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Research article
A genetic algorithm approach to design job rotation schedules ensuring
homogeneity and diversity of exposure in the automotive industry
Ana Assunç~
ao
a
,
*
,Nafiseh Mollaei
b
,Jo
~
ao Rodrigues
b
, Carlos Fuj~
ao
c
, Daniel Os
orio
b
,
Ant
onio P. Veloso
a
, Hugo Gamboa
b
, Filomena Carnide
a
a
Biomechanics and Functional Morphology Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Cruz Quebrada Dafundo,
Portugal
b
Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UNL), Faculty of Sciences and Technology of NOVA University of Lisbon,
Caparica, Portugal
c
Volkswagen Autoeuropa –Industrial Engineering &Lean Management, Quinta da Marquesa, Palmela, Portugal
HIGHLIGHTS
This is the first formulation to consider diversity, exposure, and homogeneity.
A GA was used and proven to be a reliable solution to design job rotation.
The formulation increased diversity, decreased exposure, and balanced homogeneity.
Better results were achieved in all outcomes when compared with manual solutions.
The formulation is less time-consuming improving factory resource's management.
ARTICLE INFO
Keywords:
Automotive industry
Musculoskeletal disorders
Prevention approach
Workplace intervention
Genetic algorithm
Occupational risk factors
ABSTRACT
Job rotation is a work organization strategy with increasing popularity, given its benefits for workers and com-
panies, especially those working with manufacturing. This study proposes a formulation to help the team leader in
an assembly line of the automotive industry to achieve job rotation schedules based on three major criteria:
improve diversity, ensure homogeneity, and thus reduce exposure level. The formulation relied on a genetic al-
gorithm, that took into consideration the biomechanical risk factors (EAWS), workers’qualifications, and the
organizational aspects of the assembly line. Moreover, the job rotation plan formulated by the genetic algorithm
formulation was compared with the solution provided by the team leader in a real life-environment. The
formulation proved to be a reliable solution to design job rotation plans for increasing diversity, decreasing
exposure, and balancing homogeneity within workers, achieving better results in all of the outcomes when
compared with the job rotation schedules created by the team leader. Additionally, this solution was less time-
consuming for the team leader than a manual implementation. This study provides a much-needed solution to
the job rotation issue in the manufacturing industry, with the genetic algorithm taking less time and showing
better results than the job rotations created by the team leaders.
1. Introduction
Musculoskeletal disorders (MSD) are the most common work-related
health problem worldwide (Sebbag et al., 2019), being considered one of
the top reasons for work absenteeism (Durand et al., 2014). Within this
context, work-related musculoskeletal disorders (WRMSDs) have a sig-
nificant impact on the declined working capacity and quality of life of
workers, as well as high costs for companies and society due to produc-
tivity loss and healthcare services (De Kok et al., 2019). Preventing
WRMSDs is especially important in repetitive jobs with less exposure
variation, fewer breaks, and prolonged low-level exertions, such as that
in the automotive industry (Mossa et al., 2016), since these jobs tend to
be the reason behind the higher number of WRMSDs on the long term
(Aryanezhad et al., 2009).
* Corresponding author.
E-mail address: aassuncao@campus.ul.pt (A. Assunç~
ao).
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2022.e09396
Received 10 December 2021; Received in revised form 12 February 2022; Accepted 5 May 2022
2405-8440/©2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Heliyon 8 (2022) e09396
Alongside other measures to reduce the incidence of WRMSD (i.e.
engineering, processes, and product changes on the assembly line), the
job rotation plans have been recommended as an organizational measure
to reduce the exposure in workplaces to several risk factors and, thus,
increase the variability and reduce worker fatigue and monotony (Jor-
gensen et al., 2005;Rodriguez and Barrero, 2017;Yung et al., 2012).
Within the several solutions found in the literature to optimize job
rotation plans, there are mixed-integer programming to upper extrem-
ities (Boenzi et al., 2013;Digiesi et al., 2018;Xu et al., 2012), minimizing
net present cost within a lean manufacturing cell (McDonald et al., 2009),
multi-criteria fuzzy-genetic algorithms for assembly line balancing
(Rajabalipour Cheshmehgaz et al., 2012), and diploid genetic algorithm
(GA) in dynamic environments (Bhasin et al., 2016).
The GA stands out from the remaining solutions since it can solve
complex mathematical problems in situations where there are a large
number of possible outcomes and the environments are dynamic (Car-
nahan et al., 2000). In fact, the GA have already been implemented in
different automotive industry scenarios with several studies using this
approach to reduce the risk of MSDs and maximize the diversification of
the job rotation plans (Asensio-Cuesta et al., 2012a,2012b;Diego-Mas
et al., 2009). For instance, the GA solution provided by Diego et al. for an
automotive parts supplier assembly plant (Diego-Mas et al., 2009),
focused on maximizing the diversification while using a multi set of
criteria that characterized the workplace by physical, mental, and
communication capacities. The same authors also used a GA approach to
design a job rotation in environments characterized by high repeatability
of movements (Asensio-Cuesta et al., 2012a). Compared to their previous
work, authors added information from the Occupational Repetitive Ac-
tion (OCRA) screening tool, in which they assessed the presence of risk
factors when performing repeated movements. The solution was able to
diversify the tasks in order to aid the recovery of workers in between
jobs. In a different take on this topic, Asensio-Cuesta and colleagues
(Asensio-Cuesta et al., 2012b) developed another GA solution that
considered the competence criterion related with product quality and
employee satisfaction as a measure for the goodness of solutions.
Although the method used is the same, the choice, the number, and the
diversity of variables included in the model (e.g. movements, general
capacities, task time) as well as the criteria used to establish the GA (e.g.
capacity to perform the movement, frequency of movement per minute)
differ between studies, which leads to different results and amplifies the
lack of consensus in the literature regarding the effectiveness of rotation
plans (Comper and Padula, 2014).
Although most of the studies have focused on the issue of diversity for
the development of the job rotation plans, other criteria may have a
significant impact in reducing the risk of MSDs, and should not be
overlooked, such as the homogeneity (i.e. balanced effort) between
workers and the overall exposure (i.e. daily demand) to risk factors.
Moreover, the majority of the GAs used in the literature relied on changes
in the intensity of the task to increase the diversity of the job rotations,
which was achieved by using specific or general ergonomic risk assess-
ment metrics, differing in respect to the level of detail regarding evalu-
ation sections they cover (Carnahan et al., 2000;Diego-Mas et al., 2009).
Moreover, most of the studies covered the issue of job rotation plans in an
automobile parts supplier industry, with a lack of information on as-
sembly lines of big automotive plants, where the specificities of the tasks
performed may have different implications for WRMSDs. To the best of
our knowledge, currently there is no suitable solution to tackle the job
rotation issue in the automotive industry that focuses not only on the
diversity criteria, but also ensures the reduction of exposure throughout
the working shift, and safeguards the homogeneity within the team,
while using objective ergonomic indicators to build a job rotation plan.
This study's aims were two-fold: 1) to develop a formulation based
objective ergonomic indicators and workers qualifications to generate a
job rotation plan based on diversity, homogeneity, and exposure criteria
for an assembly line in the automotive industry, solved by means of a GA;
and 2) provide an industrial case study where the GA was tested and
applied to a randomly selected team from the automotive assembly area
in a real life-environment, in order to compare the performance of the job
rotation plan formulated by the new GA versus that of the team leader.
