Learning Dynamic Selection and Pricing of Out-of-Home Deliveries
Fabian Akkerman, Peter Dieter, Martijn Mes

TL;DR
This paper introduces DSPO, a dynamic decision-making algorithm for out-of-home delivery that optimizes location offering and pricing, significantly reducing costs in last-mile logistics by accounting for future customer behavior.
Contribution
The paper presents a novel dynamic selection and pricing model for out-of-home delivery, incorporating spatial-temporal encoding and neural networks, outperforming static and existing demand management methods.
Findings
DSPO reduces delivery costs by up to 19.9% compared to no OOH locations.
It outperforms static policies by 7% and demand benchmarks by 3.8%.
The approach provides insights into customer behavior and delivery dynamics.
Abstract
Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. A potential solution is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models for OOH delivery were so far limited to static settings, contrasting with the sequential nature of the problem. We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices. We propose Dynamic Selection and Pricing of OOH (DSPO), an algorithmic pipeline that uses a novel spatial-temporal state encoding as input to a convolutional neural network. We demonstrate the performance of our method by benchmarking it against two state-of-the-art approaches. Our extensive numerical…
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Taxonomy
TopicsUrban and Freight Transport Logistics · Advanced Manufacturing and Logistics Optimization · Transportation and Mobility Innovations
