Operations Research and Decisions

Scope & Guideline

Connecting Theory with Practical Decision-Making

Introduction

Delve into the academic richness of Operations Research and Decisions 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
ISSN2081-8858
PublisherWROCLAW UNIV SCIENCE & TECHNOLOGY, FAC MANAGEMENT
Support Open AccessYes
CountryPoland
TypeJournal
Convergefrom 2019 to 2024
AbbreviationOPER RES DECIS / Oper. Res. Decis.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressWYBRZEZE WYSPIANSKIEGO 27, WROCLAW 50-370, POLAND

Aims and Scopes

The journal 'Operations Research and Decisions' focuses on the integration of operational research methodologies with decision-making processes across various domains. It serves as a platform for disseminating innovative solutions to complex problems through rigorous analysis and theoretical development.
  1. Multi-Criteria Decision Making (MCDM):
    The journal emphasizes methodologies for solving problems where multiple conflicting criteria need to be considered, such as the Analytic Hierarchy Process (AHP), VIKOR, and fuzzy logic approaches.
  2. Operations Research Applications:
    It covers practical applications of operations research techniques in diverse fields including logistics, supply chain management, healthcare, and finance, showcasing the relevance of mathematical models and optimization techniques.
  3. Sustainability and Environmental Considerations:
    Research addressing sustainable development, green logistics, and environmentally-friendly decision-making processes is a core focus, reflecting the increasing importance of sustainability in operational frameworks.
  4. Statistical and Econometric Modeling:
    The journal publishes studies that apply statistical methods and econometric models to analyze real-world data, particularly in finance, economics, and social sciences.
  5. Stochastic and Dynamic Programming:
    There is a significant interest in dynamic systems and stochastic models, particularly in relation to optimization problems under uncertainty.
The journal 'Operations Research and Decisions' has identified several emerging themes that reflect current trends in operational research and decision-making processes. These themes indicate a shift towards innovative methodologies and contemporary issues.
  1. Integration of Artificial Intelligence and Machine Learning:
    Recent publications highlight the growing use of AI and machine learning techniques in operational research, particularly for predictive analytics and optimization problems across various sectors.
  2. Sustainable Operations and Green Supply Chains:
    There is an increasing emphasis on sustainability, with a focus on optimizing supply chains and operations to minimize environmental impact and promote sustainable practices.
  3. Healthcare Operations Research:
    Research addressing healthcare systems, logistics, and decision-making processes has surged, particularly in the context of pandemic-related challenges and the need for efficient resource allocation.
  4. Data-Driven Decision Making:
    The trend towards leveraging big data analytics for informed decision-making is evident, with studies focusing on statistical modeling and real-time data analysis.
  5. Risk Analysis and Management:
    Emerging themes include the study of risk and uncertainty in operational settings, underscoring the importance of robust decision-making frameworks in volatile environments.

Declining or Waning

While the journal continues to evolve, certain themes have seen a decline in prominence in recent years. These waning areas may reflect shifts in research focus or the maturation of certain methodologies.
  1. Traditional Linear Programming Techniques:
    There has been a noticeable decrease in publications solely focused on classical linear programming methods, as researchers increasingly explore more complex and dynamic models.
  2. Basic Queueing Theory Models:
    The prevalence of simple queueing theory studies has diminished, with a shift towards more intricate models that incorporate elements of uncertainty and multi-dimensional analysis.
  3. Single-Domain Applications:
    Research that is narrowly focused on single-domain applications without interdisciplinary approaches appears to be less frequent, indicating a trend towards more holistic and integrated studies.
  4. Static Inventory Models:
    Static inventory management models are becoming less common, with a growing preference for dynamic models that account for fluctuations and uncertainties in demand.
  5. Descriptive Statistical Analysis:
    There is a decline in the publication of papers that primarily focus on descriptive statistics, as the journal shifts toward more advanced analytical techniques and predictive modeling.

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