Collective Learning for Energy-centric Flexible Job Shop Scheduling
Collective Learning for Energy-centric Flexible Job Shop Scheduling
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DOI：
10.1109/isie51358.2023.10228029
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
A. Narayanan;Evangelos Pournaras;P. Nardelli
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作者：
A. Narayanan;Evangelos Pournaras;P. Nardelli
制造业可以通过利用制造过程中的灵活性（如机器可用性、灵活的作业和资源使用）来减少能源消耗。在本文中，我们利用某些作业、作业调度和作业能耗所固有的灵活性来建模一个以能源为中心的柔性作业车间调度问题。我们假设驱动机器的电能是有限的，我们试图将机器作业的时间表与可用的能量相匹配，以最小化能量消耗为目标。我们建议集体学习，即一种分散（无监督）学习的形式，自主代理协调他们的决策，集体学习和管理可以通过协调有效执行的任务，可以用来实现这一目标。我们提出了一种结合规划生成算法和集体学习工具-迭代经济规划和优化选择（I-EPOS）的方法，以接近最优解来解决这个问题。我们将该方法应用于包含3台机器和12个作业的实际数据集，并表明当他们选择和调度作业时，而不是独立行动或没有任何协调时，能源消耗减少了约5%。我们还表明，可以将该方法扩展到大量作业中以快速获得合理的解决方案，并且通过增加节能，协调总是优于不协调和独立的操作。
Manufacturing industries can reduce their energy consumption by exploiting the flexibility in manufacturing processes, such as machine availability, flexible jobs, and resource usage. In this paper, we exploit the flexibility inherent in some jobs, their schedules, and their energy consumption to model an energy-centric flexible job shop scheduling problem. We assume that there is limited electrical energy to power machines, and we attempt to match the schedules of machine jobs to the available energy with the objective of minimizing the energy consumption. We propose that collective learning, i.e., a form of decentralized (and unsupervised) learning where autonomous agents coordinate their decision-making to collectively learn and manage tasks that can be efficiently performed by coordination, can be employed to achieve this. We present a methodology that combines a plangeneration algorithm with a collective-learning tool—Iterative Economic Planning and Optimized Selections (I-EPOS)—to solve this problem with near optimal-solutions. We apply the methodology to a practical dataset comprising 3 machines and 12 jobs and show that the energy consumption decreases by approximately 5% when they choose and schedule the jobs, instead of acting independently or without any coordination. We also show that it is possible to scale this method to a large number of jobs to obtain reasonable solutions quickly, and that coordination always outperforms uncoordinated and independent actions by increasing the energy savings.