Operations Research
Recent Advances and Applications
Will be published approx. on 29. July 2026
258 pages
E-Book
978-1-040-59762-0 (ISBN)
Description
This book covers cutting-edge research that includes operations research and decision sciences. It examines finite queueing systems with unreliable service provisioning and feedback strategies. Retrial queueing systems with customer feedback and individual abandonments are dynamically investigated. The book presents fuzzy logic approaches for multi-objective transportation problems. Multi-objective programming methodologies effectively deal with problems associated with catastrophe management. Healthcare decision-making under uncertain contexts deserves special attention. Inventory models combine trade credit financing with credit-linked demand functions. Financial risk analysis, business estimation, and bilevel portfolio optimization strategies are thoroughly investigated.
Features:
Delves into practical approaches to operational research concerns, offering frameworks that address real-world scenarios.
Introduces new techniques and strategies for problem-solving, involving the development of physical scenarios, effective algorithms, and straightforward implementation.
Discusses topics such as inventory control models, RMS optimization, fuzzy optimization with unique membership functions, and traffic simulation for telecommunication networks.
Covers mathematical perspectives, such as variable costs, perishable and non-perishable products for consumers, fuzzy decision variables, and constraint qualifications.
Presents an in-depth analysis of the latest developments shaping the field of operations research.
It is primarily written for graduate students and academic researchers in operations research, industrial engineering, systems engineering, production engineering, manufacturing engineering, mathematics, and quality control.
Features:
Delves into practical approaches to operational research concerns, offering frameworks that address real-world scenarios.
Introduces new techniques and strategies for problem-solving, involving the development of physical scenarios, effective algorithms, and straightforward implementation.
Discusses topics such as inventory control models, RMS optimization, fuzzy optimization with unique membership functions, and traffic simulation for telecommunication networks.
Covers mathematical perspectives, such as variable costs, perishable and non-perishable products for consumers, fuzzy decision variables, and constraint qualifications.
Presents an in-depth analysis of the latest developments shaping the field of operations research.
It is primarily written for graduate students and academic researchers in operations research, industrial engineering, systems engineering, production engineering, manufacturing engineering, mathematics, and quality control.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
61 Tables, black and white; 49 Line drawings, black and white; 4 Halftones, black and white; 53 Illustrations, black and white
ISBN-13
978-1-040-59762-0 (9781040597620)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Shreekant Varshney | Jyotiranjan Nayak | Mayank Gupta
Operations Research
Recent Advances and Applications
Book
approx. 07/2026
1st Edition
CRC Press
€148.50
Not yet published
Persons
Shreekant Varshney is an Assistant Professor in the Department of Mathematics, School of Technology (SoT), Pandit Deendayal Energy University, Gandhinagar, Gujarat, India. He is actively involved in research domains, namely, Queueing Theory, Machine Repair Problem (MRP), Optimal Control, Reliability and maintainability, Stochastic Modeling, EV Network Modeling, Cost Optimization, Sensitivity Analysis, Evolutionary Computation, Statistical Analysis, Fuzzy Set and logic.
Jyotiranjan Nayak is a Professor in the Department of Mathematics at the Faculty of Science and Technology (IcfaiTech), IFHE, Hyderabad, India. His research areas include Non-convex Programming, Fuzzy Optimization, Optimal Control, and Topology Optimization.
Mayank Gupta received his Ph.D. degree from IIT Delhi. Before joining PDEU, he worked as a Project Scientist at IIT Delhi on a project of DRDO (Funding of around 7 Crores). The main area of his research is Optics and Solar Energy.
Chandra Shekhar, Professor, BITS Pilani, a distinguished academic and former Head of the Department of Mathematics at BITS Pilani, India, is actively involved in research and teaching at both undergraduate and postgraduate levels. His expertise spans various mathematical fields, including Queueing Theory, Inventory Theory, Reliability Theory, Computer and Communication Systems, Machine Repair Problems, Stochastic Processes, Evolutionary Computation, Statistical Analysis, and Fuzzy Set & Logic.
Balakrushna Tripathy is working as a professor in the higher academic grade at the CORE school of VIT, Vellore. He has the distinction of receiving a national scholarship at the PG level, a UGC (Govt. of India) fellowship for pursuing his research, and a DST (Govt. of India) fellowship for pursuing M.Tech. (Computer Science) in Pune University, and the SERC fellowship (DOE, Govt. India) for joining IIT Kharagpur as a visiting fellow.
Jyotiranjan Nayak is a Professor in the Department of Mathematics at the Faculty of Science and Technology (IcfaiTech), IFHE, Hyderabad, India. His research areas include Non-convex Programming, Fuzzy Optimization, Optimal Control, and Topology Optimization.
Mayank Gupta received his Ph.D. degree from IIT Delhi. Before joining PDEU, he worked as a Project Scientist at IIT Delhi on a project of DRDO (Funding of around 7 Crores). The main area of his research is Optics and Solar Energy.
Chandra Shekhar, Professor, BITS Pilani, a distinguished academic and former Head of the Department of Mathematics at BITS Pilani, India, is actively involved in research and teaching at both undergraduate and postgraduate levels. His expertise spans various mathematical fields, including Queueing Theory, Inventory Theory, Reliability Theory, Computer and Communication Systems, Machine Repair Problems, Stochastic Processes, Evolutionary Computation, Statistical Analysis, and Fuzzy Set & Logic.
Balakrushna Tripathy is working as a professor in the higher academic grade at the CORE school of VIT, Vellore. He has the distinction of receiving a national scholarship at the PG level, a UGC (Govt. of India) fellowship for pursuing his research, and a DST (Govt. of India) fellowship for pursuing M.Tech. (Computer Science) in Pune University, and the SERC fellowship (DOE, Govt. India) for joining IIT Kharagpur as a visiting fellow.
Content
1. Sensitivity Investigation of Finite Queueing Systems with Unreliable Service Provisioning and Feedback Mechanism. 2. Parametric Investigation of Retrial Queueing Systems with Customer Feedback Mechanism and Independent Abandonments. 3. Multi-Choice Multi-Objective Fuzzy Stochastic Transportation Problem. 4. A Multi-Objective Programming Approach for Disaster Management. 5. Inventory Model for Trade Credit Financing Using Credit-Linked Demand. 6. Analysis of Investment Pattern of DIIs and its Impact on NIFTY Returns during Pre and Post Recession Period. 7. A New Approach for Finding a Solution to the Fuzzy Transportation Problem Under Fuzzy Environments. 8. Analysing the Selection Parameters to Establish a Choice of Retail Store (Hypermarket, Convenience Store, or Supermarket) in a Target Location: A Fuzzy-AHP Approach. 9. Formulation and Optimal Solution of a Fuzzy Decision-Making Linear Model with Application in Healthcare. 10. Multi-Objective Fuzzy Programming Problems with Triangular Fuzzy Parameters Using the Signed Distance Method. 11. Financial Risk and Corporate Valuation: Evidence from BSE 500 Listed Entities. 12. Bilevel Programming Application in Cardinality-Constrained Portfolio Model. 13. On Some Perspectives of Gradient-Based Optimization Algorithms in Machine Learning
