Central European Journal of Operations Research

Scope & Guideline

Championing Excellence in Decision Sciences Research

Introduction

Delve into the academic richness of Central European Journal of Operations Research with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN1435-246x
PublisherSPRINGER
Support Open AccessNo
CountryGermany
TypeJournal
Convergefrom 2006 to 2024
AbbreviationCENT EUR J OPER RES / Cent. Europ. J. Oper. Res.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES

Aims and Scopes

The Central European Journal of Operations Research focuses on the application of operations research methodologies to address complex problems in various fields. It aims to bridge theory and practice, providing insights that can inform decision-making and policy formulation.
  1. Operations Research Methodologies:
    The journal publishes research employing a variety of operations research techniques, including mathematical programming, simulation, and statistical analysis, to solve operational problems.
  2. Application across Industries:
    Research spans multiple domains such as supply chain management, logistics, healthcare optimization, and environmental sustainability, showcasing the versatility of operations research.
  3. Interdisciplinary Approaches:
    The journal encourages interdisciplinary research that integrates operations research with fields such as economics, engineering, and social sciences, promoting innovative solutions to complex issues.
  4. Focus on Sustainability and Resilience:
    Recent publications emphasize sustainable practices and resilience in operations, reflecting a growing concern for environmental and social impacts in operational decision-making.
  5. Case Studies and Empirical Research:
    The journal often features empirical studies and case analyses that provide practical insights and validate methodologies in real-world settings, enhancing the applicability of research findings.
The journal has identified several emerging themes that reflect current trends in operations research. These themes indicate areas of growing interest and importance within the field.
  1. Sustainability and Green Operations:
    A significant increase in research addressing sustainable practices in operations highlights the sector's responsiveness to environmental concerns and the need for eco-friendly solutions.
  2. Machine Learning and Data Analytics:
    The integration of machine learning and advanced data analytics in operations research is on the rise, reflecting the increasing importance of data-driven decision-making in complex operational environments.
  3. Healthcare Operations Research:
    There has been a marked increase in studies focused on healthcare operations, particularly in optimizing resource allocation and improving service delivery in medical settings.
  4. Supply Chain Resilience and Risk Management:
    Emerging themes in supply chain resilience and risk management are gaining attention, particularly in the context of global disruptions, highlighting the need for robust operational strategies.
  5. Agent-Based Modeling and Simulation:
    The use of agent-based modeling to simulate complex interactions within systems is trending, allowing for a nuanced understanding of dynamic processes in various operational contexts.

Declining or Waning

While the journal maintains a broad focus, certain themes have seen a decline in prominence over recent years. This section highlights these waning areas, indicating a potential shift in research priorities.
  1. Traditional Linear Programming:
    There has been a noticeable decrease in publications focusing solely on traditional linear programming techniques, as more complex and adaptive methodologies gain traction.
  2. Deterministic Models in Dynamic Contexts:
    Research employing strictly deterministic models for dynamic problems is becoming less common, with a shift towards stochastic and adaptive models that better reflect real-world uncertainties.
  3. Basic Algorithmic Approaches:
    Simplistic algorithmic approaches are less frequently addressed, with increasing complexity in problem-solving methods being favored, such as hybrid algorithms and machine learning techniques.
  4. Static Supply Chain Models:
    The focus on static models for supply chains is diminishing, as researchers pivot towards dynamic and robust models that account for variability and disruptions in supply chain operations.
  5. Single-Criteria Decision Making:
    There is a waning interest in single-criteria optimization models, as multi-criteria decision-making frameworks gain popularity due to their ability to address more complex, real-world problems.

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