Manager ex Machina – Investigation of the transferability of productionmanagement tasks by systems with artificial intelligence

由于人工智能（AI）的不断发展，出现了新的可能性，甚至可以通过计算机控制复杂的决策。从科学的角度来看，人工智能实例在哪些类型的生产管理决策方面上级人类。同样，这个问题也得到了很好的回答，并被科学地应用于国际象棋、围棋或危险等游戏的用例中。人工智能的进步，特别是在强化学习（RL）方面的进步表明，人工智能可以在复杂的场景中跟上甚至超越人类玩家。因此，本研究项目采取了将人类与“机器”（AI实例）之间的决斗中获得的知识转移到生产管理的原始方法。为此，生产管理决策在仿真环境中广泛建模，然后由人类和AI使用RL执行。在AI实例完成对模拟环境的学习之后，单独确定与模拟环境中的机器的性能相比的人的性能。这两种性能的比较提供了关于哪些决策上级基于AI的方法的信息，从而可以接管否则将由人类做出的决策。最后，为了确保仿真得出的结果适用于现实世界的环境中，他们是在一个真实的生产环境中验证的例子。知识的预期增益是通过将RL应用于生产管理决策来识别基于AI的方法可以采用的生产管理决策。其次，更重要的是归纳结论，回答了人机决斗是否适合作为评估生产管理中基于人工智能的方法的性能的方法。

Due to the continuous development of artificial intelligence (AI), novel possibilities arise to control even complex decisions by a computer. From a scientific perspective, the question arises for which types of production management decisions an AI instance is superior to a human. Similarly, this question has been answered with great resonance and scientific applied for use cases in games such as Chess, Go, or Jeopardy. Advances in AI, especially in reinforcement learning (RL), show that artificial agents can keep up with or even outperform human players in complex scenarios. Therefore, this research project takes the original approach of transferring the knowledge gained from the duel between humans and "machines" (AI instances) to production management. To this end, production management decisions are modeled with a broad spectrum in a simulation environment and then executed by both humans and an AI using RL. After the AI instance completes learning to the simulation environment, the performance of the human compared to the performance of the machine in the simulation environment is determined individually. The comparison of the two performances provides information on which decisions are superior to AI-based approaches and thus can take over the decision that would otherwise be made by a human. Finally, to ensure that the simulation-derived results are applicable in a real-world environment, they are validated in a real production environment as an example. The expected gain in knowledge is the identification of production management decisions that can be adopted by AI-based approaches by applying RL to production management decisions. Second, and more significant for the inductive conclusion, the question is answered whether the human-machine duel is suitable as a method for evaluating the performance of AI-based approaches in production management.