Given the length and detail of the GA, and to guide the reader, the
manuscript is organized in to the following sections: In section 2,we
address the modelling assumptions used to apply the GA, provide a
detailed description of the job rotation variables included in the GA and
explain the respective mathematical formulation. In section 3,we
describe the GA architecture and the several steps needed to provide the
best closing condition. Section 4presents the results of an industrial case
study, where the GA was tested in a real life-environment. Finally, in
section 5, the results are discussed and wrapped up by a conclusion in
section 6.
2. Methods
2.1. Modelling assumptions
To apply the GA in this study, several assumptions were considered,
including organizational conditions, workforce, and workstation char-
acteristics, which were made to cope with real-life environments con-
straints of this assembly line, including:
Workers perform the workstations that they are qualified to, ac-
cording to the versatility matrix of the respective team.
In each rotation period, only one workstation could be assigned to
each worker.
During a shift, the same workstation should not be assigned to a
worker more than once.
Workstations with high demands on the same body region should not
be consecutively assigned to the same worker.
Any workstation can be assigned in the first period of the shift, as full
recovery from one day to the next is assumed.
All variables of the formulation are deterministic and constant during
the planning horizon.
The allocation of workers to workstations is independent of gender,
efficiency, and quality.
2.2. Notation
The notation used in the proposed model is available in Table 1.In
Table 2, the risk factors and respective abbreviations are presented.
2.3. Job rotation plan variables
Two main types of variables were considered to design the job rota-
tion plan: (1) biomechanical variables; and (2) organizational variables.
2.3.1. Biomechanical variables
The main variables used to define the quality assessment of a job
rotation schedule were: (1) the overall risk score of each workstation,
resulting from the assessment of the biomechanical and organizational
work conditions; (2) the duration and intensity of the biomechanical risk
factors present in each workstation such as posture, force and manual
material handling (MMH).
Data on biomechanical work conditions (intensity, duration and fre-
quency) were collected from the ergonomics evaluation made through
the Ergonomics Assembly Worksheet method (EAWS) (Schaub et al.,
2013) performed by certified ergonomists. The corresponding methods
evaluated the movements made by a worker while performing the
workstation. This method assessed:
working postures and movements with low additional physical
efforts;
action forces of the hand-finger system and/or whole body;
MMH;
A. Assunç~
ao et al. Heliyon 8 (2022) e09396
2
repetitive loads on the upper limbs.
As a result, a combined score of all these risk factors was used and an
overall exposure score was assigned to the workstation characterized by a
traffic light colour scheme: green - no risk or low risk (0–30 points);
yellow - possible risk (31–49 points); and red - high risk (>50 points)
(Schaub et al., 2013).
2.3.2. Organizational variables
The team's versatility matrix was obtained from the Team Leader. The
matrix indicates the qualifications of workers. In other words, it provides
which workstations can be assigned to which workers according to their
skills. The duration of each rotation period differs between shifts (early,
late, and night shifts) and even between teams within the same area.
Also, a common approach in practice is to estimate ergonomic risks as a
time-weighted average of the respective ergonomic points for the
different jobs. Thus, this data was also included to calculate the occu-
pational exposure score for the quality assessment metric.
2.4. Defining the fitness function
The fitness function is the core of this work. In this function, the
mathematical formulation that guides optimization algorithms, such as
the GA, was integrated to reach the solutions that were desired. In this
section, we describe how this mathematical formulation was created
based on the aforementioned variables.
The quality of the job rotation schedule was estimated with variables
that are present in the working day of each worker. The EAWS data was
used to characterize the occupational environment. These scores quantify
the risk of each workstation and provide an individual picture of each of
the risk factors that were used for the global score. The way these vari-
ables are combined to give a representative score of the job rotation
schedule should maximize its purpose, which is to assign a sequence of
workplaces that promotes the variation in posture, load, and muscle
activity (Mathiassen, 2006).
Furthermore, the proposed mechanism for building the fitness func-
tion was composed of three layers of analysis: (1) overall averaged
occupational exposure score, (2) diversity calculated for the sequence of
workstations considering the risk factors, and (3) a homogeneous rota-
tion schedule, so that the scores assigned to the team were balanced
between workers.
2.4.1. Exposure
The first layer of assessment involved calculating the average occu-
pational exposure score from the sequence of workstations assigned to
each worker. The occupational exposure score of a workstation (OErot )ina
given rotation period rot was calculated according to Eq. (1), considering
the network shift time:
OErot ¼APws Δt%rot (1)
The time was fixed according to the rotation period in which the
workstation was allocated. Finally, the resulting score for a sequence of
workstations (OEw) performed by a worker over the set of rotation pe-
riods (nrot ¼4) was given according to Eq. (2):
OEw¼X
nrot
rot¼1
OErot (2)
The OEwhas to be normalized to obtain a value between 0 and 1 as an
output. A sequence with a score of 0 was the best possible sequence of
Table 1. Index and parameters definition.
Index Definition
ws Index of workstations, where ws ¼1,2,…,WS
wIndex of workers, where w¼1,2,…,W
rot Index of rotation periods, where rot ¼1,2,3,4
iIndex of categories of each risk factor or risk factor layers, where i¼1,…,
N
lIndex of layers of the force risk factor categories, where l¼1,2,…,L
tIndex of the workplace transition period, where t ¼1,2,…,R1
rf Index of risk factors of the EAWS, where rf ¼p;mmh or f
Parameters
OErot Score of a workstation on a rotation period rot (See Eq.1)
APws Overall score of a workstations ws
Δt%rot Percentage of time of rotation period rot
OEwOccupational exposure score of a sequence of workstations attributed to a
worker w(See Eq.2)
NOEwNormalized occupational exposure score of a sequence of workstations
attributed to worker w(See Eq.3)
minwMinimum occupational exposure score of worker w
maxwMaximum occupational exposure score of worker w
tsAtTransition score of the risk factor group A (e.g. tsp- posture and tsmmh -
Manual Material Handling) for the transition period t(See Eq.4)
tsAt;iTransition score given to the category iof the risk factor (group A) for the
transition period t
tsBtTransition score of the risk factor group B (tsf- force) for the transition
period t
tsBt;lTransition score given to the layer lof the risk factor (group B) for the
transition period t(See Eq.5)
tsBt;l;iTransition score given to the layer land category iof the risk factor (group
B) for the transition period t
tsw;rf Transition score of a sequence for risk rf and worker w(See Eq.6)
tstTransition score for the transition period t
TswTransition score of a sequence for worker w(See Eq.7)
Wrf Weight of risk factor rf
σ
oe Standard deviation of the NOE scores of the team (See Eq.8)
σ
dStandard deviation of the Tsscores of the team (See Eq.10)
NOE Mean NOE score for the team
TsMean transition score of the team
SWSQwShift working sequence quality for worker w(See Eq.13)
SWSQ Mean shift working sequence quality (See Eq.14)
Hom Homogeneity score (See Eq.12)
HomdHomogeneity score for diversity
Homoe Homogeneity score for occupational exposure
MQ Matrix quality index of the job rotation plan (See Eq.15)
Table 2. Risk factors and abbreviations.
