Journal of Simulation

Scope & Guideline

Innovating Insights in Industrial and Manufacturing Engineering

Introduction

Welcome to your portal for understanding Journal of Simulation, 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.
LanguageEnglish
ISSN1747-7778
PublisherTAYLOR & FRANCIS LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 2008 to 2024
AbbreviationJ SIMUL / J. Simul.
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 Journal of Simulation focuses on advancing the field of simulation through a multidisciplinary approach, leveraging various methodologies to address complex systems across different domains. Its core areas include optimization, efficiency evaluation, and the integration of simulation with emerging technologies.
  1. Discrete Event Simulation (DES):
    The journal consistently publishes research on discrete event simulation, which is used to model and analyze systems where events occur at discrete points in time. This approach is applicable in various fields such as healthcare, logistics, and manufacturing.
  2. Agent-Based Modeling (ABM):
    A significant focus is on agent-based modeling, which simulates the actions and interactions of autonomous agents to assess their effects on the system as a whole. This methodology is prevalent in studies related to social dynamics, supply chain management, and resource allocation.
  3. Hybrid Simulation Techniques:
    The integration of different simulation methodologies, such as combining DES with system dynamics or optimization techniques, is a recurring theme. This hybrid approach enhances model accuracy and applicability across diverse scenarios.
  4. Optimization in Simulation:
    Many papers emphasize optimization techniques within simulation frameworks, aiming to improve system performance and decision-making processes. This includes the application of algorithms for resource management and operational efficiency.
  5. Digital Twins and Real-Time Simulation:
    The journal explores the concept of digital twins, which are virtual representations of physical systems, to enable real-time monitoring and optimization. This emerging area reflects a growing trend towards integrating simulation with IoT and data analytics.
  6. Sustainability and Resilience Modeling:
    Research also focuses on modeling sustainability and resilience strategies in various sectors, including healthcare, supply chains, and urban planning, highlighting the importance of adaptive systems in the face of challenges.
The Journal of Simulation has identified several emerging themes that reflect the evolving landscape of simulation research. These trends highlight the journal's commitment to addressing contemporary challenges through innovative methodologies.
  1. Integration of Machine Learning with Simulation:
    Recent publications show a significant increase in the integration of machine learning techniques with simulation models, enhancing predictive capabilities and decision-making processes in various applications.
  2. Real-Time and Predictive Simulations:
    There is a growing interest in developing real-time simulations and predictive models, particularly in healthcare and urban planning, to facilitate immediate decision-making and resource allocation.
  3. Sustainable and Resilient Systems Modeling:
    Emerging studies emphasize the modeling of sustainable and resilient systems, reflecting a global focus on sustainability and the need for adaptive strategies in supply chains, healthcare, and urban environments.
  4. Digital Twins and Smart Systems:
    The concept of digital twins is gaining traction, with research exploring their application in monitoring and optimizing complex systems in real-time, particularly in manufacturing and healthcare sectors.
  5. Complex Network Analysis in Simulation:
    An uptick in research using simulation to analyze complex networks, such as social networks or supply chains, is evident. This trend highlights the importance of understanding interactions within systems to improve resilience and efficiency.

Declining or Waning

While the Journal of Simulation is vibrant with current research themes, some areas appear to be losing prominence in recent publications. This decline may reflect shifting interests or advancements in methodologies.
  1. Traditional Statistical Methods:
    There has been a noticeable decrease in the publication of papers solely focusing on traditional statistical methods for simulation analysis, as more researchers adopt advanced computational techniques and machine learning approaches.
  2. Simple Scenario-Based Simulations:
    The journal has seen fewer studies that rely on basic scenario-based simulations without integration of more complex modeling techniques, indicating a shift towards more sophisticated and multi-dimensional simulation frameworks.
  3. Single-Domain Focus Studies:
    There is a decline in research that focuses solely on single-domain applications of simulation. The trend is leaning towards interdisciplinary studies that combine insights from multiple fields to address complex problems.
  4. Physical System Simulations:
    Research centered on physical system simulations, such as mechanical or electrical systems without integration with digital technologies, has decreased, possibly due to the growing emphasis on digital transformation and smart systems.
  5. Static Models without Real-Time Data Integration:
    There is a reduced frequency of publications focusing on static models that do not incorporate real-time data or adaptive algorithms, as the field moves towards dynamic and responsive simulation environments.

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