Effects of OCRA parameters and learning rate on machine scheduling

Şenyiğit E., Atici U., Senol M. B.

CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH, vol.30, no.3, pp.941-959, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.1007/s10100-020-00708-3
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Business Source Elite, Business Source Premier, EconLit, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.941-959
  • Keywords: Machine Scheduling, Learning Rate, Risk Assessment, Risk Based Deterioration, WMSD, OHSAS, Ergonomics, OCRA, ANOVA, JOB ROTATION SCHEDULES, MUSCULOSKELETAL DISORDERS, COMPLETION-TIME, INDEX, RISK, TASKS, MODEL
  • Erciyes University Affiliated: Yes


In this paper, the effects of Occupational Repetitive Actions (OCRA) parameters, learning rate on process times, and machine scheduling were investigated. We propose that Work-Related Musculoskeletal Disorder (WMSD) risks should be taken into account in machine scheduling. To the best of our knowledge, none of the earlier methods simultaneously considered effects of WMSD risks and the learning rate on processing times. The OCRA index method was employed for WMSD risk assessments. In this context, OCRA parameters such as duration, recovery, force, posture, and repetitiveness were analyzed. Observed process times of each factor were obtained from video records. Statistical analysis (ANOVA) revealed a positive (r=0.616) relationship on processing times with OCRA indexes in independent t-tests at significance level 0.05. To investigate the effects of WMSD risk, our Scheduling with Learning Effect under Risk Deterioration (SLE&RD) model was compared with six existing machine scheduling models in the literature. Detailed machine scheduling instances of 9 jobs with WMSD risks revealed that job sequences and makespan varied under different scenarios. This means that WMSD risks and OCRA factors affect machine scheduling with a deterioration effect. The results confirmed that when WMSD risks are included, actual process time and makespan move closer to observed process times. To obtain more accurate machine scheduling, which is close to real-life applications, WMSD risks, and learning rates should be considered simultaneously. The SLE&RD model is promising in machine scheduling for real-life problems and presents a holistic view of machine scheduling and WMSD risks.