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Performance optimization of unique resilient human resource management system in a coal mine industry

Author

Listed:
  • Ali Azadeh

    (University of Tehran)

  • Delaram Heydarian

    (University of Tehran)

  • Keivan Nemati

    (École Nationale Supérieure d’Arts et Métiers (ENSAM))

  • Reza Yazdanparast

    (University of Tehran)

Abstract

Coal mine industry is recognized as a highly risk-oriented environment. Integrated resilience engineering can be employed to minimize the damages and hazards caused by sudden changes in such systems. It can be used to shift human resource management (HRM) to normal state in case of emergencies and unexpected events. Resilient HRM (RHRM) is composed of cognitive, behavioral, and contextual elements of organizational resilience. Moreover, this study presents and evaluates a new concept of RHRM in a real coal industry. An integrated algorithm composed of mathematical programming and statistical techniques is presented to assess and optimize RHRM. Data are obtained by standard questionnaire in a real mining industry. Results demonstrated that the cognitive dimension and the sub-dimension of employee contributions must be accounted as the most effective characteristics of the RHRM. In addition, skill was determined as the most significant sub-dimension of the cognitive dimension. This is the first study that investigates and optimizes the impact of resilience engineering on HRM in a coal mine industry. Second, a unique RHRM is presented and solved by multi-objective mathematical model. It is a practical approach, which can be implemented by decision makers in coal industries.

Suggested Citation

  • Ali Azadeh & Delaram Heydarian & Keivan Nemati & Reza Yazdanparast, 2018. "Performance optimization of unique resilient human resource management system in a coal mine industry," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 1178-1197, October.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:5:d:10.1007_s13198-018-0728-7
    DOI: 10.1007/s13198-018-0728-7
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    References listed on IDEAS

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    1. Charnes, A. & Cooper, W. W. & Sueyoshi, T., 1986. "Least squares/ridge regression and goal programming/constrained regression alternatives," European Journal of Operational Research, Elsevier, vol. 27(2), pages 146-157, October.
    2. Cynthia A. Lengnick-Hall & Tammy E. Beck, 2009. "Resilience Capacity and Strategic Agility: Prerequisites for Thriving in a Dynamic Environment," Working Papers 0059, College of Business, University of Texas at San Antonio.
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    5. Cary L. Cooper & Jill Flint-Taylor & Michael Pearn, 2013. "Building Resilience for Success," Palgrave Macmillan Books, Palgrave Macmillan, number 978-1-137-36783-9, October.
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    Cited by:

    1. Seyed Mohammad Asadzadeh & Hadi Maleki & Mehrab Tanhaeean, 0. "A resilience engineering-based approach to improving service reliability in maintenance organizations," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-14.
    2. Seyed Mohammad Asadzadeh & Hadi Maleki & Mehrab Tanhaeean, 2020. "A resilience engineering-based approach to improving service reliability in maintenance organizations," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(5), pages 909-922, October.
    3. Abdulilah Mohammad Mayet & Seyed Mehdi Alizadeh & Zana Azeez Kakarash & Ali Awadh Al-Qahtani & Abdullah K. Alanazi & Hala H. Alhashimi & Ehsan Eftekhari-Zadeh & Ehsan Nazemi, 2022. "Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime," Mathematics, MDPI, vol. 10(10), pages 1-13, May.
    4. Abdulaziz S. Alkabaa & Osman Taylan & Mustafa Tahsin Yilmaz & Ehsan Nazemi & El Mostafa Kalmoun, 2022. "An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
    5. Mehdi Salimi & Mahboubeh Khodaparast, 2019. "Providing a combined model of fuzzy AHP and numerical taxonomy analysis for sport organizational ranking and performance appraisal," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1133-1144, October.

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