Learning Dynamic Selection and Pricing of Out-of-Home Deliveries
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866912106190733312 |
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| author | Akkerman, Fabian Dieter, Peter Mes, Martijn |
| author_facet | Akkerman, Fabian Dieter, Peter Mes, Martijn |
| contents | 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 study, guided by real-world data, reveals that DSPO can save 19.9%pt in costs compared to a situation without OOH locations, 7%pt compared to a static selection and pricing policy, and 3.8%pt compared to a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest practitioners to adopt dynamic selection and pricing policies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_13983 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Dynamic Selection and Pricing of Out-of-Home Deliveries Akkerman, Fabian Dieter, Peter Mes, Martijn Machine Learning Artificial Intelligence 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 study, guided by real-world data, reveals that DSPO can save 19.9%pt in costs compared to a situation without OOH locations, 7%pt compared to a static selection and pricing policy, and 3.8%pt compared to a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest practitioners to adopt dynamic selection and pricing policies. |
| title | Learning Dynamic Selection and Pricing of Out-of-Home Deliveries |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2311.13983 |