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Logistics, Supply Chain and Financial Predictive Analytics: Theory and Practice

Deep, Kusum and Jain, Madhu and Salhi, Said, eds. (2019) Logistics, Supply Chain and Financial Predictive Analytics: Theory and Practice. Asset Analytics . Springer, Singapore, Singapore, 254 pp. ISBN 978-981-1308-71-0. E-ISBN 978-981-1308-72-7. (doi:10.1007/978-981-13-0872-7) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:68430)

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Abstract

This book addresses a broad range of problems commonly encountered in the fields of

in the banking sector. Divided into twenty chapters, some of the contemporary topics discussed

with trade-credit financing; a non-dominated sorting water cycle algorithm for the cardinality

transportation problems by means of the “supply demand reparation method” and “continuous

smoothing and the Arima model for fuel prices; optimal policy for Weibull distributed

with shortages and deterioration for three different demand rates; outlier labeling methods for

the design of a “least cost ration formulation application for cattle”; a preservation technology

series model for stock price forecasting in India; and asset pricing using capital market curves.

areas, giving them a feel for the latest developments and encouraging them to pursue further

Lifelong 40%

Item Type: Edited book
DOI/Identification number: 10.1007/978-981-13-0872-7
Uncontrolled keywords: analytics, logistics, supply chain, finance
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Central Services > Research and Innovation Services
Divisions > Kent Business School - Division > Centre for Logistics and Heuristic Optimisation (do not use)
Depositing User: Said Salhi
Date Deposited: 25 Jul 2018 15:58 UTC
Last Modified: 16 Feb 2021 13:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/68430 (The current URI for this page, for reference purposes)
Salhi, Said: https://orcid.org/0000-0002-3384-5240
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