Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics
Published at : 28 Jul 2023
Volume : IJtech
Vol 14, No 5 (2023)
DOI : https://doi.org/10.14716/ijtech.v14i5.3854
Amalia, R., Ushada, M., Pamungkas, A.P., 2023. Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics. International Journal of Technology. Volume 14(5), pp. 1019-1028
| Rosa Amalia | Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No.1 Bulaksumur, 55281, Indonesia |
| Mirwan Ushada | Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No.1 Bulaksumur, 55281, Indonesia |
| Agung Putra Pamungkas | Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No.1 Bulaksumur, 55281, Indonesia |
Food
SMEs (Small and Medium Enterprises) were examples of labor-intensive industry,
which involved laborers in pursuing production activities. Food SMEs require
complex processes in production activities. Support to increase work
productivity and reduce ergonomic risks of the activities was needed. The study
was conducted at Tofu SMEs. The determination of the rest period could be developed
to give some recovery times to laborers. WBGT (Wet Bulb Globe Temperature) was
estimated to determine the rest period. The rest period was determined by the
workstation environment and workload labor. ANN (Artificial Neural Networks)
model was carried out due to a nonlinear relationship. ANN was used to process
the information from the data set and predict the amount of rest period and
WBGT. ANN was trained using backpropagation. The backpropagation algorithm used
the error value to change the weight with forward and backward propagation. The
result showed that dry bulb temperature, heart rate, wet bulb temperature, and
gender significantly impacted the rest period and WBGT. A total of 180 data
sets from tofu SMEs were divided into training data (80%) and validation data
(20%). The optimal ANN structure was determined by four input, four hidden, and
two output neurons. The activation function was sigmoid for both layers. SSE
(Sum of Squared Errors) was used to obtain the best structure. The value of R2
was equal to above 0.900, which indicated that ANN could model the labor rest
period based on environmental ergonomics.
Artificial neural networks; Labor; Rest period; Wet bulb globe temperature
SMEs had a vital role
in developing the Indonesian economy. SMEs carry out most business
organizations in Indonesia, with a total of 56.54 million units (Bank Indonesia and LPPI, 2015). SMEs also
employed a considerable amount, more than 90% of labor (Bank
Indonesia and LPPI, 2015). One of the characteristics of SMEs is
labor-intensive. Food SMEs require complex processes to produce value-added
products. Food product requires particular handling techniques due to their
perishable characteristic. Ushada et al.
(2017) mentioned some SMEs product delivery activities, such as boiling,
steaming, frying, baking, and assembly. The activities were closely influenced
by the workstation environment in SMEs.
Good management
practices and Sustainable Manufacturing practices (Hami
et al., 2018) could increase eco-efficiency and labor
productivity. The conditions were explored by Ushada
et al. (2017), as shown in Table 1. Production activities with
high temperatures could cause various effects on the body (Ushada et al., 2017). Physical work
combined with high temperature, radiation, and lousy air ventilation impacted
losses in productivity (Yi and Chan, 2013).
The body could experience muscle fatigue, decreased concentration, and heat
stress. The temperature between SMEs (Table 1) was above 30°C. Thus, laborers
could not perform activities effectively at 29.1°C due to heat discomfort (Ushada et al., 2017). Working under
thermal stress conditions had associated risks and consequences (Miller and Bates, 2007). Therefore, laborers
needed sufficient rest periods under these circumstances.
Table 1
Environment temperature in SMEs (Ushada et al.,
2017)
SMEs' work system was
influenced by various factors and mainly by workplace environmental ergonomics (Ushada and Okayama, 2018). Determining the rest
period could be adapted to reduce ergonomic risk at work (Tiacci, 2018). The determination of the rest period
was pursued to provide recovery time to laborers. Recovery time gave laborers
time to rest their bodies and restore the energy that comes out while working.
Every activity in production had specific characteristics; therefore, rest
period models, times, and frequencies were diverse and challenging to
standardize (Negreiros et al., 2019).
