Supply and Demand Steering in On-demand Meal Delivery
A.Arslan a , S. Sharif Azadehb and M. Savelsberghc ,Y.Maknoon a∗a Faculty of Technology, Policy, and Management, Delft University of Technology

M.Y.Maknoon@tudelft.nl, A.M.Arslan@tudelft.nl
b Department of Transport & Planning, Delft University of Technology

S.SharifAzadeh@tudelft.nl
c H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute ofTechnology martin.savelsbergh@isye.gatech.edu

January 13, 2022
Keywords: on-demand meal delivery, sequential decisions, courier guidance, dynamic delivery
promises1INTRODUCTION

On-demand logistics has been evolving rapidly due to the ever-increasing consumer desire forfaster services. Online restaurant aggregators, or meal delivery platforms, are the pioneers of thisdevelopment. Meal delivery platforms have disrupted restaurant-operated delivery systems byproviding access to a number of restaurants in a region and offering high customer service, e.g.,fast delivery, user-friendliness, availability, etc. The skyrocketing growth of the sector reflects
the appeal of the business model (Singh, 2019).
Managing an on-demand meal delivery service involves multiple interrelated tasks, e.g., recruiting couriers (strategic) and coordinating courier activities (tactical and operational). Manyof the meal delivery platforms rely on ad-hoc crowdsourced delivery capacity, i.e., rely on individuals willing to make deliveries in their free time (Arslan et al., 2019, Ulmer & Savelsbergh,2020, Dayarian & Savelsbergh, 2020). Some platforms manage temporal and spatial imbalances
between delivery capacity (supply) and order placements (demand) by adjusting compensation
to attract or deter couriers (e.g., UberEats and Deliveroo). However, because customers expect
to pay only a small fee for delivery, adjustments in courier compensation cannot easily be passedon the customers. Furthermore, regulatory changes related to crowdsourced delivery capacity,
e.g., minimum payment guarantees, provision of benefits, have led platforms to switch to the useof advance scheduled couriers (e.g., JustEat and Takeaway.com). We focus on the latter model,
in which the couriers available to make deliveries during an operating period are scheduled inadvanced.
A major challenge for platforms is to effectively handle the spatial and temporal demandvariations that naturally occur from day to day and from hour to hour. Assuming that frequently
making dispatch decisions (i.e., repeatedly solving courier-order assignments) will achieve desired
on-time performance and courier utilization is unrealistic; a “wild goose chase” phenomenon iscommonly observed (Castillo et al., 2017). When a platform cannot dynamically adjust delivery
capacity and cannot dynamically adjust delivery fees (and influence demand), other tools arenecessary to effectively manage the highly stochastic meal delivery demand. For that purpose,
we introduce demand steering, i.e., adjusting delivery time promises, and supply steering, i.e.,
adjusting courier-order assignments, to increase customers’ service quality, measured by delivery
time promise delays and delivery time promise.
TRISTAN XI Symposium

Original abstract submittal

Arslan, Sharif Azadeh , Savelsbergh, Maknoon

2

Supply and demand steering mitigates the shortcomings of myopic dispatching polices by(i) proactively directing couriers towards areas requiring additional delivery capacity and (ii)temporally lowering service by displaying increased delivery time promises in certain areas.
In this paper, we investigate and answer the following questions: (i) Under what circumstances does fleet steering provide advantages? (ii) Under what circumstances does demand
steering provide advantages? (iii) Under what circumstances is it beneficial to use both supply
and demand steering? To explore these questions, we introduce the meal-delivery problem withadvanced scheduled couriers (MDP-ASC) and two related time-expanded networks that differ in
granularity in both time and space. The finer network represents orders and couriers using actual location and time information, and is used to determine dispatching decisions. The coarser
network represents orders and couriers at an aggregated level, across both time and space, andis used to determine steering actions.
As in many other meal delivery settings, in the MDP-ASC a set of couriers with known
working periods, also referred to as blocks, serve unknown orders that arrive during the operatingperiod. A customer expects to receive his meal at or before the delivery time promise displayed
at the time the order is placed. The platform assigns orders, more specifically the delivery tasks
associated with orders, to couriers. A delivery task involves the pick up of a meal at a restaurant
and the delivery of that meal at a customer’s location. We refer to decisions related to theassigning couriers to orders as dispatching decisions. Typically, a meal delivery platform relies
only on their dispatching decisions to maximize service (e.g., on-time performance) and courier
utilization.
Fleet steering seeks to incorporate knowledge about the future state of the system (althoughuncertain) into the dispatching decisions. That is, it seeks to influence courier - order assignments(including preemptive repositioning decisions) to ensure that delivery capacity is always in theright place at the right time. Demand steering supports the management of delivery capacity
by dynamically and temporarily increasing the displayed delivery time promises, which eases thepressure on dispatching decisions as more time is available to deliver future orders. Increasing
the delivery time promise allows more on-time deliveries, but it can negatively affect customers’experience (and even customers’ choice). The premise underlying demand steering is that customers favor upfront knowledge of delay (an increased delivery time promise) over unexpected
delay (a missed delivery time promise). To capture this concept, we introduce a metric called
quality loss, which accounts for any increase in delivery time promise as well as any lateness ofdelivery (relative to the delivery time promise).