Risk factor Definition
pPosture
NS Neck and shoulders
TTrunk
EElbow
MMH Manual Material Handling
Rep Repositioning
Car Carrying
Hold Holding
Pu Push and Pull
fAction forces
WB Whole body
HF Hand and fingers
SL Arms at shoulder level
ASL Arms above shoulder level
BTrunk bent
SB Trunk strongly bent
GA6 Elbow at 60% extension
GA8 Elbow at 80% extension
GA10 Elbow at 100% extension
A. Assunç~
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3
workstations considering the qualification matrix. On the other hand, the
score of 1 represents the worst possible sequence of workstations. The
lowest exposure score (minw) was therefore associated with 0, while the
highest (maxw) was associated with 1. Before the algorithm was applied,
the worst and best reference exposure sequences for each worker were
calculated. The normalization was made taking into consideration these
reference values (minwand maxw):
NOEw¼OEwminw
maxwminw
(3)
where NOEwwas the normalized occupational exposure score for a given
worker's (w) sequence.
2.4.2. Diversity
The second layer of assessment consisted of calculating the diversity
in the sequence of workstations. Diversity is the amount of change in the
exposure score between successive workstations for each one of the
following risk factors: posture, force, and MMH. Therefore, this measure
should guide the algorithm to reach solutions that have a high diversity.
Generally, diversity was calculated through a score for the transitions
between categories of exposure in successive workstations (in a multi-
layered process). It is relevant to mention that the term transitions was
intended to represent the change in the presence of a risk factor between
successive workstations. Since there were 4 working periods, there were
3 transitions evaluated. Independent of the risk factor, each transition
can be categorized, based on the presence (1) or absence (0) of a risk
factor, as one of the three possible types of transitions showed in Figure 1,
namely Type 1, 2 or 3:
Type 1 transitions - there is a change between the presence and
absence of risk factor in two consecutive workstations (presence to
absence, or vice-versa). The score for this transition is 1, as it is the type
of transition preferred to be searched.
Type 2 transitions - the risk factor is absent in two consecutive
workstations, so the score is
1
/
3
(absence to absence). This value was
given because the absence of a risk factor in two consecutive worksta-
tions should not be evaluated as bad, but the algorithm should be guided
in searching for solutions that have diversity, therefore it should be
scored under 1. This way, type 2 transitions are favoured against type 3
transitions, but not with regard to type 1 transitions.
Type 3 transitions - the risk factor is present in two consecutive
workstations, thus being the non-desirable transition. The score attrib-
uted to this type of transition was dependent on the risk factor evaluated.
The process to calculate the score of a transition depends on the risk
factor category. For posture and MMH, the process is showed in Figure 1,
while for force, the process is showed in Figure 2.
Diversity in posture and manual material handling
The diversity in posture and MMH was calculated following the same
rationale. The first step was to verify the presence of a risk factor in the
next workstation. Therefore, if the risk factor was present in the first
workstation (1) but not in the next one (0) (or vice versa - type 1 tran-
sitions), then the score for the transition was calculated for the risk factor
between these two workstations was 1. However, if the risk factor was
absent in both (type 2 transitions), then the transition score was 1/3. If
the risk factor was present in the first two workstations, which means
that no transition existed, then a second step was needed.
EAWS evaluates posture according to time spent in an awkward
posture during the cycle time. A transition in the sequence would mean
that the difference in the scores of the following workstations was sig-
nificant. To establish significance levels, the distributions of the risk
factor scores for each posture category, and each worker were divided
into four percentiles (0–25%, 25–50%, 50–75%, and 75–100%). Tran-
sitions were considered significant when consecutive workstations had
scores belonging to different percentiles. If there was a change in the
percentile, the score was 1, and if not, the score was 0. Although the body
region was recruited in two consecutive workstations, the intensity with
Figure 1. Diversity in posture and manual material handling. The process is depicted as a flowchart. When the risk factor is present in both workstations, the process
iterates over the categories of the risk factor, being ithe iterator variable.
A. Assunç~
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4
which this recruitment took place was different, and, therefore, there was
diversity. This diversity was sought with this algorithm.
The process to calculate the diversity score for MMH was the same as
posture. In the case of posture, a diversity score for each body region (N ¼
3) (i.e. elbow, trunk, and shoulder/neck) was calculated. In the case of
MMH, 4 categories (N ¼4) were considered: repositioning, carrying,
holding, and pushing and pulling.
Equation 4 shows the process to calculate the transition score (tsAt)
for posture and MMH:
tsAt¼X
N
i¼1
tsAt;i
NtsAt;i¼1if Qa6¼ Qb;wsrot 2Qa;wsrotþ12Qb
0if Qa¼Qb;wsrot 2Qa;wsrotþ12Qb
(4)
Here, the tsAtis the score for the transition of the risk factor; irep-
resents the categories of the risk factor; tsAt;iis the score for the transition
tand the category ifor the risk factor (body region and MMH categories),
and Qais the percentile where the workstation ws on the rotation period
rot belongs to, with Qbbeing the percentile belonging to where the
workstation ws is on the next rotation period rot þ1. To have an output
between 0 and 1, a denominator factor Nwas used, which is equal to the
number of categories in each factor.
Diversity in force
The calculation of diversity in force follows the same logic as in the
previously mentioned risk factors. However, when facing a transition of
type 3, the process was made in more layers and differently. The first step,
like posture and MMH, was checking if a risk factor was present in
consecutive workstations and if so, the following layers were evaluated:
(1) the presence of that risk in one or both systems “whole-body”and/or
“hand-arm-finger”; (2) if present, at what intensity and, (3) in what type,
dynamic or static. Figure 2 represents the calculation of diversity for force.
Looking at Figure 2, the rationale followed in a downward direction
until the transition was verified or until the last layer was met (the type of
force).
In the case of being present in two consecutive workstations, the score
was calculated in the next layer to verify the change in the presence or
absence of the risk factor in the whole body or hand-arm finger system.
Next, the process was repeated, and if the risk factor was presented in
both workstations in the system layer, the presence and absence of the risk
factor was checked for each intensity level (light, medium, high, stressed,
maximum, or >maximum). If the risk factor was observed in both
workstations at a specificintensity level, the calculation of the score goes
deeper and the change of presence and absence of the risk factor was
evaluated for the force mode (dynamic or static). Finally, in that layer, if
the presence of the risk factor was verified in both workstations, then the
output was 0.
The details in Eq. (5) show how to calculate the diversity in force:
tsBt;l¼X
Nl
i¼1
tsBt;l;i
Nl
tsBt;l;i¼
8
>
>
>
>
>
>
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
>
>
>
>
>
>
:
1:for type 1transitions
1
3:for type 2transitions
tsBt;l:for type 3transitions;
with l ¼lþ1and if l <3
0:for type 3transitions;
if l ¼3
(5)
In this case, tsBt;lis the transition score given to layer l,tsBt;l;iis the
transitions score for the category iin that layer l.Nlis the number of
categories of layer land iis the iterator over the categories.
