INFOR

Scope & Guideline

Advancing Knowledge at the Intersection of Technology and Management

Introduction

Welcome to your portal for understanding INFOR, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageMulti-Language
ISSN0315-5986
PublisherTAYLOR & FRANCIS INC
Support Open AccessNo
CountryCanada
TypeJournal
Convergefrom 1971 to 1989, from 1996 to 2024
AbbreviationINFOR / Infor
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106

Aims and Scopes

The journal 'INFOR' focuses on advancing the field of operations research and its applications across various domains, emphasizing mathematical modeling, optimization techniques, and decision-making processes.
  1. Operations Research and Optimization:
    A central theme of 'INFOR' is the application of operations research methodologies to solve complex problems in various sectors, such as supply chain management, healthcare, and logistics. This includes both theoretical advancements and practical implementations.
  2. Data Envelopment Analysis (DEA):
    The journal consistently publishes papers on DEA, focusing on performance measurement and efficiency evaluation of decision-making units. This includes innovative methodologies and applications in diverse industries.
  3. Game Theory and Strategic Decision Making:
    Research in 'INFOR' explores game-theoretic models applied to strategic interactions in supply chains, marketing, and resource allocation, contributing to the understanding of competitive and cooperative behaviors.
  4. Machine Learning and AI Integration:
    The incorporation of machine learning techniques into optimization and decision-making processes is a growing focus area, showcasing how AI can enhance traditional operations research methodologies.
  5. Sustainability and Green Supply Chain Management:
    There is a consistent emphasis on sustainability in operations, particularly in supply chain contexts, reflecting the increasing importance of environmental considerations in decision-making.
Recent publications in 'INFOR' reveal emerging themes that highlight the journal's adaptive focus on contemporary issues and methodologies in operations research.
  1. Integration of AI and Machine Learning:
    There is a significant increase in research exploring the integration of AI and machine learning with operations research techniques, reflecting the industry's trend towards data-driven decision-making.
  2. Sustainable Operations and Green Logistics:
    Emerging themes in sustainability and green logistics are prominent, indicating a growing concern for environmental impacts and the need for eco-friendly practices in operations.
  3. Dynamic and Adaptive Supply Chain Models:
    Recent publications emphasize the importance of dynamic models that adapt to real-time changes in supply chains, showcasing a shift from traditional static approaches.
  4. Healthcare Operations Research:
    Research focusing on healthcare operations, including scheduling, resource allocation, and efficiency evaluation, is gaining momentum in response to the increasing complexity of healthcare systems.
  5. Multi-Agent Systems in Optimization:
    The exploration of multi-agent systems for solving optimization problems is emerging, highlighting a shift towards collaborative and decentralized approaches in operations research.

Declining or Waning

While 'INFOR' continues to evolve, certain themes have shown a decline in prominence, indicating a shift in research focus within the journal.
  1. Traditional Inventory Management Models:
    Research related to classical inventory management techniques has decreased, as newer methodologies, particularly those incorporating advanced analytics and machine learning, gain traction.
  2. Static Decision-Making Frameworks:
    Papers that focus on static or one-time decision-making frameworks are becoming less frequent, with a shift towards dynamic and adaptive models that account for real-time data and changing environments.
  3. Purely Theoretical Studies without Practical Applications:
    There is a noticeable reduction in purely theoretical studies that do not incorporate practical applications or empirical validation, as the journal aims to bridge theory with real-world applications.

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