Logistics, Supply Chain and Financial Predictive Analytics: Theory and Practices (Asset Analytics)

Logistics, Supply Chain and Financial Predictive Analytics: Theory and Practices (Asset Analytics)

by KusumDeep (Editor), Madhu Jain (Editor), SaidSalhi (Editor)

Synopsis

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 methods for 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.


$206.00

Quantity

20+ in stock

More Information

Format: Hardcover
Pages: 264
Edition: 1st ed. 2019
Publisher: Springer
Published: 27 Oct 2018

ISBN 10: 9811308713
ISBN 13: 9789811308710

Author Bio

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.