A Murell formula has been
developed to determine the rest periods model (Iridiastadi
and Yassierli, 2017). The Murell formula used work time and metabolic
rate to determine rest time. Also, in a previous study, Yi and Chan (2013) optimized the rest time schedule for
construction laborers in hot and humid working conditions. Ushada et al. (2017) developed an ANN model
to determine food-based SMEs' set initial temperature values. Batubara and Dharmastiti (2017) stated that
ergonomics intervention was used to improve work systems to reduce workload. Ushada et al. (2017) indicated the
nonlinear relationship between worker ergonomics status and workplace
environment. ANN (Artificial Neural Networks), as one of the Artificial
Intelligence approaches could frame nonlinear structures based on the human
brain system and were known as an estimator and could detect nonlinear
relationships between variables (Vinoth et al.,
2016). Previous studies did not demonstrate a link between the WBGT and
the rest period. Thus, an advanced rest period model using ANN was developed in
this study, considering the environmental ergonomics, labor circumstances, and
production times factor.
The study was conducted at
SME tofu in five districts in the Special Region of Yogyakarta. Based on the
preliminary research, the Special Region of Yogyakarta has a tofu SMEs center
in each district as the representative. The research objectives were (1) to
analyze the environmental workstation, labor workload, and rest period; and (2)
to develop an ANN model for determining labor rest periods. Based on the
objectives, the model could be expanded as an appropriate technology for
sustainable, ergonomic application in food SMEs. Thus, the research would
benefit the stakeholders in food SMEs, especially the owners, managers, and
laborers.
Figure 1 Conceptual
modelling of the ergonomic parameters of the labor and workstation environment
The
previous research by Li et al. (2016) showed that 14:00 to 15:00 was the high-risk
time for laborers, while 07:00 to 08:00 was the non-hazardous time for
laborers. Based on Li et al. (2016), the data were collected three times: initial,
intermediate, and final labor shift.
2.2. Rest Period and WBGT
Metabolic
rate (y) was determined by heart rate labor (x) in tofu SMEs. The metabolic
rate was calculated using a formula from Astuti in Ushada and Okayama (2018), as
shown in Equation 1.
Rest
period (R) was determined by labor work time (w), metabolic rate (b), and the
threshold value of labor work time (s). The rest period was calculated using a
Murell formula in Iridiastadi and Yassierli (2017), as shown in Equation 2.
WBGT was
determined by effective temperature (ET) and wet bulb temperature. ET was
obtained from Effective Temperature Chart, and wet bulb temperature from
Psychrometric Chart. WBGT was calculated using Equation 3 (OSHA, 2012).
Table 2
indicates the categories of metabolic rate and threshold for WBGT value based
on the Indonesian government (Minister of Health Indonesia, 2016). Table 2 is a standard
by the Minister of
Health Indonesia (2016) compared with the actual condition in tofu SMEs.
Table 2
Work cycle and rest period in an hour
2.3. Development
of ANN Model
Based on
Figure 1, the inputs of ANN were ergonomic parameters of labor and workstation
environment. The predicted outputs were the rest period and WBGT. Data sets
were divided into training data (n=144) and testing data (n=36). SSE (Sum of
Squared Errors) was used as an error value to determine the optimal combination
on the ANN model. SSE was minimized between the system output and the neural
network model output (Janczak, 2005). The R2 correlation was used to express
the reliability of the ANN model. ANN was used to predict continuous variables,
and a valuable measure of goodness of fit for each output was the coefficient
of multiple determination (R2) (Lingireddy and Brion, 2005).
ANN was
trained based on the backpropagation algorithm. Backpropagation had a layered
feed-forward neural network structure in which the nonlinear neurons were
arranged in consecutive layers; afterward, the information passed from the
input layer to the output layer through hidden layers (Vinoth et al., 2016). Ushada et al. (2017) found a
nonlinear relationship between environmental ergonomics in Food SMEs. Thus, ANN
could be a powerful computing approach for complex computations (Cavalieri, Maccarrone,
and Pinto, 2004).
