PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Stochastic models of risk management of worker fatigue emergence

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The purpose of the paper is to develop stochastic models for managing the risk of fatigue in an organisation, taking into account the intensity of the negative impact of fatigue factors on workers at the workplace and the intensity of their recovery from such an impact. Design/methodology/approach: It uses the method of analysis of scientific literature to actualise the purpose and define the research tasks; Markov process theory methods are used for mathematical description of random processes of worker fatigue development and their recovery from it during a work shift; methods of probability theory and queuing are used to find the limiting probability distribution of random Markov process’ states. Findings: The proposed stochastic models allow the organisation to carry out the process of managing the risk of fatigue emergence by changing the work-rest schedule’s duration, depending on the parameters’ characteristics of the negative impact intensity of fatigue factors on workers and the recovery of their corpora from such an impact. By changing the specified parameters’ characteristics, it is possible to determine the work schedule during which the period of worker’s fatigue will be as long as possible and the rest schedule during which the period of recovery from the fatigue state will be minimal. Practical implications: The application of the proposed models makes it possible to increase the level of labour productivity in the organisation by determining such durations of work and rest schedules, which provide the opportunity for workers to carry out labour activities during the maximum possible period of time of the work shift, without reaching a fatigued state. Originality/value: For the first time, an approach for managing the fatigue risk is proposed by establishing dependencies between the duration of work and rest schedule and the parameters’ characteristics of the negative impact intensity of the fatigue factors on the worker and their recovery from such an impact, based on the application of the Markov processes theory.
Rocznik
Strony
72--85
Opis fizyczny
Bibliogr. 58 poz., rys.
Twórcy
  • Department of Systems Management Life Safety, Odessa Polytechnic National University, Shevchenko ave., 1, Odessa, 65044, Ukraine
  • Department of Systems Management Life Safety, Odessa Polytechnic National University, Shevchenko ave., 1, Odessa, 65044, Ukraine
Bibliografia
  • [1] S.L. Murray, M.S. Thimgan, Human Fatigue Risk Management, 1 st Edition, Academic Press, Cambridge, MA, 2016. DOI: https://doi.org/10.1016/C2014-0- 02207-1
  • [2] G. Maisey, M. Cattani, A. Devine, I.C. Dunican, Fatigue Risk Management Systems Diagnostic Tool: Validation of an Organizational Assessment Tool for Shift Work Organizations, Safety and Health at Work 13/4 (2022) 408-414. DOI: https://doi.org/10.1016/j.shaw.2022.08.002
  • [3] S. Fan, Z. Yang, Accident data-driven human fatigue analysis in maritime transport using machine learning, Reliability Engineering and System Safety 241 (2024) 109675. DOI: https://doi.org/10.1016/j.ress.2023.109675
  • [4] A.P. Bochkovskуi, N.Yu. Sapozhnikova, Minimization of the “human factor” influence in Occupational Health and Safety, Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 6 (2019) 95-106. DOI: https://doi.org/10.29202/nvngu/2019-6/14
  • [5] F. Alonso, C. Esteban, S. Useche, E. López de Cózar, Prevalence of Physical and Mental Fatigue Symptoms on Spanish Drivers and Its Incidence on Driving Safety, Advances in Psychology and Neuroscience 1/2 (2016) 10-18. DOI: https://doi.org/10.11648/j.apn.20160102.12
  • [6] N. Muecklich, I. Sikora, A. Paraskevas, A. Padhra, The role of human factors in aviation ground operation-related accidents/incidents: A human error analysis approach, Transportation Engineering 13 (2023) 100184. DOI: https://doi.org/10.1016/j.treng.2023.100184
  • [7] F. Li, C.-H. Chen, P. Zheng, S. Feng, G. Xu, L.P. Khoo, An explorative context-aware machine learning approach to reducing human fatigue risk of traffic control operators, Safety Science 125 (2020) 104655. DOI: https://doi.org/10.1016/j.ssci.2020.104655
  • [8] D. Dawson, D. Darwent, G.D. Roach, How should a bio-mathematical model be used within a fatigue risk management system to determine whether or not a working time arrangement is safe?, Accident Analysis and Prevention 99/B (2017) 469-473. DOI: https://doi.org/10.1016/j.aap.2015.11.032
  • [9] GBD 2015 Mortality and Causes of Death Collaborators, Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015, Lancet 388/10053 (2016) 1459-1544. DOI: https://doi.org/10.1016/S0140-6736(16)31012-1
  • [10] World Health Organization and the International Labour Organization, WHO/ILO joint estimates of the work-related burden of disease and injury, 2000-2016, Global monitoring report, Geneva, 2021.
