Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations

Surendra Reddy Kancharla; Tom van Woensel; S. Travis Waller; Satish V. Ukkusuri

doi:10.1007/s11067-024-09643-1

Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations

Surendra Reddy Kancharla, Tom van Woensel, S. Travis Waller (Corresponding author), Satish V. Ukkusuri

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	997-1020
Number of pages	24
Journal	Networks and Spatial Economics
Volume	24
Issue number	4
Early online date	18 Sept 2024
DOIs	https://doi.org/10.1007/s11067-024-09643-1
Publication status	Published - Dec 2024

Keywords

Adaptive large neighborhood search
Meal delivery routing
Sample average approximation
Uncertainty

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Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations

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