The
training process was pursued to evaluate weights and biases in ANN (Argatov and Chai, 2019). The training process required a data set of
experimentally measured input-output (Argatov and Chai, 2019). The
training process was carried out in several stages as follows.
3. Training with variations in the value of the learning rate
3.1.
Respondents and Sample Location
This study obtained sixty
(60) respondents from laborers with three times different sampling times. A
total sample of one hundred and eighty (180) data sets was obtained. The data
samples were obtained from each district in the Special Region of Yogyakarta,
such as Yogyakarta, Bantul, Kulon Progo, Gunungkidul, and Sleman. Table 3
indicates the demographic information of the respondents. The
average age of the laborers in Tofu SMEs was 42.78 ± 13.64 years old, and the
majority ranged from 31 to 40 years old and 41 to 50 years old. The majority
gender of the laborers were men (n = 39), and the rest were women (n =
21). Based on the correlation test, age, and weight parameters had a value
below 0.200, which indicated a low degree of correlation
between the rest period and WBGT.
3.2.
Workload and Workstation Environment
Various tofu-making
processes were generally carried out in several SMEs. The processes were
soaking, milling, boiling, filtering, processing, solidifying, cutting, and
frying. These processes were pursued in a batch continuously. The activities in
the tofu production process can be categorized into potentials that cause
physical workload (Widyanti et al., 2017).
Thus, the activities affected the labor heart rate. Figure 2a shows a graph of
labor heart rate, indicating an increase in labor heart rate from the initial
to the final shift of the laborer.
Metabolic rate was counted
from labor heart rate. The result indicated three (3) categories such as light,
moderate, and heavy (Table 4). Figure 2b indicates an increase in metabolic
rate from the initial to final labor shift.
Figure 2 Labor condition: (a) Heart
rate; and (b) Metabolic rate
Table 4 Metabolic categories of labor
The workstation
environment was where the laborers did some activities to process the tofu. The
environment was observed during working time. The result showed dry bulb
temperature increased during work time (Figure 3a).
The average WBGT value in
tofu SME workstations was 26.39 °C. The value was still at the threshold for
WBGT in the industry (Minister of Health Indonesia,
2016). Although it met the threshold, the value had a level of
discomfort. WBGT value was included in the extremely hot environment category (Chowdhury, Hamada, and
Ahmed, 2017). Based on Chowdhury, Hamada,
and Ahmed (2017), it had the
risk of discomfort, but there was no health risk. Work inconvenience at SMEs
arose from 25.82 to 26.86 °C due to the extremely hot environment at the tofu
SMEs workstation (Figure 3b). Tofu SMEs use heat sources in most of the
process, which could impact increasing the temperature at the workstation.
Figure 3 Workstation environment: (a) Dry bulb temperature; and (b) WBGT
Table
5 indicates one hundred and sixty-three (163) laborers with a positive WBGT
gap. The positive value of the WBGT gap meant that WBGT in laborers did not
exceed the threshold value. Laborers were still comfortable with the WBGT
values and did well in their activities. On another side, the negative WBGT gap
value was found in ten (10) laborers. If the WBGT gap was negative, the WBGT
value exceeded the specified threshold. If the WBGT value exceeds the standard
value, it could indicate high heat stress on laborers (Bolghanabadi,
Ganjali, and Ghalehaskar 2019). Thus, during working hours, the activities increased, and performance
did rise after the activities began.
Table 5 Gap of
WBGT
3.3. Rest
Period
The rest period was a scheduled break
during a working day, and the laborers stopped their activities to rest, eat,
and any other needs (ILO, 2019). The rest
period was determined by metabolic rate labor during work time. Table 6
indicates the rest period of labor categorized by workload. Work breaks had a
range from -44.3 to 11.3 minutes for an hour. The negative number said that
laborers did not need an additional rest period because the workload was not
too heavy. It could be interpreted that the labor had an extra rest period of 0
minutes to calculate negative numbers. In light and moderate workloads, the
average additional rest period was obtained by a negative number, so laborers
in tofu SMEs did not need extra work breaks. Labors in the heavy workload
category had an average break time of 11.33 minutes. The results show the need
for additional breaks of one hour for each work with heavy categories carried
out in production activities. The work preferences related to the rest period
length were not equal for every labor (Di-Pasquale et
al., 2017). Research by Dababneh, Swanson,
and Shell (2001) showed
that frequent rest periods with short time would not decrease productivity.
