ارائه رویکرد مبتنی بر سیستم استنتاج عصبی-فازی به منظور ارزیابی رضایت شغلی با درنظرگیری شاخص‌های HSEE

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی صنایع، دانشکدگان فنی، دانشگاه تهران، ایران

2 دانشکده مهندسی صنایع و مدیریت، دانشگاه صنعتی شاهرود، ایران

3 دانشکده مهندسی صنایع، دانشگاه علم و صنعت، تهران، ایران

10.22091/jemsc.2024.11008.1183

چکیده

رضایت شغلی منجر به افزایش بهرهوری و کارایی سازمانها می شود. با توجه به خطرات احتمالی که واحدهای نگهداری و تعمیرات با آن مواجه هستند، باید تمرکز خاصی بر تصمیمات و اقدامات کارکنان آنها باشد. این مطالعه از یک رویکرد یادگیری ماشین برای تقویت عملکرد و رضایت شغلی واحدهای نگهداری و تعمیرات از منظر رعایت نکات بهداشت، ایمنی، محیط زیست و ارگونومی (HSEE) استفاده می‌کند. در این پژوهش ابتدا یک پرسشنامه استاندارد برای جمع‌آوری داده ها طراحی شده است که پایایی آن با استفاده از ضریب آلفای کرونباخ ارزیابی می شود. در گام بعد مدل‌های مختلف سیستم استنتاج عصبی-فازی به منظور تخمین رضایت شغلی بر اساس اطلاعات مربوط به HSEE اجرا گردید. در گام بعد،کارایی هر یک از افراد با استفاده از خطای محاسبه شده تحلیل گردید. در شبکه استنتاج عصبی-فازی طراحی شده میانگین دسته‌های HSEE به‌عنوان ورودی مشخص و رضایت شغلی به‌عنوان خروجی در نظر گرفته شده است. نتایج حاکی از این است که افزایش رضایت شغلی کارکنان منوط به تمرکز روی بهبود مسائل مربوط به ارگونومی و محیط زیست می‌باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors

نویسندگان [English]

  • Mehrab Tanhaeean 1
  • Fatemeh Raeisi 2
  • Hamid Saffari 3
1 Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran
3 Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran
چکیده [English]

Job satisfaction plays a crucial role in enhancing productivity and reveals intriguing insights that impact the operational effectiveness of organizations. Due to the importance of maintenance units, special attention should be paid to their employees. This study employs a machine learning approach to enhance the performance and job satisfaction of maintenance units through the focus on health, safety, environment, and ergonomics (HSEE). A standardized questionnaire is developed for on HSEE data. Within the neural-fuzzy inference network, inputs such as health and safety protocols, environmental data collection, and its reliability is assessed using Cronbach's alpha coefficient. Subsequently, various adaptive neuro fuzzy inference system (ANFIS) models are utilized to predict job satisfaction based factors, and ergonomics are considered, while job satisfaction serves as the output. Following the selection of the optimal model, individual efficiency levels are assessed and scrutinized based on the calculated error. The findings suggest that enhancing employee job satisfaction relies on prioritizing the enhancement of ergonomics and the work environment.

کلیدواژه‌ها [English]

  • Safety
  • Job Satisfaction
  • Machine Learning
  • Adaptive neuro fuzzy inference system

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