2.4.3. Total diversity score
For each one of the risk factors described in the previous sections, the
transition score tshad to be accounted for all rotation periods during the
working day. Therefore, the diversity score of each risk factor was
calculated with Eq. (6):
tsw;rf ¼X
R1
t¼1
tst(6)
Figure 2. Flowchart to calculate force diversity. In each layer, on the left, it is indicated the number of categories (N). On the right side, the possible scores attributed
to type 1 (top), type 2 (middle), and type 3 (down) transitions are presented.
A. Assunç~
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5
Note that, tsw;rf is the transition score for risk factor rf for worker win
the transition period t, resulting from the sum of the transition score tst
for the transition period t.
The total diversity score, considering all risk factors was calculated.
As the effect of the risk factors on occupational exposure was not equal,
the relevance of the transition score of each risk factor was weighted
(Wrf ) differentially: 3 for posture, 2 for force, and 1 for MMH. The
rationale for this choice was based on the ergonomics assessment and the
weight of each risk factor to the total score (Bao, 2015). The score of each
change in the workplace was then the sum of the transition score for each
risk factor normalized between 0 and 1. The final score value was
calculated according to Eq. (7):
Tsw¼Prf Wrf tsrf
6(7)
In this formulation, rf is the risk factor considered: posture, force, or
MMH.
2.4.4. Homogeneity
The homogeneity was the last variable included in the fitness func-
tion, and our formulation. In order to guarantee the balance between
the team, homogeneity aimed to guide the algorithm to avoid favouring
workers differently. The homogeneity score was calculated after the
occupational exposure and diversity score were calculated for all of the
team workers. The standard deviation of occupational exposure (Eq. 8)
and diversity scores (Eq. 10) was calculated. Then, the homogeneity
contribution of occupational exposure (Eq. 9) and diversity (Eq. 11)was
determined.
σ
oe ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
WX
W
w¼1NOEwNOE2
v
u
u
t(8)
Homoe ¼1
σ
oe (9)
where: the
σ
oe is the standard deviation of occupational exposure, Wis
the number of workers on the team, wis the iterator over the workers,
NOEwis the occupational exposure score for the worker wand NOE is the
mean occupational exposure score of the team. Homoe is the homogeneity
contribution of the exposure.
σ
d¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
WX
W
w¼1TswTs2
v
u
u
t(10)
Homoe ¼1
σ
d(11)
Here
σ
dis the standard deviation of occupational exposure, Wis
the number of workers on the team, wis the iterator over the
workers, Tswis the diversity score for the worker wand Ts is the
mean diversity score of the team. Homoe is the homogeneity contri-
bution of diversity.
Since the standard deviation is a measure of dispersion, the higher
the value the worse the balance is of the job rotation plan between
workers. As a mean to have a value with a positive trend (the higher
the better), the homogeneity score (Hom) results from an inverse sum
of both standard deviations. The final homogeneity score is given by
Eq. (12):
Hom ¼HomdþHomoe (12)
2.5. Formulation of the fitness function
The fitness function is the combination of occupational exposure,di-
versity, and homogeneity. For each worker sequence, a score was calcu-
lated for occupational exposure and diversity, normalized between 0 and 1.
The index that characterizes the quality of this worker sequence was the
weighted sum of both scores, 2 for diversity, and 1 for occupational
exposure (Eq. 13).
SWSQw¼1scoreOEwþ2scoreDw(13)
In this case, SWSQwis the quality of the shift working sequence index
for worker w. Note that the occupational exposure score has a negative
trend (the lower the better), therefore the subtraction in the equation was
used to invert the trend of the parameter.
The shift working sequence quality (SWSQ), which means the quality
of the job rotation plan for the entire team (i.e. characterizes the job
Figure 3. Flowchart of the genetic algorithm architecture: Step (1): Creating
initial population with valid chromosomes - randomly generated. Step (2):
Evaluating the fitness of population members applying Eq. (13), which considers
exposure, diversity, and homogeneity. Step (3): Selection of the individuals that
will undergo crossover and mutation with 2% Elitism (E), 10% Tournament (T),
and 30% Rank-Based Wheel (RW). Step (4): Apply Crossover and Mutation
methods. Step (5): Generate an offspring population from the selected chro-
mosomes. Step (6): If the closing condition is met, return the best offspring (Step
7), otherwise, return to step 2. Abbreviations: SWSQ –Mean shift working
sequence quality; SWSQw- Shift working sequence quality for worker w;OX–
ordered crossover.
A. Assunç~
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6
rotation plan in terms of occupational exposure and diversity), was calcu-
lated by averaging the set of indexes (Eq. 14).
SWSQ ¼X
W
w¼1
SWSQw(14)
Finally, the homogeneity score was added, resulting in the matrix
quality index (MQ)(Eq. 15):
MQ ¼SWSQ þ0:25 Hom (15)
Since the search should favour job rotation schedules with reduced
exposure and high diversity above homogeneous schedules, a weight of
0.25 was calculated for the homogeneity score to adjust its influence in
the guidance of the algorithm.
The fitness function is then the MQ index, which has to be maximized
to reach solutions that increase the diversity, reduce the exposure and
increase homogeneity, as presented in Eq. (16):
max MQ ¼SWSQ þ0:25 Hom (16)
3. Heuristic approach for job rotation scheduling
The fitness function (Eq. 15) represents the quality of the job rotation
plan regarding occupational exposure, diversity, and homogeneity. This
function guides the algorithm in generating a job rotation plan that
maximizes the MQ function (Eq. 16). From this formulation, any opti-
mization algorithm can be applied to reach a desired solution. In this
case, the proposed algorithm was based on a GA, which was already
applied in similar contexts by Diego-Mas et al. (2009). In this section, we
describe the several steps that comprehend the GA's architecture
(Figure 3). The GA relies on the natural selection theory, in which evo-
lution of the overall population into better offspring was expected over
several iterations.
The algorithm starts by generating the initial population. Thereafter,
in each iteration, a selection of a set of chromosomes that belonged to the
population pool were selected to perform a crossover with their genes
and/or were mutated, expecting that better chromosomes would be
created over the iteration process that ended when a closing condition was
verified. The proposed genetic algorithm followed the same architecture.
In this case, the nomenclature is defined in Figure 4, showing that the
population is the overall set of possible job rotation plans; the chromosome
belonging to the population is a valid job rotation plan; and the gene
belonging to the chromosome is a workstation of the job rotation plan.
The way a GA is structured can vary extensively and several ap-
proaches were found in the literature for the selection, crossover, and
mutation steps. The chosen methods depend on the type of problem itself
and the restrictions that the problem implies. In this case, the main re-
strictions were related to the definition of a valid job rotation plan. The
structure of the proposed algorithm will be explained further, namely
which methods were used for the selection, crossover, and mutation, as
well as what comprised was the closing condition.
3.1. Population generation
The GA started by randomly generating a primary population pool
that contained a set of chromosomes. Each of these chromosomes is valid
and cannot be generated against the constraints defined. Thus, a chro-
mosome had a size of nwnrot , with nwbeing the number of workers
(equal to the number of workstations) and nrot the number of rotation
periods. One workstation was randomly assigned to each of the cells in
the matrix, and no workstations was repeated on the same row. The
initial number of chromosomes in the population can vary. The value of
100 individuals was considered, after obtaining satisfactory results for
the case of 12 workstations and 4 rotation periods. Each of the chro-
mosomes belonging to the population was evaluated by the fitness
function to get a score that characterized their fitness.
