Mehmood, R and Graham, G orcid.org/0000-0002-9908-4974 (2015) Big data logistics: a health-care transport capacity sharing model. In: Procedia Computer Science. Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist 2015, 07-09 Oct 2015, Algarve Portugal. Elsevier , pp. 1107-1114.
Abstract
The growth of cities in the 21st century has put more pressure on resources and conditions of urban life. There are several reasons why the health-care industry is the focus of this investigation. For instance, in the UK various studies point to the lack of failure of basic quality control procedures and misalignment between customer needs and provider services and duplication of logistics practices. The development of smart cities and big data present unprecedented challenges and opportunities for operations managers; they need to develop new tools and techniques for network planning and control. Our paper aims to make a contribution to big data and city operations theory by exploring how big data can lead to improvements in transport capacity sharing. We explore using Markov models the integration of big data with future city (health-care) transport sharing. A mathematical model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. The results from our analysis of 13 different sharing/demand scenarios are presented. A key finding is that the probability for system failure and performance variance tends to be highest in a scenario of high demand/zero sharing.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015, The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Future city, Big data; transport operation management; healthcare information systems; integrated systems; shared resources |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Management Division (LUBS) (Leeds) > Logistics, Info, Ops and Networks (LION) (LUBS) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Jul 2015 10:23 |
Last Modified: | 01 Mar 2019 11:51 |
Published Version: | https://doi.org/10.1016/j.procs.2015.08.566 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.procs.2015.08.566 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:87497 |