  • [11] International Labour Organization, Workplace stress: A collective challenge, Report, Geneva, 2016. Available from: https://www.ilo.org/wcmsp5/groups/public/---ed_protect/---protrav/---safework/documents/publication/wcms_466547.pdf (access in: 08.02.2024)
  • [12] International Labour Organization, Safety and Health at the Heart of the Future of Work: Building on 100 years of experience, Report, Geneva, 2019. Available from: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/documents/publication/wcms_686645.pdf (access in: 08.02.2024)
  • [13] W. Rohmert, W. Laurig, Evaluation of work requiring physical effort, Institute of Industrial Science Darmstadt Polytechnic, Directorate-General Social Affairs, Luxembourg, 1975.
  • [14] A.C. Reynolds, K.A. Loffler, N. Grivell, B.WJ. Brown, R.J. Adams, Diagnosis and management of sleep disorders in shift workers, with patient informed solutions to improve health services research and practice, Sleep Medicine 113 (2024) 131-141. DOI: https://doi.org/10.1016/j.sleep.2023.11.027
  • [15] R.P. Sari, B.N.A. Susanto, E. Komalasari, The correlation between work shift and level of fatigue among workers, Enfermería Clínica 31/S2 (2021) S450-S453. DOI: https://doi.org/10.1016/j.enfcli.2020.09.043
  • [16] J. Kang, S.C. Payne, F. Sasangohar, R.K. Mehta, Field-based longitudinal evaluation of multimodal worker fatigue assessments in offshore shiftwork, Applied Ergonomics 115 (2024) 104164. DOI: https://doi.org/10.1016/j.apergo.2023.104164
  • [17] B.S. Bhatia, R. Baumler, M.C. Arce, A. Pazaver, Adjustment of Work-Rest Hours Records in the Shipping Industry: A Systematic Review, Case Studies on Transport Policy 15 (2024) 101125. DOI: https://doi.org/10.1016/j.cstp.2023.101125
  • [18] A.P. Bochkovskуi, Improvement of risk management principles in occupational health and safety, Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 4 (2020) 94-104. DOI: https://doi.org/10.33271/nvngu/2020-4/094
  • [19] L. Schmidt, H. Luczak, Design of work systems according to ergonomic and health-promoting principles, in: D. Spath, E. Westkämper, H.-J. Bullinger, H.-J. Warnecke (eds), New developments in corporate organization, Springer Vieweg, Berlin, Heidelberg, 2017. DOI: https://doi.org/10.1007/978-3-662-55426-5_41 (in German)
  • [20] S. Xu, N.G. Hall, Fatigue, personnel scheduling and operations: Review and research opportunities, European Journal of Operational Research 295/3 (2021) 807-822. DOI: https://doi.org/10.1016/j.ejor.2021.03.036
  • [21] V.J. Gawron, Overview of self-reported measures of fatigue, The International Journal of Aviation Psychology 26/3-4 (2016) 120-131. DOI: https://doi.org/10.1080/10508414.2017.1329627
  • [22] J.M.R.S. Nascimento, L.G.M. Bispo, J.M. Norte da Silva, Risk factors for work-related musculoskeletal disorders among workers in Brazil: A structural equation model approach, International Journal of Industrial Ergonomics 99 (2024) 103551. DOI: https://doi.org/10.1016/j.ergon.2024.103551
  • [23] J. Li, J. Zhu, C. Guan, Assessing illumination fatigue in tunnel workers through eye-tracking technology: A laboratory study, Advanced Engineering Informatics 59 (2024) 102335. DOI: https://doi.org/10.1016/j.aei.2023.102335
  • [24] W. Yi, A.P.C. Chan, Optimizing work-rest schedule for construction rebar workers in hot and humid environment, Building and Environment 61 (2013) 104-113. DOI: https://doi.org/10.1016/j.buildenv.2012.12.012
  • [25] A.P.C. Chan, W. Yi, D.P. Wong, M.C.H Yam, D.W.M. Chan, Determining an optimal recovery time for construction rebar workers after working to exhaustion in a hot and humid environment, Building and Environment 58 (2012) 163-171. DOI: https://doi.org/10.1016/j.buildenv.2012.07.006
  • [26] W. Yoon, G. Shin, Muscle fatigue tracking during dynamic elbow flexion-extension movements with a varying hand load, Applied Ergonomics 116 (2024) 104217. DOI: https://doi.org/10.1016/j.apergo.2023.104217
