Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …
science. Until recently, its methods have focused on solving problem instances in isolation …
A comprehensive survey on the process, methods, evaluation, and challenges of feature selection
Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …
Decision diagrams for discrete optimization: A survey of recent advances
In the last decade, decision diagrams (DDs) have been the basis for a large array of novel
approaches for modeling and solving optimization problems. Many techniques now use DDs …
approaches for modeling and solving optimization problems. Many techniques now use DDs …
Improving variable orderings of approximate decision diagrams using reinforcement learning
Prescriptive analytics provides organizations with scalable solutions for large-scale,
automated decision making. At the core of prescriptive analytics methodology is …
automated decision making. At the core of prescriptive analytics methodology is …
Peel-and-bound: Generating stronger relaxed bounds with multivalued decision diagrams
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete
optimization. However, the field of decision diagrams is relatively new, and is still …
optimization. However, the field of decision diagrams is relatively new, and is still …
Improved Peel-and-Bound: Methods for generating dual bounds with multivalued decision diagrams
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete
optimization. However, the field of decision diagrams is relatively new, and is still …
optimization. However, the field of decision diagrams is relatively new, and is still …
Integrating machine learning and operations research methods for scheduling problems: a bibliometric analysis and literature review
A Ouhadi, Z Yahouni, M Di Mascolo - IFAC-PapersOnLine, 2024 - Elsevier
Operations research (OR) techniques have been widely used for optimizing problems, such
as manufacturing scheduling, supply chain optimization, and resource allocation. Despite its …
as manufacturing scheduling, supply chain optimization, and resource allocation. Despite its …
OPTIMIZING FULFILLMENT: A MULTI-FACETED APPROACH INTEGRATING LINEAR PROGRAMMING, BRANCH AND BOUND TECHNIQUES, AND …
P Koushik - INTERNATIONAL JOURNAL OF COMPUTER …, 2024 - iaeme-library.com
In the ever-changing supply chain management environment, the shift towards omnichannel
retail experiences has emerged as a crucial element impacting customer satisfaction and …
retail experiences has emerged as a crucial element impacting customer satisfaction and …
Using Clustering to Strengthen Decision Diagram Bounds for Discrete Optimization
M Nafar, M Römer - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
Offering a generic approach to obtaining both upper and lower bounds, decision diagrams
(DDs) are becoming an increasingly important tool for solving discrete optimization …
(DDs) are becoming an increasingly important tool for solving discrete optimization …
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 …
an optimization problem, enabling the generation of dual bounds to prove the optimality of …