Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315347 
Year of Publication: 
2024
Citation: 
[Journal:] Networks and Spatial Economics [ISSN:] 1572-9427 [Volume:] 24 [Issue:] 4 [Publisher:] Springer US [Place:] New York [Year:] 2024 [Pages:] 997-1020
Publisher: 
Springer US, New York
Abstract: 
Abstract We investigate the Meal Delivery Routing Problem (MDRP), managing courier assignments between restaurants and customers. Our proposed variant considers uncertainties in meal preparation times and future order numbers with their locations, mirroring real challenges meal delivery providers face. Employing a rolling-horizon framework integrating Sample Average Approximation (SAA) and the Adaptive Large Neighborhood Search (ALNS) algorithm, we analyze modified Grubhub MDRP instances. Considering route planning uncertainties, our approach identifies routes at least 25% more profitable than deterministic methods reliant on expected values. Our study underscores the pivotal role of efficient meal preparation time management, impacting order rejections, customer satisfaction, and operational efficiency.
Subjects: 
Meal delivery routing
Uncertainty
Sample average approximation
Adaptive large neighborhood search
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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