2

Problem statement

In the MDP-ASC, a set of couriers K with working periods that have been scheduled in advancehave to perform delivery tasks associated with unknown set of meal delivery orders O that arrive
during a finite operating period [0, T ]. The service region has a set of restaurants, R, where themeals are prepared. Each arriving order o is characterized by a placement time, tpo ∈ [0, T ], aready time, tro ∈ [0, T ] (tro ≥ tpo ), a pickup location (a restaurant), `po , and a delivery location, `do .
Figure 1: Screenshot of the platform interface. The displayed delivery time promise is 25 minutes.

Each order o also has a delivery time promise tdo , which is derived from the displayed deliverytime promise at the time of order placement. The displayed delivery time promise for an orderis typically a default delivery time promise t̂do , which is strategically determined by the platform
TRISTAN XI Symposium

Original abstract submittal

Arslan, Sharif Azadeh , Savelsbergh, Maknoon

3Figure 2: Quality loss for order oand depends on the restaurant, the delivery location, and the time of day, and accounts formeal preparation time, travel time from pickup to delivery location, and market competition.
However, the platform can increase the displayed delivery time promise to manage any demandand supply imbalances.
Given an order’s delivery time promise of tdo , the service quality depends on (i) any increasein displayed delivery time promise, i.e., tdo − t̂do and (ii) any lateness of the delivery (relative tothe delivery time promise), i.e., max(0, t̃do − tdo ), where t̃do is the realized delivery time of order o.
We define the quality loss Q(t̃do , tdo ) of order o to beQ(t̃do , tdo ) = β1 (tdo − t̂do ) + β2 (t̃do − tdo | tdo = t̂do ) + αβ2 (t̃do − tdo | tdo > t̂do ),(1)where β1 : R 7→ R+ is a function that computes the quality loss due to an increase of a delivery
time promise, β2 : R 7→ R+ is a function that computes the quality loss due to lateness of adelivery, and α ≥ 1 captures the fact that a late delivery is perceived as worse when the delivery
time promise has already been increased.
Each courier k has a start time tk , a start location `k , an end time t̄k , and an end location`¯k . As courier k is compensated for the period t̄k − tk , we assume that the courier performs alltasks assigned to the courier during that period. (The first delivery task cannot start earlier
than tk and later than t̄k .) . We consider three possible tasks: delivery, the courier travels tothe pickup location to pick up a meal and travels to the delivery location to drop off a meal,reposition, the courier travels from the current location to a specified location, and wait, thecourier remains idle in the current location for a specified period. The platform seeks to minimize
the expected total quality loss over the orders arriving during the operating period:"#Xd dmin E
Q(t̂o , to ) .
(2)o∈OTo do so, the platform determines (i) which courier to assign to each order, (ii) whether or
not to reposition a courier and, if so, how to reposition the courier, and (iii) whether or not
to temporarily increase the displayed delivery time promise for an order (increasing the display
delivery time promise for an order to infinity is allowed and indicates that no meal can be orderedfrom the restaurant).