3.2. Selection
Having the starting population, the next step was to select a set of
chromosomes for the search space exploration with crossover and
Figure 4. Nomenclature of the genetic algorithm. The population is regarded as the group of possible job rotation plans; the chromosome is a valid job rotation plan
and the gene is a workstation.
A. Assunç~
ao et al. Heliyon 8 (2022) e09396
7
mutation. The selection had several criteria. The main idea was that
the population should be able to evolve and chromosomes should have
better scores over the iteration process. First, it was necessary to guar-
antee the presence of 2% of the best chromosomes of the population for
the next iteration, a process called elite selection. Second, for this pro-
cess, a rank-based roulette wheel selection (Goldberg, 1989) was used to
select 30% of the population pool. It is important to note that a chro-
mosome selected was excluded from the population set to avoid further
repetitions in the selection.
3.3. Crossover
The selected chromosomes were the base individuals that origin the
new population for the next iteration. The crossover was responsible for
50% of the new population. During crossover, the selected chromosomes
were merged based on a specific method. When merging the information
of two chromosomes, the sequences of workstations attributed to each
worker based on the information of two job rotation plans was expected
to be reorder. The problem in swapping information from one job rota-
tion plan to the other was that the offspring would probably be invalid,
because: (i) it would have repeated workstations on the same rotation
period; and (ii) the workstations assigned to a worker might not be
present in his/her qualification matrix. To tackle these constraints, the
proposed solution was to use a permutation-based crossover method
applied column-wise to the chromosomes. In this case, the method
considered was the ordered crossover (OX) (Moscato, 1989). Consider the
example presented in Figure 5. For this example, we assumed that there
were six different workstations and six different workers. The colour and
the corresponding number represent each workstation. The correspond-
ing qualification matrix is presented in Figure 1 Supplementary Material.
From two job rotation plans (matrix1and matrix2), a random number
of rotation periods (column) were selected to go through the OX method.
From matrix1a column was selected as the first parent (parent1, and the
same column from matrix2was selected as the second parent (parent2Þ.
The OX method starts by selecting randomly a subsection of workstations
from parent1. The child was mapped by inserting into parent2, on the
same subsection positions, the subsection of parent1.InFigure 6,ws1; 2
and 6 were shifted from ws6; ws5 and ws4. After that, the repeating
workstations (ws1 and ws2 - group A) were deleted from parent2. The now
missing workstations (5 and 4 - group B) were added by order of
appearance in the original parent2. This new rotation period was child1
with ws5; ws4; ws1; ws2; ws6 and. ws3:
Each row (worker) had a set of valid workstations. If during the OX
method, a workstation was shifted into a row where it was not valid, the
process searched for rows where this workstation could fit and made the
exchange. This process was a checkpoint to ensure the child generated
was a valid option.
3.4. Mutation
The mutation is the other operator used to generate the other 50% of
the new population. The method used was a variation of the bit string
mutation. The process comprised 3 steps and was done per column: (1)
random selection of rotation periods; (2) random selection of a work-
station for a given rotation period; (3) change of the workstation selected
for another in the same period of rotation, as long as it ensures compli-
ance with the qualification matrix.
Consider the example presented in Figure 6 and the qualification
matrix (Figure 1 Supplementary Material). The example shows a column
Figure 5. Example of the ordered crossover method applied to this problem.
From two parents (one rotation of a job rotation plan for each parent) a child
was created. The child was based on a variation of parent 2, which has received
the selection from parent 1, in the same positions. The genes that were now
repeated in the child (group A) are erased, and the ones that are not present in
the child will be added by the order they appear in parent 2. For those who
belong to the map, the shift of genes will go through a qualification check. The
red points indicate the checkpoint because of the workstation shift. Abbrevia-
tions: w –worker.
Figure 6. Mutation example. A rotation period was selected and would be mutated to generate a variation of the rotation period. Abbreviations; w –worker.
A. Assunç~
ao et al. Heliyon 8 (2022) e09396
8
of one of the selected job rotation plans. The column had randomly been
selected (step 1). Then one workstation was randomly selected (step 2).
After that, this workstation was shifted with workstations that would
follow the requirements. In this case, ws 3 from w1 was selected. The
possible workers to shift this workstation with were w3, w5 and w6,
because these have ws 3 on their qualifications, and w1 is able to perform
ws 3, ws 5 and ws 6. The shift workstation was then chosen randomly
from the valid group.
3.5. Closing conditions
When the closing condition is reach, the iterative process of the
genetic algorithm ends, and a result is returned. Two conditions must be
met: (1) "is the score of the best chromosome higher than the reference
value?" and (2) "is the number of iterations above 100?”. The first
condition guarantees that a job rotation plan with a referenced adequacy
was returned. The reference value was the mean score of the weekly job
Table 3. Ergonomic evaluation and risk factors characteristics. Risk factor scores for all categories of the EAWS. The colours on the Action Forces
section represent the type of force exerted: black - dynamic and static forces; dark grey - dynamic forces; light blue –static force; light grey - the risk
factor is not present. The unit %t indicates the percentage of time spent in that risk factor during 1 cycle time, and n represents the number of times
these risk factors appear in 1 cycle time.
Abbreviations: Ws –Workstation; NS –Neck and shoulder; ASL –At/Above shoulder level; AHL –Above head level; T-Trunk; B-Bent; SB-Strongly
bent; GA6 –Arm reach at 60%; GA8 –Arm reach at 80%; GA10 –Arm reach at 100%; MMH –Manual material handling; R –Repositioning; C –
Carrying; H –Holding; P –Pushing and Pulling; WB –Whole body force; HAF –Hand Arm Finger force; S - Score. Note that posture was evaluated
considering the percentage of time that an awkward posture was observed during the cycle time (approximately 79 s), as well as the static force for
the whole body and hand arm finger systems. The dynamic type of force was accessed according to the frequency of its presence in the cycle time. The
presence or absence of MMH in the workstation was used to classify this risk factor.
Table 4. Worker's versatility according to the workstations. The empty cells indicate that the worker does not have the competence to perform the respective
workstation.
WS1 WS2 WS3 WS4 WS5 WS6 WS7 WS8 WS9 WS10 WS11 WS12
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
Abbreviations: W –Worker; WS - Workstation.
A. Assunç~
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9
rotation plan designed by the team leaders for their URQ. The second
condition was meant to give the algorithm enough iterations to stabilize.
This value had to be experimentally calculated by running the algorithm
1000 times and extracting the value that ensures a good margin to let the
algorithm stabilize. When these two conditions are met, the algorithm
stops and outputs the best chromosome of the population. If the con-
ditions are not met, the algorithm is kept running to improve the
offspring.
4. Industrial case study
A full detailed example on how to the GA can be applied can be found
in the Supplementary Material. The GA was also tested in a real life-
environment, by being applied to a randomly selected team from the
assembly area of an automotive industry with 12 workers, 12 worksta-
tions, and under the responsibility of (hereafter) a team leader. All
workstations were close together and the standard rotation did not affect
the normal operation of the production line (since rotation periods
coincided with breaks). Although there was a standard job rotation at the
company (provided by the Team Leader), the choice of the workstations
was mostly based on empirical knowledge and experience.
