Dynamic Time Slot Pricing Using Delivery Costs Approximations

Fabian Akkerman*, Martijn Mes, Eduardo Lalla-Ruiz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

72 Downloads (Pure)


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 standard home delivery services (Yrjölä, 2001). To influence customers to choose a time slot that results in fewer travel costs, companies often give incentives (discounts) or penalties (delivery charges) depending on the costs of a time slot. The main focus of this paper is on determining the costs of a time slot and adjusting time slot pricing accordingly, i.e., dynamic pricing. We compare two time slot cost approximation methods, a cheapest insertion formula and a method employing random forests with a limited set of features. Our results show that time slot incentives have added value for practice. In a hypothetical situation where customers are infinitely sensitive to incentives, we can plan 6 % more customers and decrease the per-customer travel costs by 11 %. Furthermore, we show that our method works especially well when customer locations are heavily clustered or when the area of operation is sparsely populated. For a realistic case of a European e-grocery retailer, we show that we can save approximately 6 % in per-customer travel costs, and plan approximately 1 % more customers when using our time slot incentive policy.

Original languageEnglish
Title of host publicationComputational Logistics
Subtitle of host publication13th International Conference, ICCL 2022, Barcelona, Spain, September 21–23, 2022, Proceedings
EditorsJesica de Armas, Helena Ramalhinho, Stefan Voß
Place of PublicationCham
Number of pages17
ISBN (Electronic)978-3-031-16579-5
ISBN (Print)978-3-031-16578-8
Publication statusPublished - 2022
Event13th International Conference on Computational Logistics, ICCL 2022 - Barcelona, Spain
Duration: 21 Sept 202223 Sept 2022
Conference number: 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13557 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Computational Logistics, ICCL 2022
Abbreviated titleICCL 2022


  • Time slot management
  • Dynamic pricing
  • Vehicle routing
  • Machine learning
  • Cost approximation
  • 22/3 OA procedure


Dive into the research topics of 'Dynamic Time Slot Pricing Using Delivery Costs Approximations'. Together they form a unique fingerprint.

Cite this