Operational Research

Scope & Guideline

Empowering Research that Drives Operational Success.

Introduction

Welcome to the Operational Research information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Operational Research, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1109-2858
PublisherSPRINGER HEIDELBERG
Support Open AccessNo
CountryGermany
TypeJournal
Convergefrom 2009 to 2024
AbbreviationOPER RES-GER / Oper. Res.
Frequency3 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY

Aims and Scopes

The journal 'Operational Research' is dedicated to advancing the field of operations research through the publication of high-quality research that applies mathematical models and analytical methods to solve complex decision-making problems in various domains. The journal covers a wide array of topics that are both theoretical and applied in nature, emphasizing the development of innovative methodologies and frameworks that enhance operational efficiency and effectiveness.
  1. Multi-Criteria Decision-Making (MCDM):
    The journal frequently publishes research on MCDM techniques, which help decision-makers evaluate and prioritize different alternatives based on various criteria, reflecting the complexity of real-world scenarios.
  2. Supply Chain Management:
    A significant focus area includes optimization and modeling within supply chains, addressing challenges such as logistics, inventory management, and sustainability, often incorporating advanced analytical methods.
  3. Optimization Techniques:
    The journal emphasizes the development and application of optimization algorithms, including metaheuristics, to solve complex operational problems in various industries.
  4. Data Envelopment Analysis (DEA):
    Research on DEA continues to be a core area, providing methodologies for evaluating the efficiency of decision-making units and benchmarking performance across different sectors.
  5. Stochastic Modeling:
    The journal includes studies that utilize stochastic processes to model uncertainty in operational systems, enhancing the robustness of decision-making under variable conditions.
  6. Game Theory Applications:
    Game theoretical approaches are commonly explored to analyze competitive and cooperative scenarios within operational contexts, providing insights into strategic decision-making.
The journal 'Operational Research' has also seen the emergence of new themes and trends in recent years, reflecting the evolving landscape of operations research and the need for innovative solutions to contemporary challenges.
  1. Sustainable Operations and Green Supply Chains:
    The integration of sustainability into operational strategies is gaining significance, with an increasing number of studies focusing on environmentally-friendly practices and their impact on supply chain performance.
  2. Artificial Intelligence and Machine Learning Applications:
    There is a notable rise in research applying AI and machine learning techniques to optimize operational processes, enhance predictive capabilities, and improve decision-making efficiency.
  3. Blockchain Technology in Supply Chains:
    Emerging research on the application of blockchain technology in supply chain management highlights its potential for enhancing transparency, traceability, and security in operations.
  4. Resilience and Risk Management:
    The focus on developing resilient operational frameworks that can withstand disruptions, particularly in light of recent global challenges, is becoming increasingly prominent.
  5. Big Data Analytics:
    The utilization of big data analytics for operational decision-making is trending, as organizations seek to leverage large datasets for improved insights and strategic planning.

Declining or Waning

While the journal maintains a robust focus on various themes, some areas have shown a declining trend in recent publications. These waning scopes may reflect shifts in research priorities or the saturation of topics within the field.
  1. Traditional Linear Programming:
    Although foundational, traditional linear programming methods have seen a decrease in focus as more complex and adaptive optimization techniques gain traction in addressing real-world problems.
  2. Basic Queueing Theory:
    Research centered solely on basic queueing theory has diminished, possibly due to the increasing complexity of operational systems that require more integrated and multifaceted approaches.
  3. Static Inventory Models:
    Static models that do not account for dynamic factors or uncertainties are becoming less prevalent, as researchers seek more adaptable frameworks that reflect real-time operational challenges.
  4. Simple Decision Trees:
    The use of basic decision trees for operational analysis has waned, with a shift towards more sophisticated models that incorporate uncertainty and multiple criteria.
  5. Single-Objective Optimization:
    Research focusing exclusively on single-objective optimization problems is declining as multi-objective and robust optimization approaches offer more comprehensive solutions to complex operational challenges.

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