  • [27] Y. Tuo, Z. Zhang, T. Wu, Y. Zeng, Y. Zhang, L. Junqi, Multimanned disassembly line balancing optimization considering walking workers and task evaluation indicators, Journal of Manufacturing Systems 72 (2024) 263-286. DOI: https://doi.org/10.1016/j.jmsy.2023.11.011
  • [28] W. Umer, Y. Yu, M.F.A. Afari, S. Anwer, A. Jamal, Towards automated physical fatigue monitoring and prediction among construction workers using physiological signals: An on-site study, Safety Science 166 (2023) 106242. DOI: https://doi.org/10.1016/j.ssci.2023.106242
  • [29] M.A. Darwish, Optimal workday length considering worker fatigue and employer profit, Computers and Industrial Engineering 179 (2023) 109162. DOI: https://doi.org/10.1016/j.cie.2023.109162
  • [30] B.Z.Q. Seah, W.H. Gan, S.H. Wong, M.A. Lim, P.H. Goh, J. Singh, D.S.Q. Koh, Proposed Data-Driven Approach for Occupational Risk Management of Aircrew Fatigue, Safety and Health at Work 12/4 (2021) 462-470. DOI: https://doi.org/10.1016/j.shaw.2021.06.002
  • [31] R.D. MacDonald, C. Wallner, Articles That May Change Your Practice: Fatigue Risk Management, Air Medical Journal 39/3 (2020) 162-163. DOI: https://doi.org/10.1016/j.amj.2020.03.007
  • [32] A. Lamp, J.M.C. Chen, D. McCullough, G. Belenky, Equal to or better than: The application of statistical non-inferiority to fatigue risk management, Accident Analysis and Prevention 126 (2019) 184-190. DOI: https://doi.org/10.1016/j.aap.2018.01.020
  • [33] M. Butlewski, G. Dahlke, M. Drzewiecka, L. Pacholski, Fatigue of Miners as a Key Factor in the Work Safety System, Procedia Manufacturing 3 (2015) 4732-4739. DOI: https://doi.org/10.1016/j.promfg.2015.07.570
  • [34] P. Cabon, S. Deharvengt, J.Y. Grau, N. Maille, I. Berechet, R. Mollard, Research and guidelines for implementing Fatigue Risk Management Systems for the French regional airlines, Accident Analysis and Prevention 45/Suppl. (2012) 41-44. DOI: https://doi.org/10.1016/j.aap.2011.09.024
  • [35] M. Yung, B. Du, J. Gruber, A. Yazdani, Developing a Canadian fatigue risk management standard for first responders: Defining the scope, Safety Science 134 (2021) 105044. DOI: https://doi.org/10.1016/j.ssci.2020.105044
  • [36] M. Ingre, W.V. Leeuwen, T. Klemets, C. Ullvetter, S. Hough, G. Kecklund, D. Karlsson, T. Akerstedt, Validating and Extending the Three Process Model of Alertness in Airline Operations, PLoS ONE 9/10 (2014) e108679. DOI: https://doi.org/10.1371/journal.pone.0108679
  • [37] J. Sun, Y. Liao, F. Lu, R. Sun, H. Jia, Assessment of pilot fatigue risk on international flights under the prevention and control policy of the Chinese civil aviation industry during the COVID-19, Journal of Air Transport Management 112 (2023) 102466 DOI: https://doi.org/10.1016/j.jairtraman.2023.102466
  • [38] M. Sprajcer, M.J.W. Thomas, C. Sargent, M.E. Crowther, D.B. Boivin, I.S. Wong, A. Smiley, D. Dawson, How effective are Fatigue Risk Management Systems (FRMS)? A review, Accident Analysis and Prevention 165 (2022) 106398. DOI: https://doi.org/10.1016/j.aap.2021.106398
  • [39] C. Zhang, Y. Ma, S. Chen, J. Zhang, G. Xing, Exploring the occupational fatigue risk of short-haul truck drivers: Effects of sleep pattern, driving task, and time-on-task on driving behavior and eye-motion metrics, Transportation Research Part F: Traffic Psychology and Behaviour 100 (2024) 37-56. DOI: https://doi.org/10.1016/j.trf.2023.11.012
  • [40] D. Querstret, K. O'Brien, D.J. Skene, J. Maben, Improving fatigue risk management in healthcare: A systematic scoping review of sleep-related/fatigue- management interventions for nurses and midwives, International Journal of Nursing Studies 106 (2020) 103513. DOI: https://doi.org/10.1016/j.ijnurstu.2019.103513
  • [41] M. Yung, B. Du, J. Gruber, A. Hackney, A. Yazdani, Fatigue measures and risk assessment tools for first responder fatigue risk management: A scoping review with considerations of the multidimensionality of fatigue, Safety Science 154 (2022) 105839. DOI: https://doi.org/10.1016/j.ssci.2022.105839
