Estimating the solution value of transportation problems can be useful to assign customers
to days for multi-period vehicle routing problems, or to make customer selection decisions …

Cross-docking is a supply chain distribution and logistics strategy for which less-thantruckload
shipments are consolidated into full-truckload shipments. Goods are stored up to a …

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 …

Large discrete action spaces remain a central challenge for reinforcement learning methods.
Such spaces are encountered in many real-world applications, eg, recommender systems, …

This paper investigates dual sourcing problems with supply mode dependent failure rates,
particularly relevant in managing spare parts for downtime-critical assets. To enhance …

Attended home delivery (AHD) is a popular type of home delivery for which companies
typically offer delivery time slots. The costs for offering time slots are often double compared to …

This paper presents directions for using reinforcement learning with neural networks for
dynamic vehicle routing problems (DVRPs). DVRPs involve sequential decision-making under …

Approximating the solution value of transportation problems has become more relevant in
recent years, as these approximations can help to decrease the computational effort required …

Inventory Record Inaccuracy (IRI) is a significant challenge in inventory management,
caused by discrepancies between actual stock and inventory records due to factors such as …

Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning.
Existing solution approaches can handle unstructured LDAS with up to a few million actions. …