Research on deep reinforcement learning (DRL) based production scheduling (PS) has gained a lot of attention in recent years, primarily due to the high demand for optimizing scheduling problems in diverse industry settings. Numerous studies are carried out and published as stand-alone experiments that often vary only slightly with respect to problem setups and solution approaches. The programmatic core of these experiments is typically very similar. Despite this fact, no standardized and resilient framework for experimentation on PS problems with DRL algorithms could be established so far. In this paper, we introduce schlably, a Python-based framework that provides researchers a comprehensive toolset to facilitate the development of PS solution strategies based on DRL. schlably eliminates the redundant overhead work that the creation of a sturdy and flexible backbone requires and increases the comparability and reusability of conducted research work.

翻译：近年来，基于深度强化学习（DRL）的生产调度（PS）研究因各工业领域对调度问题优化的高需求而备受关注。大量研究作为独立实验开展并发表，这些实验在问题设置和解决方案上往往仅存在细微差异，其编程核心通常高度相似。然而，目前仍缺乏标准化的弹性实验框架，用于支持基于DRL算法的PS问题研究。本文介绍了schlably——一个基于Python的框架，为研究人员提供综合工具集，以促进基于DRL的PS解决方案策略开发。schlably消除了构建稳固灵活框架所需的大量重复性工作，并提升了研究成果的可比性与可复用性。