Table 6 Rest
period based on workload categories
The rest period depended on the situation
in tofu SMEs. Production activities would affect the labor rest period.
Activities increased during working hours, and performance increased 2 hours
after starting work (Fahed, Ozkaymak, and Ahmed 2018). These conditions were similar to the
workstation at tofu SMEs. Labors took a rest during the working day to take
toilet breaks, prayer breaks or breaks to address other personal needs (ILO, 2019).
3.4. ANN
Model
Data normalization was performed by the
min-max normalization method. The method changed the data into a smaller range.
The data changed to a 0 to 1 range. T-test, F-test, and correlation tests were
performed. The result showed that dry bulb temperature (i1), heart
rate (i2), gender (i3), and wet bulb temperature (i4)
had a significant impact on the rest period (o1) and WBGT (o2).
These parameters had a high correlation value toward dependent variables so
that they could be used as an ANN input.
The result of the training process was an
optimum structure for WBGT and the rest period, as shown in Figure 4. To
improve the performance, ANN changed the weights between neurons in each layer
to modify the structure of the training data (Darvishi
et al., 2017). Training data with the lowest error value was at 4-4-2.
The activation function for the between layers was sigmoid. The optimum
learning rate was 0.01. That combination got SSE training 0.113 and SSE testing
0.037. The SSE indicated a low error.
Figure 4 ANN structure for
the rest period
The value of R2 between the
calculation and prediction of resting time was 0.989 (Figure 5a), while the
value of R2 between the calculation and prediction of WBGT was equal
to 0.968 (Figure 5b). An R2 value of 1.0 represented a perfect
prediction (Lingireddy and Brion, 2005). The
results indicated that the calculation of the rest period in explaining the
variance of the predicted rest period value was 98.9%. The value showed that
the prediction of the rest period could be explained well by calculating the
rest period. The WBGT value calculation explains that the WBGT prediction value
variance was 96.8%. The predicted WBGT value could be explained well by the
WBGT value calculation. An appropriate rest period could reduce the risk caused
by workload (Chen and Xie, 2014). Thus, the
ANN model of rest period was developed using dry bulb temperature, heart rate,
wet bulb temperature, and gender as the input with a rest period and WBGT as
the output.
Figure 5 R2 Value for (a) Rest period; (b) WBGT
Murell
formula (Equation 2) stated that the rest period was impacted by the labor work
time and metabolic rate (Iridiastadi and Yassierli,
2017). Tofu SMEs utilize heat sources for most of the process, which
affects the workstation's temperature. The results in this study indicated that
the dry bulb temperature, heart rate, wet bulb temperature, and gender
significantly impacted the rest period and WBGT, not only the work time and
metabolic rate. Laborers needed more time to rest in a hot work environment
since they were uncomfortable (Ushada et al.,
2017; Yi and Chan, 2013).
The novelty of the research was the relationship
between labor, workload, environmental ergonomics, rest periods, and WBGT. The
dry bulb temperature (i1), heart rate (i2), gender (i3),
and wet bulb temperature (i4) demonstrated a strong impact on the
rest periods (o1) and WBGT (o2) in the performance of
production tasks. The ANN model of WBGT and rest periods were the best
combination to solve the SME's issues. The rest periods could reduce the
discomfort, whereas the WBGT displayed a temperature-based measure of work
discomfort as the environment's temperature significantly impacted the rest
periods.
3.5.
