Demand and capacity management for meal delivery systems
Author(s)
Auad, Ramon
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
With the recent boom of the gig economy, urban delivery systems have experienced substantial demand growth. In such systems, orders are delivered to customers from local distribution points respecting a delivery time promise. An important example is a restaurant meal delivery system, where delivery times are expected to be minutes after an order is placed. The system serves orders by making use of couriers that continuously perform pickups and deliveries. Operating such a rapid delivery system can be very challenging, primarily due to the high service expectations and the considerable uncertainty in both demand and delivery capacity.
In this thesis, we study problems that are based on most recent challenges in meal delivery operations, namely (i) demand management in the form of service area control around restaurants, (ii) capacity management considering the satisfaction of couriers, and (iii) on-demand capacity acquisition based on real-time market signals. To contextualize out work, in Chapter 1 we give an overview of the meal delivery process, and comment on its past success and growth expectations for the future. We also discuss the main operational aspects that make meal delivery a challenging problem in logistics, and then motivate our study on demand and capacity management.
In Chapter 2, we seek to answer several questions in meal delivery operations focused on matching the correct levels of supply with demand. In particular, we consider a demand management mechanism in practice used by meal delivery providers to ensure acceptable customer service when the system is low in delivery capacity, which consists on decreasing demand during an operating day by temporarily reducing the delivery area for one or more restaurants. We show that simple demand restriction strategies allow a significantly smaller fleet to meet service requirements. To simplify analysis, we focus on problem geometries that enable the use of stylized mixed integer programs to optimally deploy a fleet of couriers serving large numbers of orders. Applying the proposed framework to several scenarios with one and two depots, we conduct an extensive experimental study of the effects on system performance of (i) allowing courier sharing between multiple depots, (ii) relaxing the delivery deadlines of placed orders, and (iii) restricting demand through limited adjustment of the service area of restaurants. The results demonstrate the potential effectiveness of different dispatch control and demand management mechanisms, in terms of both the required courier fleet size to serve requests and the coverage level of orders.
Chapter 3 in turn, is devoted to the study of capacity management by considering courier satisfaction. This is a critical aspect in terms of retention/loyalty in a highly competitive environment. Under the premise that couriers prefer to operate in relatively small geographic areas to increase their efficiency, we propose the novel concept of dynamic courier regions: small operating regions for couriers which can also be dynamically and temporarily expanded to allow delivery capacity to be shared between regions when necessary to keep customer service performance metrics high. We propose an optimization-based rolling horizon algorithm for courier management which handles both region resizing and request assignment decisions. Experimental results for realistic settings show that the proposed algorithm successfully balances customer and courier satisfaction, simultaneously achieving service quality levels that are comparable to those of a single operating region and courier satisfaction metrics that are comparable to those achieved by fixed, inflexible regions.
Lastly, in Chapter 4 we study the problem of dynamically adding extra courier capacity in a rapid delivery system. Delivery providers typically plan courier shifts for an operating period based on demand forecast. However, because of the high demand volatility it may at times during the operating period be necessary to adjust and dynamically add couriers. To address this problem, we propose a deep reinforcement learning approach to obtain a policy that balances the cost of adding couriers and the cost of service quality degradation by an insufficient number of couriers. Specifically, we seek to ensure that a high fraction of orders is delivered on time and with a small number of courier hours. A computational study shows that when performing corrective capacity adjustments, a learned policy using the proposed framework outperforms policies representing current practice in the meal delivery space, demonstrating the potential of deep learning for solving operational problems in highly stochastic logistic settings.
Sponsor
Date
2021-08-04
Extent
Resource Type
Text
Resource Subtype
Dissertation