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Crowdsourced logistics: The pickup and delivery problem with transshipments and occasional drivers
Author(s) -
Voigt Stefan,
Kuhn Heinrich
Publication year - 2022
Publication title -
networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.977
H-Index - 64
eISSN - 1097-0037
pISSN - 0028-3045
DOI - 10.1002/net.22045
Subject(s) - transshipment (information security) , pickup , computer science , flexibility (engineering) , operations research , integer programming , scheme (mathematics) , service (business) , service provider , compensation (psychology) , mathematical optimization , business , engineering , mathematics , algorithm , artificial intelligence , computer security , image (mathematics) , psychology , mathematical analysis , statistics , marketing , psychoanalysis
This article considers a setting in which a courier, express, and parcel service provider operates a fleet of vehicles with regular drivers (RDs) to ship parcels from pickup to delivery points. Additionally, the company uses a platform where occasional drivers (ODs) offer their willingness to take on requests that are on or near the route they had originally planned. There exist transshipment points (TPs) to better integrate these ODs. ODs or RDs may transfer load at these predetermined TPs. The problem is modeled as a mixed‐integer programming model and called pickup and delivery problem with transshipments and occasional drivers (PDPTOD). We develop a solution approach based on an adaptive large neighborhood search. The article provides insights on how the number and location of TPs impact the cost advantages achieved by integrating ODs. It also shows that the cost savings are highly sensitive to the assumed flexibility and compensation scheme.