Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management

Liana van der Hagen, Niels Agatz, Remy Spliet, Thomas Visser and Adrianus Kok

ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam

Abstract: 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 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

Keywords: time slot management; vehicle routing; supervised machine learning (search for similar items in EconPapers)
Pages: 29
Date: 2022-01-17
New Economics Papers: this item is included in nep-big, nep-cmp and nep-tre
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Citations: View citations in EconPapers (2)

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