Faster Deliveries and Smarter Order Assignments for an On-Demand Meal Delivery Platform

2019 ◽  
Author(s):  
Wenzheng Mao ◽  
Liu Ming ◽  
Ying Rong ◽  
Christopher S. Tang ◽  
Huan Zheng
Keyword(s):  
On Demand ◽  
Delivery Platform ◽  
Meal Delivery

Learning to Bundle Proactively for On-Demand Meal Delivery

2021 ◽  
Author(s):  
Chengbo Li ◽  
Lin Zhu ◽  
Guangyuan Fu ◽  
Longzhi Du ◽  
Canhua Zhao ◽  
...  
Keyword(s):  
On Demand ◽  
Meal Delivery

scholarly journals Being a Deliveroo Rider: Practices of Platform Labor in Nijmegen and Berlin

2021 ◽  
pp. 089124162199467
Author(s):  
Peter Timko ◽  
Rianne van Melik
Keyword(s):  
Business Models ◽  
Social Groups ◽  
Food Delivery ◽  
Uneven Distribution ◽  
Independent Contractors ◽  
Urban Economies ◽  
On Demand ◽  
Delivery Platform ◽  
Management Techniques

On-demand delivery platforms have become a common feature of urban economies across the globe. Noted for their hyper-outsourced, “lean” business models and reliance on independent contractors, these companies evade traditional employer obligations while still controlling workers through complex algorithmic management techniques. Using food delivery platform Deliveroo as a case-study, this paper investigates the diverse array of practices that on-demand workers carry out in order to enact this new platform labor arrangement in different spatial contexts. One of us conducted an auto-ethnographic project, working as a Deliveroo Rider in Nijmegen and Berlin for a period of nine months. Additionally, we interviewed 13 fellow platform workers. The findings reveal the motley, contingent, and conditional ways in which on-demand labor comes together on the ground. The paper concludes with discussing the uneven distribution of these practices across locations and social groups, and the sometimes contradictory impacts they have on the structure of platform labor.

scholarly journals Land Consumption of Delivery Robots and Bicycle Couriers for On-Demand Meal Delivery Using GPS Data and Simulations Based on the Time-Area Concept

2021 ◽  
Vol 13 (20) ◽  
pp. 11375
Author(s):  
Maren Schnieder ◽  
Chris Hinde ◽  
Andrew West
Keyword(s):  
Land Use ◽  
Autonomous Vehicles ◽  
Evaluation Method ◽  
Operating Strategy ◽  
On Demand ◽  
Delivery Services ◽  
Gps Traces ◽  
Land Consumption ◽  
Strategy Changes ◽  
Meal Delivery

Regulating the curbside usage of delivery vehicles and ride-hailing services as well as micromobility has been a challenge in the last years, a challenge which might worsen with the increase of autonomous vehicles. The contribution of the research outlined in this paper is an evaluation method of the land use of on-demand meal delivery services such as Deliveroo and UberEats. It evaluates the effect parking policies, operating strategy changes, and scheduling options have on the land consumption of bicycle couriers and sidewalk automated delivery robots (SADRs). Various operating strategies (i.e., shared fleets and fleets operated by restaurants), parking policies (i.e., parking at the restaurant, parking at the customer or no parking) and scheduling options (i.e., one meal per vehicle, multiple meals per vehicle) are simulated and applied to New York City (NYC). Additionally, the time-area requirements of on-demand meal delivery services are calculated based on GPS traces of Deliveroo and UberEats riders in two UK cities. The simulation in the paper shows that SADRs can reduce the time-area requirements by half compared with bicycle couriers. The effect of operating strategy changes and forbidding vehicles to park at the customer’s home is small. Delivering multiple meals in one tour halves the time-area requirements. The time-area requirements based on GPS traces is around 300 m2·min per order. The study allows policymakers to learn more about the land use of on-demand meal delivery services and how these can be influenced. Hence, they can adjust their policy strategies to ensure that on-demand meal delivery services are provided in a way that they use land effectively, reduce external costs, improve sustainability and benefit everyone.

scholarly journals Comparative Analysis of Drones and Riders in On-Demand Meal Delivery Based on Prospect Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Hong Jiang ◽  
Xinhui Ren
Keyword(s):  
Comparative Analysis ◽  
Prospect Theory ◽  
Consumer Attitudes ◽  
Delivery Time ◽  
Consumer Awareness ◽  
On Demand ◽  
Better Than ◽  
Meal Delivery

At present, the demand for on-demand meal delivery is increasing, and the main delivery pattern is rider delivery. However, rider delivery has certain problems in terms of timeliness and security. Due to its advantages of being fast, convenient, and safe, drone delivery can, to a certain extent, solve the problems of rider delivery. However, can drone delivery completely replace rider delivery? The paper mainly uses the prospect theory to discuss the conditions under which drone delivery is superior to rider delivery based on four factors: delivery distance, degree of rider delay, pickup time, and consumer attitudes towards drone delivery. Based on the research, it was found that when the delivery distance is more than 7 kilometres, the pickup time is within 2 minutes, or when consumers accept drone delivery, drone delivery is better than rider delivery. When the rider’s delay caused the delivery time to increase by more than 20%, the advantages of drone delivery began to stand out. Moreover, research has proven that drone delivery will help expand the scope of instant delivery, and the rational layout of drone airports and strengthening of consumer awareness and friendliness towards drone delivery will also help promote the development of drone delivery.

scholarly journals SmartLOC

2021 ◽  
Vol 5 (4) ◽  
pp. 1-24
Author(s):  
Yi Ding ◽  
Dongzhe Jiang ◽  
Yunhuai Liu ◽  
Desheng Zhang ◽  
Tian He
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Low Cost ◽  
Time Estimation ◽  
Travel Time Estimation ◽  
On Demand ◽  
High Penetration ◽  
Shopping Experience ◽  
Delivery Platform ◽  
Outdoor Localization

On-demand delivery is a rapidly developing business worldwide, where meals and groceries are delivered door to door from merchants to customers by the couriers. Couriers' real-time localization plays a key role in on-demand delivery for all parties like the platform's order dispatching, merchants' order preparing, couriers' navigation, and customers' shopping experience. Although GPS has well solved outdoor localization, indoor localization is still challenging due to the lack of large-coverage, low-cost anchors. Given the high penetration of smartphones in merchants and frequent rendezvous between merchants and couriers, we employ merchants' smartphones as indoor anchors for a new sensing opportunity. In this paper, we design, implement and evaluate SmartLOC, a map-free localization system that employs merchants' smartphones as anchors to obtain couriers' real-time locations. Specifically, we design a rendezvous detection module based on Bluetooth Low Energy (BLE), build indoor shop graphs for each mall, and adopt graph embedding to extract indoor shops' topology. To guarantee anchors' accuracy and privacy, we build a mutual localization module to iteratively infer merchants' state (in-shop or not) and couriers' locations with transformer models. We implement SmartLOC in a large on-demand delivery platform and deploy the system in 566 malls in Shanghai, China. We evaluate SmartLOC in two multi-floor malls in Shanghai and show that it can improve the accuracy of couriers' travel time estimation by 24%, 43%, 70%, and 76% compared with a straightforward graph solution, GPS, Wi-Fi, and TransLoc.

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