Computers & Operations Research - Elsevier - Impact Factor

Computers & Operations Research - Elsevier | 2024 Impact Factor:4.3 | Cite Score:8.9 | Q1

Impact Factor and Journal Rank of Computers & Operations Research

About: Computers & Operations Research is a peer-reviewed journal published by Elsevier. It focuses on research at the intersection of computer science and operations research, particularly in the areas of optimization, decision-making, and computational methods. The journal provides a platform for scholars, researchers, and practitioners to publish original research articles, reviews, and case studies that advance the understanding and application of computational techniques in operations research.
Objective: The primary objective of Computers & Operations Research is to promote research and innovation in computational operations research. The journal aims to explore theories, methodologies, and applications that enhance the use of computational methods in solving complex optimization and decision-making problems. By publishing high-quality research, the journal contributes to the advancement of computational operations research and its practical applications in various domains.
Interdisciplinary Focus: Computers & Operations Research adopts an interdisciplinary approach, welcoming contributions from various fields related to both computer science and operations research, including but not limited to, Optimization Theory, Mathematical Programming, Combinatorial Optimization, Integer Programming, Metaheuristic Methods, Simulation, Stochastic Processes, Game Theory, Supply Chain Management, Logistics, Transportation Systems. This interdisciplinary perspective fosters collaboration and innovation, leading to the development of advanced computational techniques that address the complex challenges in operations research.
Global Reach and Impact: With a broad international readership and authorship, Computers & Operations Research has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in computational operations research worldwide. The journal content influences both academic research and practical applications, driving progress in areas such as production planning, scheduling, inventory management, network optimization, and decision support systems.
High Standards and Rigorous Review: Maintaining high academic standards, Computers & Operations Research conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
Significance: Computers & Operations Research plays a significant role in advancing research and practice in the field of computational operations research. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in computational methods for optimization and decision-making. It serves as an essential resource for researchers, practitioners, and policymakers seeking to leverage computational techniques to improve efficiency, enhance decision-making processes, and address complex operational challenges in various industries and organizations.

Journal Home: Journal Homepage

Editor-in-Chief: Francisco Saldanha da Gama

Scope: The Computers & Operations Research journal, published by Elsevier, is a prominent peer-reviewed publication that focuses on the application of computational methods and operations research techniques in solving complex decision-making problems. It serves as a crucial platform for researchers, practitioners, and academics to disseminate their latest findings, methodologies, and innovations in the field of computational optimization and operations research. Here is an overview of its key focus areas and scope:
1. Optimization Techniques:
Research on mathematical optimization methods, including linear programming, integer programming, nonlinear programming, and combinatorial optimization.
Studies on metaheuristic algorithms, evolutionary optimization, and swarm intelligence for solving large-scale optimization problems.
2. Decision Support Systems:
Exploration of decision support systems (DSS) and optimization software tools for assisting decision-makers in various domains.
Research on modeling frameworks, algorithmic approaches, and interactive interfaces for developing effective DSS in complex decision environments.
3. Operations Research Applications:
Advancements in operations research methodologies and their applications in diverse sectors, including supply chain management, logistics, transportation, finance, and healthcare.
Studies on mathematical modeling, simulation, and optimization techniques for improving operational efficiency, resource allocation, and strategic planning.
4. Stochastic Optimization and Uncertainty Quantification:
Research on stochastic optimization models, including stochastic programming, robust optimization, and chance-constrained programming.
Studies on uncertainty quantification techniques, risk analysis, and decision-making under uncertainty in operations research applications.
5. Combinatorial Optimization and Integer Programming:
Exploration of combinatorial optimization problems, including graph theory, network optimization, and scheduling problems.
Research on exact algorithms, approximation algorithms, and heuristics for solving combinatorial optimization and integer programming problems.
6. Metaheuristic Optimization Algorithms:
Advancements in metaheuristic optimization algorithms, including genetic algorithms, simulated annealing, particle swarm optimization, and ant colony optimization.
Studies on hybrid and adaptive metaheuristic approaches for addressing complex optimization problems in operations research.
7. Supply Chain Optimization and Logistics:
Research on supply chain optimization, inventory management, production planning, and distribution logistics.
Studies on optimization models, algorithms, and decision support systems for improving supply chain efficiency, responsiveness, and resilience.
8. Computational Methods in Finance:
Exploration of computational methods and optimization techniques in financial modeling, portfolio optimization, risk management, and algorithmic trading.
Research on quantitative finance models, asset pricing, and optimization strategies for investment decision-making and financial risk assessment.
9. Metaheuristic Optimization and Machine Learning:
Advancements in the integration of metaheuristic optimization algorithms with machine learning techniques, such as neural networks, support vector machines, and reinforcement learning.
Studies on optimization-based machine learning models for pattern recognition, data mining, and predictive analytics in operations research applications.
10. Multi-objective Optimization and Decision Making:
Research on multi-objective optimization models, Pareto optimization methods, and decision-making frameworks for handling conflicting objectives.
Studies on preference modeling, trade-off analysis, and interactive methods for solving multi-objective optimization problems in operations research.