3Results

We design our numerical experiments with the set of instances derived from the public GrubHubinstances, see (Reyes et al., 2018) for detail explanation. We use ten public Grub-hub instances
varying in the service area, number of orders, restaurants and couriers. We use the location ofrestaurants and the orders destination as they are, and the arrival time of the orders.
The proposed steering actions provide a joint supply and demand steering guidance to thedispatching operations in the meal delivery service. We compare this joint dynamic supply anddemand steering policy to a series of benchmarks. In total we consider following benchmark
policies: No steering (NS). The platform carries out courier-order assignments solely based on
solving dispatching problem every minute Fleet steering (FS). Fleet and demand steering (FDS).

TRISTAN XI Symposium

Original abstract submittal

Arslan, Sharif Azadeh , Savelsbergh, Maknoon

4Figure 3: Relative Quality Loss to No Steering, ββ21 (·)(·) = 4, α = 3
120
104.8

Relative quality loss (Q(·) /QNS )

100

94.6
89.7

89.3

80

74.5

71.9
61.6

61.4

60
40
28.9

27.123.7
20.3

20

27.3
23.4

20.3

19.3

16.5
7.1

00

o0

o0

o1

0

o2

o3

Fleet steeringo4o5Instances

o6o7

o8

o9o9

Fleet & demand steering

Figure 3 presents the change of the total quality loss when the platform uses the steering
QF Sactions. The figure shows the relative quality loss to the no steering case i.e., Qgives theNSrelative quality loss when only fleet steering in use. The result shows that the total quality loss
drops drastically with steering actions.
When we look at the average of ten experiments, 51.5% of the total quality loss could beavoidable. It is possible to see the bigger impact in the reduction of late deliveries. With thesteering actions, 96.1% of the late deliveries would be delivered on time. On the other side,
31.5% of arriving order will see extended displayed delivery promise.
Table 1: Late deliveries
Instances
0o100t100s1p100
1o100t100s1p100
2o100t100s1p100
3o100t100s1p100
4o100t100s1p100
5o100t100s1p100
6o100t100s1p100
7o100t100s1p100
8o100t100s1p100
9o100t100s1p100

4Late deliveries %Ave Lateness (min)Maximum lateness (min)NS
2.0
8.9
0.3
0.4
13.6
18.9
19.6
7.3
1.3
2.5

NS
11.9
21.3
5.1
6.3
20.9
29.2
24.3
12.4
31.6
17.6

NS
30.8
76.3
9.9
15.3
53.8
133.8
91.7
39.4
148.2
59.9

FS
1.4
16.9
0.0
0.3
24.3
39.0
26.0
7.3
3.9
3.4

FDS
0000
00
0
000

FS
11.5
11.9
0
6.1
11.9
24.4
15.6
12.6
6.7
10.3

FS
20.8
43.5
0
12.4
31.5
93.9
54.9
52.5
55.6
49.2

REFERENCES

References
Arslan, Alp M., Agatz, Niels, Kroon, Leo, & Zuidwijk, Rob. 2019. Crowdsourced delivery—a dynamicpickup and delivery problem with ad hoc drivers. Transportation Science, 53(1), 222–235.
Castillo, Juan Camilo, Knoepfle, Dan, & Weyl, Glen. 2017. Surge pricing solves the wild goose chase.
Pages 241–242 of: EC 2017 - Proceedings of the 2017 ACM Conference on Economics and Computation, vol. 1. New York, NY, USA: Association for Computing Machinery, Inc.
Dayarian, Iman, & Savelsbergh, Martin. 2020. Crowdshipping and same-day delivery: Employing in-store
customers to deliver online orders. Production and Operations Management, 29(9), 2153–2174.
Reyes, Damián, Erera, A., Savelsbergh, M., Sahasrabudhe, Sagar, & O’Neil, Ryan J. 2018. The Meal
Delivery Routing Problem.
Singh, Sarwant. 2019 (9). The Soon To Be $200B Online Food Delivery Is Rapidly Changing The Global
Food Industry.
Ulmer, Marlin, & Savelsbergh, Martin. 2020. Workforce Scheduling in the Era of Crowdsourced Delivery.
Transportation Science, 54(4), 1113–1133.

TRISTAN XI Symposium

Original abstract submittal