In this study, the morning shift was considered. The working day was
composed of 8h, with a lunch break of 30 min, and two breaks of 7 min
each, before and after lunch. This translates into a mean network time of
466 min. Considering the network time, four working periods were
already established with the following relative distributions: (1) 22.6%;
(2) 30.7%; (3) 27.0%; and (4) 19.7%. Each worker performs 4 different
workstations during the working day, according to their qualification.
The versatility matrix was consulted to allocate workers to workstations
that they were able to perform autonomously.
4.1. Workstations and workers
The evaluations of the 12 workstations belonging to the team are
presented in Table 3. Most of the workstations were classified with me-
dium risk, one workstation was classified as no risk (ws10) and two
workstations were classified as high risk (ws3 and ws11).
The team's qualification matrix is given in Table 4. From the 12
workers, eight had full versatility, i.e., they can perform autonomously
all workstations, which was an advantage to the Team Leader.
4.2. Convergence of the algorithm
The fitness function guides the algorithm during the iterative pro-
cess and it is expected to improve all variables contributing to the
quality score. Therefore, the occupational exposure score should
decrease, and diversity and homogeneity scores should increase.
Figure 7 shows a higher improvement for diversity and homogeneity as
expected, but on the other hand, exposure did not change significantly
during the entire process. Regarding the quality score, the best job
rotation plan in the population over the iteration process was verified
as an improvement.
Figure 8 gives the evolution of the execution of the algorithm con-
cerning exposure, diversity, and homogeneity and reflects the capacity of
the algorithm to progressively generate better solutions by employing
simulated evolution techniques.
The algorithm reaches a stable solution around the 70th iteration.
4.3. Job rotation schedule obtained
The algorithm took 53 s to generate the proposition of a job rotation
plan for the problem proposed: 12 workers, 12 workstations, and 4
working periods. The working computer used an Intel i5 quad core
processor with 3.2 GHz, 8 GB of RAM and ran on a Linux Ubuntu 18.0.1
operative system.
The best solution obtained is presented in Figure 9. In this solution,
the allocation of workers to workstations satisfied the restrictions
imposed on the problem and tried to decrease the prolonged time
consumed by the same movement. During the working day, the workers
were not assigned to the same workstations and did not occupy two red
workstations. This happened mainly because diversity was highly pro-
moted over the iteration process, resulting in working sequences with
better diversity results. In the last 3 columns of Figure 9, the contribution
of each worker to the fitness function is given with the values of expo-
sure, diversity, and SWSQ.
The scores for the job rotation schedules obtained are presented in
Table 5. As expected, the first matrix had the worst MQ score compared
to the last matrix (2.02 and 2.44, respectively). This was due to the fact
that the first matrix had the worst set of occupational exposure and di-
versity scores, and these scores were not homogenous. The final score
had a better homogeneity score when compared to the initial score (1.84
and 1.74, respectively).
An improvement in the results during the iteration process resulted in
a better solution, i.e., in a better job rotation schedule for this specific
team.
A job rotation plan designed by a team leader is presented in
Figure 10.
For evaluation purposes, look at w1, w7, and w11. Besides the risk
level of the workstations, scores for the sequence evaluation are
Figure 7. Convergence of the algorithm considering exposure (orange), di-
versity (green), and homogeneity (red).
A. Assunç~
ao et al. Heliyon 8 (2022) e09396
10
Figure 8. Evolution of the fitness of the best individual throughout the generations, concerning diversity (A), exposure (B), and homogeneity (C).
Figure 9. Best scored job rotation schedule for the last iteration of the algorithm. Each cell is coloured considering the colour traffic light scheme used to classify the
risk of the workstation. Scores: Hom ¼1:84;SWSQ ¼1.98, MQ ¼2:44. Abbreviations: W –Worker; Rot –Rotation period; SWSQ - Shift working sequence quality.
A. Assunç~
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11
presented, namely the exposure, the diversity, and the sequence
quality score. The sequence of workstations attributed to w1was
medium levelled, except for the first workstation, which had a low-
risk level. This fact was verifiedbytheexposurescore,whichwas
0.09, close to 0, reflecting that this sequence was near to the best
possible sequence w1 could have. Nevertheless, the diversity score of
w1 was not as good (0.58). This shows how different the evaluation
made for the diversity was between the three workers. Regarding
w11, the scores were different. In this case, the sequence had two red-
labelled workstations, which increased the exposure score. On the
other hand, the diversity score was the same as for w1. This dem-
onstrates how different the measures of exposure and diversity were.
A sequence of workstations with low-levelled scores might be good in
terms of exposure, but might be bad in diversity, because it measures
different outcomes. For instance, w7 had the worst diversity score of
the team. This was a result of the two identical workstations at the
end of the shift, therefore compromising the diversity score at the last
transition.
When comparing the best results obtained by the GA for one day
(Table 5), with a full week planned by the team leader (Table 6), we
found that the algorithm provided better results in all the parameters,
including homogeneity, diversity, and matrix quality, regardless of the
day analysed.
5. Discussion
The main purposes of this study were: (1) to develop a formulation
based on objective ergonomic indicators and workers qualifications to
generate job rotation schedules based on three main criteria: diversity,
exposure and homogeneity for an assembly line of the automotive industry
solved by means of a GA; and (2) provide an industrial case study where
the GA was tested and applied to a randomly selected team from the
automotive assembly area in a real life-environment, in order to compare
the performance of the job rotation plan formulated by the new GA versus
that of the team leader. The algorithm proposed showed a high diversity
sequence during working hours, a lower overall exposure, and reassured
homogeneity to balance the rotation within each team. These results also
demonstrate that the time spent by the team leader organizing the weekly
schedule was considerably higher when compared with the time that the
algorithm took to deliver a job rotation plan for a week.
A job rotation plan is an essential tool to the automotive industry and
its aim is to facilitate not only the work of the team leader but also, in the
long run, to reduce the risk associated with musculoskeletal injuries by
Table 5. Results for job rotation schedules obtained in the first and last iteration.
SWSQ Hom MQ
Best scored job rotation for 1
st
iteration 1.80 1.74 2.23
Worst scored job rotation for 1
st
iteration 1.63 1.59 2.02
Best scored job rotation for the last iteration 1.98 1.84 2.44
Abbreviations: SWSQ - Mean shift working sequence quality; Hom –Homoge-
neity; MQ –Matrix quality.
Figure 10. Example of a job rotation plan designed by a team leader. Abbreviations: W –worker; Rot –Rotation; SWSQ –Shift working sequence quality.
Table 6. Scores for shift working sequence quality, homogeneity, and matrix
quality for job rotation schedules for a week designed by a team leader. These
schedules were scored with the formulation designed.
Team Leader Matrix SWSQ Hom MQ
Day 1 1.72 1.77 2.16
Day 2 1.64 1.71 2.07
Day 3 1.72 1.73 2.15
Day 4 1.76 1.74 2.20
Day 5 1.69 1.70 2.12
Abbreviations: SWSQ –Shift working sequence quality; Hom –homogeneity; MQ
–Matrix quality.
