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This study introduces the shift expert, an AI system identifying negative impacts of shift work. It prioritizes interventions to improve worker well-being and job performance.

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Area of Science:

  • Occupational Health
  • Human Factors Engineering
  • Artificial Intelligence

Background:

  • Shift work arrangements significantly influence job performance, worker motivation, and overall well-being.
  • Negative effects of shift work encompass demographic, sleep-related, health, and socio-domestic factors.
  • Identifying and mitigating these negative effects is crucial for optimizing work environments.

Purpose of the Study:

  • To design an expert system, the 'shift expert,' to identify adverse shift work effects.
  • To prioritize mitigation strategies based on their importance in preventing negative outcomes.
  • To provide tailored recommendations at both individual and organizational levels.

Main Methods:

  • A comprehensive literature review identified key factors associated with shift work.
  • A questionnaire was developed focusing on 26 critical issues related to shift work.
  • An expert system was built using a fuzzy analytic hierarchy process (AHP) for prioritization.
  • An empirical study involving 61 rotating shift workers was conducted.

Main Results:

  • The shift expert successfully identified negative effects of shift work across various domains.
  • Statistical analysis of questionnaire data informed the system's recommendations.
  • The fuzzy AHP model effectively prioritized interventions for shift workers.
  • Individual and organizational recommendations were generated for the study participants.

Conclusions:

  • The developed shift expert system offers a novel approach to managing the challenges of shift work.
  • Prioritized, evidence-based recommendations can enhance worker health, safety, and job performance.
  • This system provides a valuable tool for organizations seeking to optimize shift work arrangements.