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

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.