Latest Articles for Operations Research

A new method blends deep learning and constraint programming to enhance job scheduling.

An overview of contraction perfect graphs and their significance in graph theory.

A novel approach to address variable costs in economic lot-sizing.

Exploring how machine learning improves MILP solver efficiency and effectiveness.

A look into average hereditary graphs and their significance in graph theory.

New method improves task scheduling efficiency on machines.

This study evaluates efficient neural network ensembles for classifying industrial parts under uncertainty.

Explore the structure and relevance of directed graphs in various fields.

Examining the complexities of packing unit squares into simple polygons.

Optimizing routes for pickup and delivery tasks using reinforcement learning.

Discover the new features of SCIP 9.0 for efficient optimization.

Learn about reflected Brownian motion and its applications in queueing theory.

New methods to improve job scheduling efficiency across identical machines.

Learn how direct-search methods help find optimal solutions effectively.

Learn the basics of constraint satisfaction problems and their applications.

A look into parking functions and their geometric representations.

Explore the nature, types, and real-world uses of constraint satisfaction problems.

Learn about flexible job shop scheduling and its impact on efficiency.

Exploring a novel method for optimizing semi-continuous variables in complex scenarios.

Learn about optimal transport and its applications in logistics and beyond.

Training open-source LLMs enhances optimization modeling for industry applications.

Leveraging reinforcement learning to optimize job scheduling using Gittins index techniques.

Discover how cycloids help model synchronization in processes across various fields.

A look into stable partitions and their role in matching individuals based on preferences.

A new model enhances decision-making in complex scenarios with interdependent choices.

Research introduces advanced methods for efficient network coverage solutions.

A look into policy iteration methods for effective decision-making and their verification.

A method to optimize dynamic vehicle routing with LIFO unloading and docking constraints.

A guide to managing resources in project scheduling while minimizing costs.

This research examines how variable arrival and service rates affect queues.

This study presents a novel method to optimize logistics task assignment using surrogate models.

Learn methods to minimize binary polynomials and their applications in various fields.

Learn how stochastic control methods help in uncertain decision-making.

A new policy enhances choices in uncertain decision-making environments.

A new method improves decision-making under uncertainty using robust Markov Decision Processes.

A look into integer programming and the Unbounded Subset Sum problem.

A fresh framework uses deep learning for integer programming solutions.

Optimizing job scheduling can enhance customer satisfaction and business performance.

New methods improve approximate solutions for the Traveling Salesperson Problem.

An overview of graph polynomials and their relevance in various fields.