Introduction
Modern Operations Research (MOR) is an international, peer-reviewed journal dedicated to advancing the theory, methodology, and application of operations research (OR) and analytics in addressing complex decision-making problems. In an era defined by data abundance, computational power, and interconnected systems, the journal serves as a pivotal forum for disseminating cutting-edge research that bridges the gap between abstract analytical models and tangible real-world impact. We recognize that the challenges of modern industry, government, and society—from optimizing sustainable supply chains and enhancing healthcare delivery to managing smart cities and mitigating financial risks—demand sophisticated, innovative OR solutions. MOR is committed to publishing high-quality, original work that not only refines existing techniques but also pioneers novel paradigms. Our scope encompasses the full spectrum of OR, including deterministic and stochastic optimization, simulation, decision analysis, game theory, and their convergence with data science, machine learning, and artificial intelligence. The journal fosters interdisciplinary dialogue, welcoming contributions that demonstrate rigorous methodology, computational excellence, and significant practical relevance or theoretical insight.
The primary aims of Modern Operations Research are to:
Publish original, high-impact research that extends the methodological frontiers of operations research, analytics, and management science.
Promote the development and application of novel algorithms, models, and computational techniques for solving complex, large-scale problems.
Encourage interdisciplinary research that integrates OR with fields such as computer science, economics, engineering, and behavioral sciences.
Provide a platform for studies demonstrating the effective implementation of OR methods in practice, highlighting challenges, solutions, and verifiable benefits.
Facilitate the translation of advanced theoretical and quantitative insights into actionable strategies for improved decision-making in business, public policy, and non-profit organizations.
Support open science and reproducibility by encouraging authors to share code, data, and models where feasible.
The journal's scope includes, but is not limited to, the following areas:
Optimization: Linear, nonlinear, integer, conic, and multi-objective programming; combinatorial optimization; global optimization; network optimization.
Stochastic Models: Queuing theory, stochastic processes, simulation modeling and analysis, stochastic dynamic programming, reliability theory, risk analysis.
Decision & Risk Analysis: Multi-criteria decision analysis, decision theory, Bayesian methods, risk management, behavioral operations.
Applied Analytics & Machine Learning: Data-driven optimization, predictive and prescriptive analytics, OR applications of machine learning, reinforcement learning for sequential decision-making.
Game Theory & Mechanism Design: Economic models, auctions, pricing, and incentive design in operational contexts.
Applications in: Supply chain management, logistics, healthcare operations, revenue management, energy systems, transportation, public policy, financial engineering, service systems, and sustainability.
The vision of Modern Operations Research is to be the premier scholarly destination for research that defines the future of decision science. We envision a journal that not only responds to emerging challenges but actively anticipates them by fostering the next generation of analytical tools. Our goal is to catalyze a community where groundbreaking theory and transformative practice converge, enabling the design of more efficient, resilient, and equitable systems worldwide. We strive to be recognized for intellectual leadership in shaping an era where data and models serve fundamental human and organizational needs, driving innovation and creating sustainable value for society.