Comparison of Manual and Automated Decision-Making with a Logistics Serious Game

Martijn Mes*, Wouter van Heeswijk

*Corresponding author for this work

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

3 Citations (Scopus)
112 Downloads (Pure)


This paper presents a logistics serious game that describes an anticipatory planning problem for the dispatching of trucks, barges, and trains, considering uncertainty in future container arrivals. The problem setting is conceptually easy to grasp, yet difficult to solve optimally. For this problem, we deploy a variety of benchmark algorithms, including two heuristics and two reinforcement learning implementations. We use the serious game to compare the manual performance of human decision makers with those algorithms. Furthermore, the game allows humans to create their own automated planning rules, which can also be compared with the implemented algorithms and manual game play. To illustrate the potential use of the game, we report the results of three gaming sessions: with students, with job seekers, and with logistics professionals. The experimental results show that reinforcement learning typically outperforms the human decision makers, but that the top tier of humans come very close to this algorithmic performance.

Original languageEnglish
Title of host publicationComputational Logistics - 11th International Conference, ICCL 2020, Proceedings
EditorsEduardo Lalla-Ruiz, Martijn Mes, Stefan Voß
Number of pages17
ISBN (Print)9783030597467
Publication statusPublished - 22 Sept 2020
Event11th International Conference on Computational Logistics, ICCL 2020 - Online conference, Enschede, Netherlands
Duration: 28 Sept 202030 Sept 2020
Conference number: 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12433 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Computational Logistics, ICCL 2020
Abbreviated titleICCL
Internet address


  • Approximate dynamic programming
  • Heuristics
  • Intermodal transport
  • Reinforcement learning
  • Serious gaming
  • 22/2 OA procedure


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