Ergonomic Benefit and Practical Implication to SMEs
The model would benefit the stakeholders
in food SMEs, especially the owners, managers, and laborers. The owners and
managers could use it as a reminder for rest periods based on the condition of
environmental ergonomics. The worker could use the rest period properly and
feel comfortable while working.
The study results indicated that the
workload in tofu SMEs had three categories: light, moderate, and heavy. The
average threshold value of WBGT in tofu SMEs was 26.39 °C which was below the
threshold. Thus, laborers still felt comfort in carrying out their activities.
Additional rest periods for laborers in tofu SMEs are known to have a range
from 0 to 11.3 minutes for every hour of work. The result showed that dry bulb
temperature, heart rate, wet bulb temperature, and gender significantly
impacted the rest period and WBGT. The best ANN model was a 4-4-2 network
structure with four neurons in a hidden layer. The activation function used
between the layers both were sigmoid. The best learning rate used was 0.01. The
structure resulted in the value of SSE training at 0.113 and SSE testing at
0.037. The value of R2 between the calculation and prediction of
resting time was 0.989, while the value of R2 between the
calculation and prediction of WBGT was equal to 0.968. Thus, the model fits the
performance and can be applied to calculate the rest period and WBGT using
programming software. Therefore, the model could be used as a reminder for a
rest period based on the condition of environmental ergonomics.
This research was supported by the Ministry of Research, Technology and Higher Education of the Republic of Indonesia by 2019 Universitas Gadjah Mada Grants of "Penelitian Terapan Unggulan Perguruan Tinggi" No: 2770/UN1.DITLIT/DIT-LIT/LT/2019 and National Competitive Grants of "Penelitian Dasar" No: 2788/UN1.DITLIT/DIT-LIT/LT/2019.00.
Argatov, I.I., Chai, Y.S., 2019. An Artificial
Neural Network Supported Regression Model for Wear Rate. Tribology
International, Volume 138, pp. 211–214
Bank Indonesia and LPPI, 2015. Profil
Bisnis Usaha Mikro, Kecil dan Menengah (Business Profile of Small and Medium
Enterprises). Indonesia
Batubara, H., Dharmastiti, R. 2017. Redesign of
Liquid Aluminum Pouring Tool Based on Participatory Ergonomics to Improve
Productivity, Workload, and Musculoskeletal Disorders. International Journal
of Technology, Volume 8(2), pp. 352–361
Bolghanabadi, S., Ganjali, A., Ghalehaskar, S.,
2019. Investigation of Thermal Exposure in Traditional Neyshabur Bakeries Using
Heat Strain and Physiological Indices. MethodsX, Volume 6, pp. 355–359
Cavalieri, S., Maccarrone, P., Pinto, R., 2004.
Parametric Vs. Neural Network Models for The Estimation of Production Costs: A
Case Study in The Automotive Industry. International Journal of Production
Economics, Volume 91, pp. 165–177
Chen, C., Xie, Y., 2014. The Impacts of Multiple
Rest-Break Periods on Commercial Truck Driver's Crash Risk. Journal of
safety research journal, Volume 48, pp. 87–93
Chowdhury, S., Hamada, Y., Ahmed, K.S., 2017.
Prediction and Comparison of Monthly Indoor Heat Stress (WBGT and PHS) for RMG
Production Spaces in Dhaka, Bangladesh. Sustainable Cities and Society,
Volume 29, pp. 41–57
Dababneh, A.J., Swanson, N., Shell, R.L., 2001.
