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Found 11 papers, 6 papers with code
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning
Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems.
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Lagrangian relaxation is a versatile mathematical technique employed to relax constraints in an optimization problem, enabling the generation of dual bounds to prove the optimality of feasible solutions and the design of efficient propagators in constraint programming (such as the weighted circuit constraint).
Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver
Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time.
A prediction-based approach for online dynamic patient scheduling: a case study in radiotherapy treatment
When our prediction-based approach is compared to flat-reservation policies, it does a better job of preventing overdue treatments for emergency patients, while also maintaining comparable waiting times for the other patients.
SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning
This paper presents the proof of concept for SeaPearl, a new CP solver implemented in Julia, that supports machine learning routines in order to learn branching decisions using reinforcement learning.
Predicting the probability distribution of bus travel time to move towards reliable planning of public transport services
Two types of probabilistic models, namely similarity-based density estimation models and a smoothed Logistic Regression for probabilistic classification model, are compared on a dataset of more than 41, 000 trips and 50 bus routes of the city of Montr\'eal.
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes.
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems.
A new constraint programming model and a linear programming-based adaptive large neighborhood search for the vehicle routing problem with synchronization constraints
We consider a vehicle routing problem which seeks to minimize cost subject to time window and synchronization constraints.
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman Problem
In this paper, we address the limitations of ML approaches for solving the TSP and investigate two fundamental questions: (1) how can we measure the level of accuracy of the pure ML component of such methods; and (2) what is the impact of a search procedure plugged inside a ML model on the performances?
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems.
