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Found 4 papers, 1 papers with code
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution.
Machine Learning based Simulation Optimisation for Trailer Management
In many situations, simulation models are developed to handle complex real-world business optimisation problems.
Remaining Useful Lifetime Prediction via Deep Domain Adaptation
We propose a Domain Adversarial Neural Network (DANN) approach to learn domain-invariant features that can be used to predict the RUL in the target domain.
Data-driven Policy on Feasibility Determination for the Train Shunting Problem
The TUSP is an important problem as it is used to determine the capacity of shunting yards and arises as a sub-problem of more general scheduling and planning problems.
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