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

Martijn Mes; Wouter van Heeswijk

doi:10.1007/978-3-030-59747-4_45

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

Martijn Mes^*, Wouter van Heeswijk

^*Corresponding author for this work

Industrial Engineering & Business Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

148 Downloads (Pure)

Abstract

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 language	English
Title of host publication	Computational Logistics - 11th International Conference, ICCL 2020, Proceedings
Editors	Eduardo Lalla-Ruiz, Martijn Mes, Stefan Voß
Publisher	Springer
Pages	698-714
Number of pages	17
ISBN (Print)	9783030597467
DOIs	https://doi.org/10.1007/978-3-030-59747-4_45
Publication status	Published - 22 Sept 2020
Event	11th International Conference on Computational Logistics, ICCL 2020 - Online conference, Enschede, Netherlands Duration: 28 Sept 2020 → 30 Sept 2020 Conference number: 11 https://iccl2020.nl/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12433 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Computational Logistics, ICCL 2020
Abbreviated title	ICCL
Country/Territory	Netherlands
City	Enschede
Period	28/09/20 → 30/09/20
Internet address	https://iccl2020.nl/

Keywords

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

Access to Document

10.1007/978-3-030-59747-4_45

Mes-Heeswijk2020_Chapter_ComparisonOfManualAndAutomatedFinal published version, 1.33 MBLicence: Taverne

Cite this

Mes, M., & van Heeswijk, W. (2020). Comparison of Manual and Automated Decision-Making with a Logistics Serious Game. In E. Lalla-Ruiz, M. Mes, & S. Voß (Eds.), Computational Logistics - 11th International Conference, ICCL 2020, Proceedings (pp. 698-714). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12433 LNCS). Springer. https://doi.org/10.1007/978-3-030-59747-4_45

Mes, Martijn ; van Heeswijk, Wouter. / Comparison of Manual and Automated Decision-Making with a Logistics Serious Game. Computational Logistics - 11th International Conference, ICCL 2020, Proceedings. editor / Eduardo Lalla-Ruiz ; Martijn Mes ; Stefan Voß. Springer, 2020. pp. 698-714 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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Mes, M & van Heeswijk, W 2020, Comparison of Manual and Automated Decision-Making with a Logistics Serious Game. in E Lalla-Ruiz, M Mes & S Voß (eds), Computational Logistics - 11th International Conference, ICCL 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12433 LNCS, Springer, pp. 698-714, 11th International Conference on Computational Logistics, ICCL 2020, Enschede, Netherlands, 28/09/20. https://doi.org/10.1007/978-3-030-59747-4_45

Comparison of Manual and Automated Decision-Making with a Logistics Serious Game. / Mes, Martijn ; van Heeswijk, Wouter.
Computational Logistics - 11th International Conference, ICCL 2020, Proceedings. ed. / Eduardo Lalla-Ruiz; Martijn Mes; Stefan Voß. Springer, 2020. p. 698-714 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12433 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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Mes M , van Heeswijk W. Comparison of Manual and Automated Decision-Making with a Logistics Serious Game. In Lalla-Ruiz E, Mes M, Voß S, editors, Computational Logistics - 11th International Conference, ICCL 2020, Proceedings. Springer. 2020. p. 698-714. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59747-4_45

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

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