  • [42] M.E. McCauley, P. McCauley, S.M. Riedy, S. Banks, A.J. Ecker, L.V. Kalachev, S. Rangan, D.F. Dinges, H.P.A. Van Dongen, Fatigue risk management based on self-reported fatigue: Expanding a bio-mathematical model of fatigue-related performance deficits to also predict subjective sleepiness, Transportation Research Part F: Traffic Psychology and Behaviour 79 (2021) 94-106. DOI: https://doi.org/10.1016/j.trf.2021.04.006
  • [43] E. Gülmez, H.I. Koruca, M.E. Aydin, K.B. Urganci, Heuristic and swarm intelligence algorithms for work-life balance problem, Computers and Industrial Engineering 187 (2024) 109857. DOI: https://doi.org/10.1016/j.cie.2023.109857
  • [44] A.P. Bochkovskуi, Elaboration of occupational risks evaluation models considering the dynamics of impact of harmful factors, Journal of Achievements in Materials and Manufacturing Engineering 102/2 (2020) 76-85. DOI: https://doi.org/10.5604/01.3001.0014.6777
  • [45] A.P. Bochkovskуi, Elaboration of stochastic models to comprehensive evaluation of occupational risks in complex dynamic systems, Journal of Achievements in Materials and Manufacturing Engineering 104/1 (2021) 31-41. DOI: https://doi.org/10.5604/01.3001.0014.8484
  • [46] L.V. Shiryaeva, Methods and models for managing the reproduction of equipment parks. Probabilistic approach. Monograph, Astroprint, Odessa, 2008 (in Ukrainian).
  • [47] A.P. Bochkovskyi, Stochastic models of work and rest schedules, Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu 1 (2024) 114-121. DOI: https://doi.org/10.33271/nvngu/2024-1/114
  • [48] V.D. Gogunskii, A.P. Bochkovskii, A.U. Moskaliuk, O. Kolesnikov, S. Babiuk, Developing a system for the initiation of projects using a Markov chain, Eastern- Еuropean Journal of Enterprise Technologies 1/3(85) (2017) 25-32. DOI: https://doi.org/10.15587/1729- 4061.2017.90971
  • [49] M.Ya. Postan, Economic and mathematical models of intermodal transportation. Monograph, Astroprint, Odessa, 2006 (in Ukrainian).
  • [50] J.W. Seo, J. Lee, S. Jeon, Y. Hwang, J. Kim, S. Lee, S.J. Kim, Fatigue and somatization in shift-workers: Effects of depression and sleep, Journal of Psychosomatic Research 173 (2023) 111467. DOI: https://doi.org/10.1016/j.jpsychores.2023.111467
  • [51] O.C. Ibe, Markov Processes for Stochastic Modeling, 2 nd Edition, Elsevier, Amsterdam, 2013. DOI: https://doi.org/10.1016/C2012-0-06106-6
  • [52] I. Song, S.R. Park, S. Yoon, Probability and Random Variables: Theory and Applications, Springer, Cham, 2022. DOI: https://doi.org/10.1007/978-3-030-97679-8
  • [53] N.T. Thomopoulos, Fundamentals of Queuing Systems: Statistical Methods for Analyzing Queuing Models, Springer, New York, 2012. DOI: https://doi.org/10.1007/978-1-4614-3713-0
  • [54] W. Rohmert, H. Luczak, Determination of work load in field studies: evaluation and design of an inspection task, Le Travail Humain 37/1 (1974) 147-164 (in French).
  • [55] A.P. Bochkovskyi, N.Yu. Sapozhnikova, Development of system of automated protection of employees from covid-19 and other infections at the enterprise, Journal of Achievements in Materials and Manufacturing Engineering 112/2 (2022) 70-85. DOI: https://doi.org/10.5604/01.3001.0016.0705
  • [56] X. Fang, X. Yang, X. Xing, J. Wang, W. Umer, W. Guo, Real-Time Monitoring of Mental Fatigue of Construction Workers Using Enhanced Sequential Learning and Timeliness, Automation in Construction 159 (2024) 105267. DOI: https://doi.org/10.1016/j.autcon.2024.105267
  • [57] S.L. Grach, J. Seltzer, T.Y. Chon, R. Ganesh, Diagnosis and Management of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome, Mayo Clinic Proceedings 98/10 (2023) 1544-1551. DOI: https://doi.org/10.1016/j.mayocp.2023.07.032
  • [58] A.P. Bochkovskyi, N.Yu. Sapozhnikova, Development of system of automated occupational health and safety management in enterprises, Journal of Achievements in Materials and Manufacturing Engineering 107/1 (2021) 28-41. DOI: https://doi.org/10.5604/01.3001.0015.2454
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d4957243-645c-4c25-8471-54b7730be211
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.