Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/315347 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Networks and Spatial Economics [ISSN:] 1572-9427 [Volume:] 24 [Issue:] 4 [Publisher:] Springer US [Place:] New York, NY [Year:] 2024 [Pages:] 997-1020
Verlag: 
Springer US, New York, NY
Zusammenfassung: 
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.
Schlagwörter: 
Meal delivery routing
Uncertainty
Sample average approximation
Adaptive large neighborhood search
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