Opsearch

Scope & Guideline

Transforming Ideas into Practical Applications

Introduction

Welcome to the Opsearch information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Opsearch, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN0030-3887
PublisherSPRINGER INDIA
Support Open AccessNo
CountryIndia
TypeJournal
Convergefrom 2009 to 2024
AbbreviationOPSEARCH / Opsearch
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address7TH FLOOR, VIJAYA BUILDING, 17, BARAKHAMBA ROAD, NEW DELHI 110 001, INDIA

Aims and Scopes

Opsearch is a journal dedicated to advancing the field of operations research and optimization. It emphasizes innovative methodologies and their applications to complex real-world problems across various sectors.
  1. Mathematical Modelling and Optimization Techniques:
    The journal extensively covers mathematical models and optimization techniques, focusing on both deterministic and stochastic problems across diverse applications.
  2. Supply Chain and Inventory Management:
    Research on supply chain dynamics, inventory control, and logistics management is a core area, highlighting strategies for efficiency and sustainability.
  3. Decision-Making Frameworks:
    Opsearch publishes studies on multi-criteria decision-making frameworks, integrating fuzzy logic, neural networks, and other advanced methodologies to enhance decision processes.
  4. Data Analysis and Machine Learning Applications:
    The journal explores the intersection of operations research with data science, particularly through the application of machine learning algorithms to optimize operational processes.
  5. Healthcare Operations and Management:
    There is a consistent focus on healthcare-related operations research, addressing issues such as resource allocation, patient flow optimization, and healthcare supply chain management.
  6. Environmental and Sustainability Considerations:
    Research addressing environmental impacts and sustainability challenges in operations management is increasingly prominent, reflecting a growing concern for ecological factors in decision-making.
Opsearch has begun to witness the emergence of several new themes and trends, reflecting the evolving landscape of operations research and its applications.
  1. Hybrid Optimization Algorithms:
    Recent publications show an increasing interest in hybrid algorithms that combine various optimization techniques, such as genetic algorithms with neural networks, to solve complex problems more effectively.
  2. Sustainability and Green Operations:
    There is a growing trend in research focusing on sustainability in operations, with studies exploring the environmental impact of supply chains and operations management practices.
  3. Data-Driven Decision Making:
    The integration of big data analytics and machine learning into operations research is rapidly emerging, with an emphasis on data-driven methodologies for improving decision-making processes.
  4. Healthcare Supply Chain Innovations:
    Research dedicated to optimizing healthcare supply chains, especially in response to recent global health challenges like the COVID-19 pandemic, is gaining traction.
  5. Robust and Stochastic Optimization:
    There is an increasing focus on robust and stochastic optimization techniques that address uncertainties in various fields, including finance, logistics, and supply chain management.

Declining or Waning

While Opsearch has consistently focused on diverse aspects of operations research, certain themes appear to be declining in prominence based on recent publications.
  1. Traditional Queueing Theory:
    Though still relevant, the frequency of publications centered on traditional queueing theory has decreased as the journal shifts towards more complex, integrated models that address real-world applications.
  2. Single-Objective Optimization Problems:
    There is a noticeable decline in studies focused solely on single-objective optimization, with a trend towards multi-objective and hybrid approaches that better reflect practical challenges.
  3. Basic Inventory Models:
    Research on basic inventory models, which were once a staple, is waning in favor of more sophisticated models that incorporate uncertainties and dynamic factors.
  4. Linear Programming Techniques:
    While still foundational, there has been a shift away from purely linear programming techniques towards more versatile and modern methodologies that incorporate various types of uncertainty and non-linearity.

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