A. Assunç~
ao et al. Heliyon 8 (2022) e09396
12
increasing diversity, decreasing exposure, and ensuring homogeneity.
This investigation, through its resources and departments, namely the
industrial engineering, the ergonomics, and the occupational health
teams, built a job rotation plan using a GA. The algorithm had a good
computational performance and generated a solution that took less time
building a rotation plan, when compared with that of the team leaders.
More specifically, it took the algorithm 53 s to generate a job rotation
plan, where usually team leaders spend approximately 2–3 h. Thus, the
use of GA has the potential to spare the team leaders time for allocation to
other important tasks. The reduction in the time to generate a job rota-
tion plan is in accordance with other studies that also used this type of
algorithms (Asensio-Cuesta et al., 2012a;Diego-Mas, 2020;Diego-Mas
et al., 2009;Hochd€
orffer et al., 2018a).
Our results also suggest that the repetition of the same workstation,
followed by rotation periods, although allowed, was not promoted by the
method. This is a result of the algorithm giving higher relevance to the
diversity score. This score has a wide progression and is the major factor
of convergence. It also demonstrates that the algorithm can improve the
conditions from the first iteration to the last and give a result that reflects
the need for increased diversity and homogeneity, and decreased expo-
sure. Diego et al. applied a GA in an automotive parts supplier assembly
plant considering the previous rotations, trying to minimize the perfor-
mance with the same body region, but not quantifying it, as we did with
diversity (Diego-Mas et al., 2009). The option to favour diversity was
supported by the physiologic pathways of musculoskeletal health stating
that posture and load variation are beneficial (Mathiassen, 2006). One of
the strengths and a novelty of this study is the fact that it included the
calculation of diversity of force and MMH along with posture, which
provides more risk factors being embedded by the GA, whereas, the
majority of the algorithms presented in the literature relied on posture
and movement, ergonomic score (from an evaluation method, e.g. OCRA,
EAWS), learning skills, and others (Padula et al., 2017).
As far as exposure is concerned, it is one of the parameters contrib-
uting to the fitness function, but with less weight than diversity. In the
literature, the cumulative exposure, with different criteria's used between
studies, is one of the key factors to evaluate the effectiveness of the job
rotation schedule (Asensio-Cuesta et al., 2012a;Diego-Mas et al., 2009;
Hochd€
orffer et al., 2018b;Rajabalipour Cheshmehgaz et al., 2012;Xu
et al., 2012). In this study, exposure did not change significantly because
its weight was very low when compared to diversity. The choice to
promote diversity over occupational exposure reflects the idea to pro-
mote an opportunity of relaxing overloaded motor units by having
workstations that differ in all the risk factors considered (Mathiassen,
2006). Besides, the proposed formulation also considered homogeneity
as a key feature, allowing workers to have a similar exposure during the
shift. The team selected showed characteristics of versatility that were
reflected on the matrix of the work team, where most of the workers were
able to perform the majority of the workstations with autonomy. This is
beneficial for workers since they have the possibility to improve their
diversity and reduce exposure during the shift. A previous study has
considered the balance between the workers as a contribution to the
target function (Diego-Mas et al., 2009).
Even though there is no consensus in the literature about the
effectiveness of this measure in the prevention of WRMSD's (Comper
and Padula, 2014), several approaches have been implemented,
considering different criteria (Padula et al., 2017). The use of GA to
generate job rotation plans in the industry is a common option due to
its combinatorial nature and satisfactory results (Asensio-Cuesta et al.,
2012a;Diego-Mas et al., 2009). The decision to use a GA to solve the
combinatorial problem in designing the job rotation plan in this study
was due to it already being proven to be successfully used similar
context (Asensio-Cuesta et al., 2012a;Diego-Mas et al., 2009). The
methods that were applied for selection, crossover, and mutation are
well known and were used because these were found to be adequate
for this problem (Moscato, 1989). The mutation rate, in this case, was
higher than what is usually found in the literature, but better
convergence results were reached with a higher mutation rate. The job
rotation schedules generated by the GA provided better scores than
the ones developed by the team leaders in homogeneity, diversity, and
matrix quality.
Any tool developed to assist in work organization must be flexible and
appropriate to the specific requirements of each production process.
Nevertheless, the GA can be implemented in the rest of the assembly area,
due to the similarity of processes. In the future, transfers to production
areas can be made with the optimization of their specificities and char-
acteristics. It's also important to highlight that the work organization
variables, such as the duration and number of working periods and the,
duration, and frequency of breaks during the shift, have not been
changed. However, it will be interesting to compare the results of the
fitness function of the two remaining shifts, late evening, and night, since
the working periods have different durations. This comparison could give
different perspectives to make a more suitable duration and distribution
of working periods throughout the shift at the organization level. Also,
recent publications suggest that motivational and preferential aspects
within the job rotation could also be integrated (Asensio-Cuesta et al.,
2019).
Despite presenting a case study with 12 workers with promising re-
sults, this formulation lacks a broader application and validation in an
ecological context in order, to further understand its effectiveness in a
larger scale sample and musculoskeletal symptom prevention. In an era
of technological development, the use of direct quantitative assessment
of risk factors in the working field, such as those acquired through motion
sensors, would enable the proposed formulation to have more reliable
risk scores than the ones globally provided by the EAWS.
6. Conclusion
The formulation developed in this study generated job rotation
schedules considering constraints present in the assembly line of the
automotive industry. This formulation has been proven to be a reliable
solution to design job rotation plans, increasing diversity, decreasing
exposure, and balancing homogeneity for the team. The solution pre-
sented in this study combined the information from workers in terms of
qualification and the requirements of the workstations to generate and
evaluate solutions looking for the best sequences. Moreover, this
approach helped the team leaders, in a time-efficient manner, to decide
which job rotation plan would be better suited when considering all the
constraints, his experience and his knowledge about the workstations
and his team.
From the company point of view, this approach could additionally
be a relevant tool for data generation, which could be crucial for
designing new production systems and to manage investments aimed
at improving productivity and promote musculoskeletal health at
work. Nonetheless, future research is warranted to analyse the effec-
tiveness of the job rotation plans generated by this type of formula-
tions with those provided by the team leaders, while considering a
larger sample, how the plans impact the results of diversity, exposure,
and homogeneity, and how they translate into the reduction of the
prevalence of WRMSD.
Declarations
Author contribution statement
Ana Assunç~
ao and Jo~
ao Rodrigues: Conceived and designed the ex-
periments; Performed the experiments; Analyzed and interpreted the
data; Contributed reagents, materials, analysis tools or data; Wrote the
paper.
Nafiseh Mollaei and Carlos Fuj~
ao: Conceived and designed the ex-
periments; Analyzed and interpreted the data; the paper.
Daniel Os
orio: Contributed reagents, materials, analysis tools or data.
Ant
onio P. Veloso: Analyzed and interpreted the data.
A. Assunç~
ao et al. Heliyon 8 (2022) e09396
13
Hugo Gamboa: Performed the experiments; Analyzed and interpreted
the data.
Filomena Carnide: Conceived and designed the experiments;
Analyzed and interpreted the data; Wrote the paper.
Funding statement
Ana Assunç~
ao was supported by Fundaç~
ao para a Ci^
encia e Tecno-
logia [SFRH/BDE/102750/2014].
