Editors:
- Explores various effective methods for inventory control and management using different optimization techniques
- Highlights ways of promoting business process improvement through inventory control techniques and provides managerial implications with all models
- Offers a comprehensive reference source for practitioners, teachers, students, and researchers in the fields of logistics, supply chain management, operations management, and retail management
Part of the book series: Asset Analytics (ASAN)
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Table of contents (25 chapters)
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Front Matter
About this book
This book discusses inventory models for determining optimal ordering policies using various optimization techniques, genetic algorithms, and data mining concepts. It also provides sensitivity analyses for the models’ robustness. It presents a collection of mathematical models that deal with real industry scenarios. All mathematical model solutions are provided with the help of various optimization techniques to determine optimal ordering policy.
The book offers a range of perspectives on the implementation of optimization techniques, inflation, trade credit financing, fuzzy systems, human error, learning in production, inspection, green supply chains, closed supply chains, reworks, game theory approaches, genetic algorithms, and data mining, as well as research on big data applications for inventory management and control. Starting from deterministic inventory models, the book moves towards advanced inventory models.
The content is divided into eight major sections: inventory control and management – inventory models with trade credit financing for imperfect quality items; environmental impact on ordering policies; impact of learning on the supply chain models; EOQ models considering warehousing; optimal ordering policies with data mining and PSO techniques; supply chain models in fuzzy environments; optimal production models for multi-items and multi-retailers; and a marketing model to understand buying behaviour. Given its scope, the book offers a valuable resource for practitioners, instructors, students and researchers alike. It also offers essential insights to help retailers/managers improve business functions and make more accurate and realistic decisions.
Editors and Affiliations
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Department of Mathematics, Gujarat University, Ahmedabad, India
Nita H. Shah
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Department of Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, India
Mandeep Mittal
About the editors
Mandeep Mittal is an Assistant Professor at the Department of Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, India. After completing his Master’s in Applied Mathematics at the Indian Institute of Technology (IIT) Roorkee, he obtained his Ph.D. from the University of Delhi, India. He subsequently completed his postdoctoral research at Hanyang University, South Korea. He has one book and more than 50 papers in international journals and conference proceedings to his credit. He received the Best Faculty Award from Amity School of Engineering and Technology, New Delhi, for the year 2016–2017. In addition, he is currently serving on the editorial boards of the journals Revista Investigacion Operacional, Journal of Control and Systems Engineering, and Journal of Advances in Management Sciences and Information Systems.
Bibliographic Information
Book Title: Optimization and Inventory Management
Editors: Nita H. Shah, Mandeep Mittal
Series Title: Asset Analytics
DOI: https://doi.org/10.1007/978-981-13-9698-4
Publisher: Springer Singapore
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-13-9697-7Published: 20 September 2019
Softcover ISBN: 978-981-13-9700-4Published: 20 September 2020
eBook ISBN: 978-981-13-9698-4Published: 31 August 2019
Series ISSN: 2522-5162
Series E-ISSN: 2522-5170
Edition Number: 1
Number of Pages: IX, 470
Number of Illustrations: 48 b/w illustrations, 89 illustrations in colour
Topics: Operations Research/Decision Theory, Operations Research, Management Science, Supply Chain Management, Logistics, Big Data/Analytics