Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management
Online grocers typically let customers choose a delivery time slot to receive their goods. To ensure a reliable service, the retailer may want to close time slots as capacity fills up. The number of cus- tomers 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 customers 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.
|ERIM Report Series Research in Management|
|ERIM report series research in management Erasmus Research Institute of Management|
|Organisation||Rotterdam School of Management (RSM), Erasmus University|
van der Hagen, L, Agatz, N.A.H, Spliet, R, Visser, T.R, & Kok, A.L. (2022). Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management (No. ERS-2022-001-LIS). ERIM report series research in management Erasmus Research Institute of Management. Retrieved from http://hdl.handle.net/1765/137095