Nafiseh Mollaei was supported by Fundaç~
ao para a Ci^
encia e Tecno-
logia [PD/BDE/142973/2018].
Jo~
ao Rodrigues was supported by Fundaç~
ao para a Ci^
encia e Tecno-
logia [PD/BDE/142816/2018].
This work was partly supported by Fundaç~
ao para a Ci^
encia e Tec-
nologia (CIPER - Centro Interdisciplinar para o Estudo da Performance
Humana (Unit 447) [UIDB/00447/2020].
Data availability statement
Data will be made available on request.
Declaration of interests statement
The authors declare no competing interests.
Additional information
Supplementary content related to this article has been published
online at https://doi.org/10.1016/j.heliyon.2022.e09396.
Acknowledgements
The authors are grateful to all participants for their time and effort.
References
Aryanezhad, M.B., Kheirkhah, A.S., Deljoo, V., Mirzapour Al-E-Hashem, S.M.J., 2009.
Designing safe job rotation schedules based upon workers’skills. Int. J. Adv. Manuf.
Technol.
Asensio-Cuesta, S., Diego-Mas, J.A., Can
os-Dar
os, L., Andr
es-Romano, C., 2012a.
A genetic algorithm for the design of job rotation schedules considering ergonomic
and competence criteria. Int. J. Adv. Manuf. Technol.
Asensio-Cuesta, S., Diego-Mas, J.A., Cremades-Oliver, L.V., Gonz
alez-Cruz, M.C., 2012b.
A method to design job rotation schedules to prevent work-related musculoskeletal
disorders in repetitive work. Int. J. Prod. Res. 50, 7467–7478.
Asensio-Cuesta, S., García-G
omez, J.M., Poza-Luj
an, J.L., Conejero, J.A., 2019. A game-
theory method to design job rotation schedules to prevent musculoskeletal disorders
based on workers’preferences and competencies. Int. J. Environ. Res. Publ. Health
16, 4666.
Bao, S., 2015. Mechanical stress. In: Handbook of Clinical Neurology. Elsevier B.V.,
pp. 367–396
Bhasin, H., Behal, G., Aggarwal, N., Saini, R.K., Choudhary, S., 2016. On the applicability
of diploid genetic algorithms in dynamic environments. Soft Comput. 20, 3403–3410.
Boenzi, F., Digiesi, S., Mossa, G., Mummolo, G., Romano, V.A., 2013. Optimal break and
job rotation schedules of high repetitive –low load manual tasks in assembly lines: an
OCRA –based approach. IFAC Proc. 46, 1896–1901.
Carnahan, B.J., Redfern, M.S., Norman, B., 2000. Designing safe job rotation schedules
using optimization and heuristic search. Ergonomics 43, 543–560.
Comper, M.L.C., Padula, R.S., 2014. The effectiveness of job rotation to prevent work-
related musculoskeletal disorders: protocol of a cluster randomized clinical trial. BMC
Muscoskel. Disord.
De Kok, J., Vroonhof, P., Snijders, J., Roullis, G., Clarke, M., Peereboom, K., Dorst, P. van.,
Isusi, I., 2019. Work-related Musculoskeletal Disorders : Prevalence, Costs and
Demographics in the EU. European Agency for Safety and Health at Work.
Diego-Mas, J.A., 2020. Designing cyclic job rotations to reduce the exposure to
ergonomics risk factors. Int. J. Environ. Res. Publ. Health 17, 1073.
Diego-Mas, J.A., Asensio-Cuesta, S., Sanchez-Romero, M.A., Artacho-Ramirez, M.A.,
2009. A multi-criteria genetic algorithm for the generation of job rotation schedules.
Int. J. Ind. Ergon. 39, 23–33.
Digiesi, S., Facchini, F., Mossa, G., Mummolo, G., 2018. Minimizing and balancing
ergonomic risk of workers of an assembly line by job rotation: a MINLP Model. Int. J.
Ind. Eng. Manag. 9, 129–138.
Durand, M.J., Corbi
ere, M., Coutu, M.F., Reinharz, D., Albert, V., 2014. A Review of Best
Work-Absence Management and Return-To-Work Practices for Workers with
Musculoskeletal or Common Mental Disorders. Work.
Goldberg, D.E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning,
first ed. Addison-Wesley Longman Publishing Co., Inc., USA.
Hochd€
orffer, J., Hedler, M., Lanza, G., 2018a. Staff scheduling in job rotation
environments considering ergonomic aspects and preservation of qualifications.
J. Manuf. Syst.
Hochd€
orffer, J., Hedler, M., Lanza, G., 2018b. Staff scheduling in job rotation
environments considering ergonomic aspects and preservation of qualifications.
J. Manuf. Syst. 46, 103–114.
Jorgensen, M., Davis, K., Kotowski, S., Aedla, P., Dunning, K., 2005. Characteristics of job
rotation in the Midwest US manufacturing sector. Ergonomics 48, 1721–1733.
Mathiassen, S.E., 2006. Diversity and variation in biomechanical exposure: what is it, and
why would we like to know? Appl. Ergon. 37, 419–427.
McDonald, T., Ellis, K.P., Van Aken, E.M., Patrick Koelling, C., 2009. Development and
application of a worker assignment model to evaluate a lean manufacturing cell. Int.
J. Prod. Res. 47, 2427–2447.
Moscato, P., 1989. On genetic crossover operators for relative order preservation. C3P
Rep.
Mossa, G., Boenzi, F., Digiesi, S., Mummolo, G., Romano, V.A., 2016. Productivity and
ergonomic risk in human based production systems: a job-rotation scheduling model.
Int. J. Prod. Econ.
Padula, R.S., Comper, M.L.C., Sparer, E.H., Dennerlein, J.T., 2017. Job rotation designed
to prevent musculoskeletal disorders and control risk in manufacturing industries: a
systematic review. Appl. Ergon.
Rajabalipour Cheshmehgaz, H., Haron, H., Kazemipour, F., Desa, M.I., 2012.
Accumulated risk of body postures in assembly line balancing problem and modeling
through a multi-criteria fuzzy-genetic algorithm. Comput. Ind. Eng. 63, 503–512.
Rodriguez, A.C., Barrero, L.H., 2017. Job rotation: effects on muscular activity variability.
Appl. Ergon. 60, 83–92.
Schaub, K., Caragnano, G., Britzke, B., Bruder, R., 2013. The European assembly
Worksheet. Theor. Issues Ergon. Sci. 14, 616–639.
Sebbag, E., Felten, R., Sagez, F., Sibilia, J., Devilliers, H., Arnaud, L., 2019. The world-
wide burden of musculoskeletal diseases: a systematic analysis of the World Health
Organization Burden of Diseases Database. Ann. Rheum. Dis. annrheumdis-2019.
Xu, Z., Ko, J., Cochran, D.J., Jung, M.C., 2012. Design of assembly lines with the
concurrent consideration of productivity and upper extremity musculoskeletal
disorders using linear models. Comput. Ind. Eng.
Yung, M., Mathiassen, S.E., Wells, R.P., 2012. Variation of force amplitude and its effects
on local fatigue. Eur. J. Appl. Physiol.
A. Assunç~
ao et al. Heliyon 8 (2022) e09396
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