| Autor: |
Liana van der Hagen, Niels Agatz, Remy Spliet, Thomas R. Visser, Leendert Kok |
| Přispěvatelé: |
Department of Technology and Operations Management, Econometrics |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Transportation Science. INFORMS Institute for Operations Research and the Management Sciences |
| ISSN: |
0041-1655 |
| Popis: |
Online grocers typically let customers choose a delivery time slot to receive their goods. To ensure reliable service, the retailer may want to close time slots as capacity fills up. The number of customers that can be served per slot largely depends on the specific order sizes and delivery locations. Conceptually, checking whether it is possible to serve a certain customer in a certain time slot given a set of already accepted customer orders involves solving a vehicle routing problem with time windows. This is challenging in practice as there is little time available and not all relevant information is known in advance. We explore the use of machine learning to support time slot decisions in this context. Our results on realistic instances using a commercial route solver suggest that machine learning can be a promising way to assess the feasibility of customer insertions. On large-scale routing problems it performs better than insertion heuristics. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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