Predicting an RS, instead
of directly predicting a solution ensures first-stage feasibility of the
solution. If the problem is known to have complete recourse, second-stage
feasibility is also guaranteed. For computational testing, we learn to find an
RS for a two-stage stochastic facility location problem with integer variables
and linear constraints in both stages and consistently provide near-optimal
solutions. Our computing times are very competitive with those of
general-purpose integer programming solvers to achieve a similar solution
quality
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Decision-focused learning (DFL) is an emerging paradigm in machine learning
which trains a model to optimize decisions, integrating prediction and
optimization in an end-to-end system. This paradigm holds the promise to
revolutionize decision-making in many real-world applications which operate
under uncertainty, where the estimation of unknown parameters within these
decision models often becomes a substantial roadblock. This paper presents a
comprehensive review of DFL. It provides an in-depth analysis of the various
techniques devised to integrate machine learning and optimization models,
introduces a taxonomy of DFL methods distinguished by their unique
characteristics, and conducts an extensive empirical evaluation of these
methods proposing suitable benchmark dataset and tasks for DFL. Finally, the
study provides valuable insights into current and potential future avenues in
DFL research.Comment: Experimental Survey and Benchmarkin
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
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
A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment
Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three‐hidden‐layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k‐nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large‐scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation
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