Journal article
The Restaurant Meal Delivery Problem: Dynamic Pickup and Delivery with Deadlines and Random Ready Times
Transportation science, Vol.55(1), pp.75-100
01/01/2021
DOI: 10.1287/trsc.2020.1000
Abstract
We consider a stochastic dynamic pickup and delivery problem in which a fleet of drivers delivers food from a set of restaurants to ordering customers. The objective is to dynamically control a fleet of drivers in a way that avoids delays with respect to customers' deadlines. There are two sources of uncertainty in the problem. First, the customers are unknown until they place an order. Second, the time at which the food is ready at the restaurant is unknown. To address these challenges, we present an anticipatory customer assignment (ACA) policy. To account for the stochasticity in the problem, ACA postpones the assignment decisions for selected customers, allowing more flexibility in assignments. In addition, ACA introduces a time buffer to reduce making decisions that are likely to result in delays. We also consider bundling, which is the practice of assigning multiple orders at a time to a driver. Based on real-world data, we show howACAis able to improve service significantly for all stakeholders compared with current practice.
Details
- Title: Subtitle
- The Restaurant Meal Delivery Problem: Dynamic Pickup and Delivery with Deadlines and Random Ready Times
- Creators
- Marlin W Ulmer - Tech Univ Carolo Wilhelmina Braunschweig, Carl Friedrich Gauss Fak, D-38106 Braunschweig, GermanyBarrett W Thomas - University of Iowa, Bus Admin CollegeAnn Melissa Campbell - University of IowaNicholas Woyak - Univ Iowa, Tippie Coll Business, Iowa City, IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Transportation science, Vol.55(1), pp.75-100
- Publisher
- INFORMS
- DOI
- 10.1287/trsc.2020.1000
- ISSN
- 0041-1655
- eISSN
- 1526-5447
- Number of pages
- 26
- Language
- English
- Electronic publication date
- 08/27/2020
- Date published
- 01/01/2021
- Academic Unit
- Bus Admin College; Business Analytics
- Record Identifier
- 9984240028602771
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