Effects of OCRA parameters and learning rate on machine scheduling
- PDF / 443,740 Bytes
- 19 Pages / 439.37 x 666.142 pts Page_size
- 77 Downloads / 216 Views
Effects of OCRA parameters and learning rate on machine scheduling 3 Ercan Senyi ¸ git ˘ 1 · Ugur ˘ Atici2 · Mehmet Burak Senol ¸
Accepted: 28 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Machine Scheduling · Learning Rate · Risk Assessment · Risk Based Deterioration · WMSD · OHSAS · Ergonomics · OCRA · ANOVA
Communicated by E. van der Maaten. Extended author information available on the last page of the article
123
E. S¸ enyigit ˘ et al.
1 Introduction Occupational health and safety (OHSAS) regulations and risk assessment methods provide requirements that should be taken into account to protect labourers from occupational risks and accidents. Thus, risk assessments are employed to determine safety levels of machinery, improve ergonomics, labour efficiency, and performance. WMSD is one of the most common OHSAS risks encountered in the work environment. To the best of our knowledge, the effects of WMSD risks on processing times, which are caused by repeated activities, have not been studied in machine scheduling literature. To achieve efficiency in production and to obtain more realistic and effective schedules, the actual processing time should be calculated more accurately. Three important variables that are employed in calculating the actual process time are the basic processing time, deterioration rate, and
Data Loading...
