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Sustainable personnel scheduling supported by an artificial neural network model in a natural gas combined cycle power plant
International Journal of Energy Research ( IF 4.3 ) Pub Date : 2020-04-23 , DOI: 10.1002/er.5480
Emir Hüseyin Özder 1 , Evrencan Özcan 2 , Tamer Eren 2
Affiliation  

Despite the impact of personnel scheduling on the sustainability of production processes, it is a rarely studied problem in the electricity generation sector, whose main purpose is sustainable energy generation. From this point of view, personnel scheduling problem is handled in this study based on the impact of this problem, which focuses on fair and balanced job distribution according to the personnel qualifications, on the sustainable generation in the power plants. With its advantages such as ease of operation and maintenance, fast commissioning, lower greenhouse gas emissions compared to other fossil fuels and etc. one of the biggest Natural Gas Combined Cycle Power Plant (NGCCPP) in Turkey is chosen as application area. The main purpose of the study is to demonstrate the effect of fair, balanced and competency‐based personnel scheduling on generation stoppages resulting from personnel planning in power plants, which have great importance in energy supply security of the countries. This study is the first in the literature in terms of three different aspects. Firstly, in the energy sector, it is different in that it is a personnel scheduling study that takes into consideration the job assignment of the personnel according to their abilities and minimizes costs. Secondly, the large size of the problem is different from that of previous studies in the literature. Finally, the number of personnel employed in this generation facility varies according to the season. At this point, the past generation data are analyzed using an Artificial Neural Network (ANN) method and an estimation is made, and personnel scheduling is performed in the light of this information. Consequently, the proposed multi‐objective mathematical model supported with multi‐criteria decision making and ANN, shutdown rate of the power plant due to operator error is reduced 67.3%, and personnel satisfaction level is increased from 42% to 89%as solution.

中文翻译:

天然气联合循环电厂中人工神经网络模型支持的可持续人员调度

尽管人员调度对生产过程的可持续性有影响,但这在发电行业中很少研究,其主要目的是可持续能源生产。从这个角度出发,本研究基于此问题的影响来处理人员调度问题,该问题关注根据人员资格的公平和平衡的工作分配对电厂的可持续发电。与其他化石燃料相比,它具有易于操作和维护,调试方便,温室气体排放低等优点,因此被选为土耳其最大的天然气联合循环发电厂(NGCCPP)之一。研究的主要目的是证明公平,平衡和基于能力的人员调度,用于电厂的人员规划所导致的发电停工,这对各国的能源供应安全至关重要。就三个方面而言,这项研究是文献中的第一篇。首先,在能源领域,不同之处在于它是一项人员调度研究,它根据人员的能力来考虑其工作分配并最大程度地降低了成本。其次,问题的规模与文献中先前的研究不同。最后,该发电设施中雇用的人员数量根据季节而变化。在这一点上,使用人工神经网络(ANN)方法分析了上一代数据,并进行了估算,并根据此信息进行人员调度。因此,所提出的多目标数学模型得到了多准则决策和人工神经网络的支持,解决方案是将因操作人员错误而导致的电厂停工率降低了67.3%,人员满意度从42%提高到89%。
更新日期:2020-04-23
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