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dc.contributor.authorGiannikas, Evangelosen
dc.date.accessioned2017-05-08T09:02:10Z
dc.date.available2017-05-08T09:02:10Z
dc.identifier.issn0263-5577
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/264127
dc.description.abstract$\textbf{Purpose }$– The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. This paper proposes an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation. $\textbf{Design/methodology/approach}$ – The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation. $\textbf{Findings}$– Computational experiments reveal that the proposed approach could reduce the total travel distance by 3% to 20%, and theoretically increase the success rate of first-round delivery approximately by18%-26%. $\textbf{Research limitations/implications}$ – The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics. $\textbf{Practical implications}$ – This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency. $\textbf{Social implications}$ – The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping. $\textbf{Originality/value}$ – Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper provides also a methodological approach to this line of research.
dc.languageengen
dc.language.isoenen
dc.publisherEmerald Group Publishing
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectcity logisticsen
dc.subjectfood deliveryen
dc.subjecte-commerceen
dc.subjectdata miningen
dc.subjectfreight transportationen
dc.titleUsing Customer-related Data to Enhance E-grocery Home Deliveryen
dc.typeArticle
prism.endingPage1933
prism.issueIdentifier9en
prism.publicationNameIndustrial Management and Data Systemsen
prism.startingPage1917
prism.volume117en
dc.identifier.doi10.17863/CAM.9490
dcterms.dateAccepted2017-02-22en
rioxxterms.versionofrecord10.1108/IMDS-10-2016-0432en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-02-22en
dc.contributor.orcidGiannikas, Evangelos [0000-0002-5762-5488]
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2017-10-16en
cam.orpheus.successThu Jan 30 12:54:04 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International