Overview
- Presents the latest research developments in and analytics for logistics, inventory control, and supply chain management
- Addresses the forecasting of financial stock price and marketing strategies
- Covers budgetary allocation based on cost optimization
Part of the book series: Asset Analytics (ASAN)
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Table of contents (19 chapters)
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Front Matter
Keywords
About this book
This book addresses a broad range of problems commonly encountered in the fields of financial analysis, logistics and supply chain management, such as the use of big data analytics in the banking sector. Divided into twenty chapters, some of the contemporary topics discussed in the book are co-operative/non-cooperative supply chain models for imperfect quality items with trade-credit financing; a non-dominated sorting water cycle algorithm for the cardinality constrained portfolio problem; and determining initial, basic and feasible solutions for transportation problems by means of the “supply demand reparation method” and “continuous allocation method.” In addition, the book delves into a comparison study on exponential smoothing and the Arima model for fuel prices; optimal policy for Weibull distributed deteriorating items varying with ramp type demand rate and shortages; an inventory model with shortages and deterioration for three different demand rates; outlier labeling methodsfor medical data; a garbage disposal plant as a validated model of a fault-tolerant system; and the design of a “least cost ration formulation application for cattle”; a preservation technology model for deteriorating items with advertisement dependent demand and trade credit; a time series model for stock price forecasting in India; and asset pricing using capital market curves.
The book offers a valuable asset for all researchers and industry practitioners working in these areas, giving them a feel for the latest developments and encouraging them to pursue further research in this direction.
Editors and Affiliations
About the editors
Dr. Kusum Deep is a Professor at the Department of Mathematics, Indian Institute of Technology Roorkee. Her research interests include numerical optimization, nature inspired optimization, computational intelligence, genetic algorithms, parallel genetic algorithms, and parallel particle swarm optimization.
Dr. Madhu Jain is an Associate Professor at the Department of Mathematics, Indian Institute of Technology Roorkee. Her research interests include computer communications networks, performance prediction of wireless systems, mathematical modeling, and biomathematics.
Dr. Said Salhi is Director of the Centre for Logistics & Heuristic Optimization (CLHO) at Kent Business School, University of Kent, UK. Prior to his appointment at Kent in 2005, Said served at the University of Birmingham’s School of Mathematics for 15 years, where in the latter years he acted as Head of the Management Mathematics Group. He obtained his BSc in Mathematics at Algiers’s University, and his MSc and PhD in OR at Southampton (Institute of Mathematics) and Lancaster (School of Management), respectively. Dr. Said has edited 6 special journal issues, chaired the European Working Group in Location Analysis in 1996 and recently the International Symposium on Combinatorial Optimisation (CO2016) in Kent, 1–3 September 2016. He has published over 100 papers in academic journals.
Bibliographic Information
Book Title: Logistics, Supply Chain and Financial Predictive Analytics
Book Subtitle: Theory and Practices
Editors: Kusum Deep, Madhu Jain, Said Salhi
Series Title: Asset Analytics
DOI: https://doi.org/10.1007/978-981-13-0872-7
Publisher: Springer Singapore
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2019
Hardcover ISBN: 978-981-13-0871-0Published: 16 August 2018
Softcover ISBN: 978-981-13-4522-7Published: 29 December 2018
eBook ISBN: 978-981-13-0872-7Published: 06 August 2018
Series ISSN: 2522-5162
Series E-ISSN: 2522-5170
Edition Number: 1
Number of Pages: VII, 254
Number of Illustrations: 16 b/w illustrations, 38 illustrations in colour
Topics: Big Data/Analytics, Supply Chain Management, Logistics, Financial Mathematics