Multitrip vehicle routing with delivery options: a data-driven application to the parcel industry

  • To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can beTo make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products.show moreshow less

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Metadaten
Author:Lukas Janinhoff, Robert KleinORCiDGND, Daniel Scholz
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/102800
ISSN:0171-6468OPAC
ISSN:1436-6304OPAC
Parent Title (English):OR Spectrum
Publisher:Springer Science and Business Media LLC
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/03/15
Tag:Management Science and Operations Research; Business, Management and Accounting (miscellaneous)
DOI:https://doi.org/10.1007/s00291-023-00710-6
Institutes:Wirtschaftswissenschaftliche Fakultät
Wirtschaftswissenschaftliche Fakultät / Institut für Statistik und mathematische Wirtschaftstheorie
Wirtschaftswissenschaftliche Fakultät / Institut für Statistik und mathematische Wirtschaftstheorie / Lehrstuhl für Analytics & Optimization
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Latest Publications (not yet published in print):Aktuelle Publikationen (noch nicht gedruckt erschienen)
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)