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Title
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Efficient parcel delivery by predicting customers' locations
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Author
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Abstract
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| An important aspect of the growing e‐commerce sector involves the delivery of tangible goods to the end customer, the so‐called last mile. This final stage of the logistics chain remains highly inefficient due to the problem of failed deliveries. To address this problem, delivery service providers can apply data science to determine the optimal, customer‐centered location and time window for handover. In this article, we present a three‐step approach for location prediction, based on mobile location data, in order to support delivery planning. The first step is identifying a user's locations of interest through density‐based clustering. Next, the semantics (home or work) of the user's locations of interest are discovered, based on temporal assumptions. Finally, we predict future locations with a decision tree model that is trained on each user's historical location data. Though the problem of location prediction is not new, this work is the first to apply it to the field of parcel delivery with its corresponding implications. Moreover, we provide a novel and detailed evaluation on real‐world data from a parcel delivery service. The promising results indicate that our approach has the potential to help delivery service providers to gain insights into their customers’ optimal delivery time and location in order to support delivery planning. Eventually this will decrease last‐mile delivery costs and boost customer satisfaction. |
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Language
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English
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Source (journal)
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Decision sciences. - Atlanta, Ga
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Publication
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Atlanta, Ga
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2020
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ISSN
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0011-7315
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DOI
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10.1111/DECI.12376
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Volume/pages
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51
:5
(2020)
, p. 1202-1231
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ISI
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000578229200005
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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