International Journal of Systems Science-Operations & Logistics

Scope & Guideline

Pioneering Research in Operations Management

Introduction

Immerse yourself in the scholarly insights of International Journal of Systems Science-Operations & Logistics with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN2330-2674
PublisherTAYLOR & FRANCIS LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 2014 to 2024
AbbreviationINT J SYST SCI-OPER / Int. J. Syst. Sci.- Oper. Logist.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND

Aims and Scopes

The International Journal of Systems Science-Operations & Logistics focuses on the integration of systems science principles within operations and logistics management. The journal aims to publish high-quality research that contributes to the development of methodologies and frameworks for optimizing complex systems in logistics and operations. It emphasizes innovative approaches that address real-world challenges in supply chains, inventory management, and sustainable practices.
  1. Supply Chain Optimization:
    Research that develops models and algorithms to enhance the efficiency and effectiveness of supply chains, including aspects such as inventory management, logistics, and resource allocation.
  2. Sustainable Operations:
    Studies focusing on integrating sustainability into operations and logistics, addressing environmental impacts, and promoting green practices within supply chains.
  3. Decision-Making Models:
    Development of various decision-making frameworks, including multi-objective optimization and game-theoretic approaches, to aid in operational planning and strategy formulation.
  4. Risk Management in Operations:
    Research on identifying, assessing, and mitigating risks within supply chains, particularly in the context of disruptions and uncertainties.
  5. Advanced Computational Methods:
    Utilization of metaheuristic algorithms, machine learning, and simulation techniques to solve complex operational problems and enhance decision-making processes.
The journal has increasingly embraced several emerging themes that are reflective of current challenges and advancements in the field of operations and logistics. These trends highlight the journal's commitment to addressing contemporary issues and leveraging new methodologies.
  1. Resilience in Supply Chains:
    Recent publications emphasize the importance of building resilient supply chains capable of withstanding disruptions, showcasing methods for risk analysis and mitigation.
  2. Green and Sustainable Practices:
    There is a growing trend towards research that integrates sustainability into logistics and operations, focusing on reducing carbon footprints and promoting eco-friendly practices.
  3. Data-Driven Decision Making:
    The use of big data analytics and machine learning techniques in operations management is on the rise, with studies demonstrating how data can enhance decision-making and operational efficiency.
  4. Multi-Objective Optimization:
    An increasing number of studies are adopting multi-objective optimization frameworks to tackle complex operational problems, reflecting a shift towards more holistic decision-making approaches.
  5. Digital Transformation in Logistics:
    Research is increasingly focusing on the impact of digital technologies, such as IoT and blockchain, on logistics and supply chain management, exploring how these innovations can improve efficiency and transparency.

Declining or Waning

While certain themes have remained strong in the journal, others have shown a decline in focus over recent years. This may reflect shifts in industry needs, technological advancements, or changing research priorities within the field of operations and logistics.
  1. Traditional Inventory Models:
    Research focusing on conventional inventory management techniques is becoming less prominent as more advanced, technology-driven approaches gain traction.
  2. Basic Supply Chain Models:
    Studies centered on simplistic or conventional supply chain models are declining, as there is a growing preference for complex, realistic models that account for multiple variables and uncertainties.
  3. Static Decision-Making Frameworks:
    There is a noticeable reduction in research that employs static models for decision-making, as dynamic and adaptive frameworks are increasingly favored in response to real-time changes in supply chain environments.
  4. Single-Criteria Optimization:
    The focus on single-criteria optimization models is waning, giving way to multi-objective and integrated approaches that better reflect the complexities of modern supply chains.

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