Impact of Added Rest Breaks on The Productivity and Well Being of Workers. Ergonomics,
Volume 44(2), pp. 164–174
Darvishi, E., Khotanlou, H., Khoubi, J., Giahi,
O., Mahdavi, N., 2017. Prediction Effects of Personal, Psychosocial, and
Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural
Networks Approach in Industrial Workers. Journal of Manipulative and
Physiological Therapeutics, Volume 40(7), pp. 486–493
Di-Pasquale, V., Fruggiero, F., Iannone, R.,
Miranda, S., 2017. A Model for Break Scheduling Assessment in Manufacturing
Systems. Computers and Industrial Engineering, Volume 111, pp. 563–580
Fahed, A.K., Ozkaymak, M.,
Ahmed, S., 2018. Impacts of Heat Exposure on Workers' Health and
Performance at Steel Plant in Turkey. Engineering Science and Technology, an
International Journal, Volume 21, pp. 745–752
Hami, N., Yamin, F.M., Shafie, S.M., Muhamad,
M.R., Ebrahim, Z., 2018. Sustainable Manufacturing Practices Among SMEs in
Malaysia. International Journal of Technology, Volume 9(8), pp.
1658-1667
International Labour Organization (ILO), 2019. Rest Periods: Definitions and
Dimensions. International Labour Organization
Iridiastadi, H., Yassierli, 2017. Ergonomi: Suatu Pengantar
(Ergonomic: An Introduction). Bandung: PT Remaja Rosdakarya
Janczak, A., 2005. Identification of Nonlinear
Systems Using Neural Networks and Polynomial Models. New York: Springer
Berlin Heidelberg New York
Li, X., Chow, K.H., Zhu, Y., Lin, Y., 2016.
Evaluating The Impacts of High-Temperature Outdoor Working Environments on
Construction Labor Productivity in China: A Case Study of Rebar Workers. Building
and Environment, Volume 95, pp. 42–52
Lingireddy, S., Brion, G.M., 2005. Artficial
Neural Networks in Water Supply Engineering. Restton: American Society of
Civil Engineers
Miller, V.S., Bates, G.P., 2007. The Thermal Work
Limit is a Simple Reliable Heat Index for The Protection of Workers in
Thermally Stressful Environments. Annals of occupational hygiene, Volume
51(6), pp. 553–561
Minister of Health Indonesia, 2016.
Permenkes RI no.70 tentang Standar dan Persyaratan Kesehatan Lingkungan Kerja
Industri (Regulation of Minister of Health of Republic of Indonesia No. 70
About Standards and Requirements for Industrial Environmental Health).
Indonesia
Negreiros, A.W.F.T.R.W. de, Silva, P.R. da,
Arezes, P.M.F.M., Dangelino, R., Padula, R.S., 2019. Manufacturing Assembly
Serial and Cells Layouts Impact on Rest Breaks and Workers' Health. International
Journal of Industrial Ergonomics, Volume 70, pp. 22–27
OSHA, 2012. Heat Stress, Plant Stress
Physiology. Occupational Safety and Health Administration
Tiacci, L., 2018. The Problem of Assigning Rest
Times to Reduce Physical Ergonomic Risk At Assembly Lines. IFAC-PapersOnLine,
Volume 51(11), pp. 692–697
Ushada, M., Okayama, T., 2018. Development of
Green-Affective Work System for Food SMEs. KnE Life Science, Volume
2017, pp. 89–95
Ushada, M., Okayama, T., Khuriyati, N.,
Suyantohadi, A., 2017. Affective Temperature Control in Food Smes Using
Artificial Neural Network. Applied Artificial Intelligence, Volume 31,
pp. 555–567
Vinoth, B., Rajarathian, A., Baumgartner, A., Bargavi,
S.K.M., 2016. Nonlinear Regression and Artificial Neural Network Based Model
for Forecasting Paddy (Oryzasativa) Production in Tamil Nadu. IOSR
Journal of Mobile Computing and Application (IOSR-JMCA), Volume 3(3), 1–6
Widyanti, A., Sofiani, N.F., Soetisna, H.R.,
Muslim, K. 2017. Eye Blink Rate as a Measure of Mental Workload in a Driving
Task: Convergent or Divergent with Other Measures. International Journal of
Technology, Volume 8(2), pp. 283–291
Yi,
W., Chan, A.P.C., 2013. Optimizing Work-Rest Schedule for Construction Rebar
Workers in Hot and Humid Environment. Building and Environment, Volume
61, pp. 104–113