论文标题
自动空间内组装任务分配问题的灵活的车间安排代表
A Flexible Job Shop Scheduling Representation of the Autonomous In-Space Assembly Task Assignment Problem
论文作者
论文摘要
随着空间勘探的增加，自主系统将在建立支持勘探的必要设施中发挥至关重要的作用。为此，自主系统必须能够在有效完成所需项目中所有工作的方案中分配任务。这项研究提出了一个灵活的车间问题（FJSP）表示，以表征一个自主组装项目，然后提出混合整数编程（MIP）解决方案配方和增强型学习（RL）溶液的公式。 MIP公式编码所有约束和互助动力学先验，并能够求解最佳解决方案以最大程度地减少MakePan。 RL公式没有收敛到最佳解决方案，而是通过与奖励功能的相互作用成功地学习了隐式互助动力学。未来的工作将包括开发一种解决方案公式，该公式利用两种建议的解决方案方法的优势来处理规模和复杂性的规模。
As in-space exploration increases, autonomous systems will play a vital role in building the necessary facilities to support exploration. To this end, an autonomous system must be able to assign tasks in a scheme that efficiently completes all of the jobs in the desired project. This research proposes a flexible job shop problem (FJSP) representation to characterize an autonomous assembly project and then proposes both a mixed integer programming (MIP) solution formulation and a reinforcement learning (RL) solution formulation. The MIP formulation encodes all of the constraints and interjob dynamics a priori and was able to solve for the optimal solution to minimize the makespan. The RL formulation did not converge to an optimal solution but did successfully learn implicitly interjob dynamics through interaction with the reward function. Future work will include developing a solution formulation that utilizes the strengths of both proposed solution methods to handle scaling in size and complexity.
