Integration of Sustainable Production Criteria into Production
Scheduling: A Systematic Search and a Critical Review
María Sol Cavallieri
1 a
, Elisabeth Viles
1 b
and Jairo R. Montoya-Torres
2 c
Department of Industrial Organisation, TECNUN School of Engineering, Universidad de Navarra, San Sebastián, Spain
School of Engineering, Universidad de La Sabana, Chia, Cundinamarca, Colombia
Keywords: Sustainable Production, Scheduling, Literature Review.
Abstract: Production scheduling plays a pivotal role in shaping and optimizing production processes to promote
sustainability in manufacturing companies. Understanding how current studies consider sustainable
production criteria in scheduling objectives can help companies transition from a reactive to a proactive
production mode. This paper presents a systematic and critical analysis of 120 articles to examine the extent
to which sustainable production criteria have been applied to scheduling problems in manufacturing systems.
The analysis categorizes articles based on the type of scheduling problem, problem formulation, resolution
method, and sustainability aspects considered, while also tracking the evolution of each sustainability
indicator to identify trends. The study reveals the use of diverse sustainability indicators in production
scheduling. Indicators such as "Makespan" and "Energy consumption" are prevalent, while social indicators
related to employee well-being and safety are still emerging and rarely considered. Notable gaps identified in
this review include the absence of real-world applications, unclear criteria for indicator selection, and limited
holistic assessments linking production improvements to overall sustainability. The review emphasizes the
need for practical and strategic approaches, serving as a guide for the manufacturing sector and informing
future research directions.
In recent years, there has been significant attention to
the concept of sustainable production in both
academic and business alike. This increased focus is
propelled by the forces of economic development,
social transformation, and increased concerns
regarding environmental degradation (Lu et al.,
2017). In striving for a delicate harmony between
economic, social, and environmental elements within
production, a holistic approach is crucial. Within the
context of a circular economy and Industry 4.0, Viles
et al. (2022) delineated ten pivotal principles that
define sustainable production for manufacturing
firms. This holistic perspective recognizes the
interdependence of these pillars and ensures the long-
term viability and sustainability of manufacturing
systems (Abedini et al., 2020).
Within the realm of the Sustainable Production
paradigm, production planning and scheduling play a
pivotal role in shaping and optimizing production
processes (Khaled et al., 2022). It serves as a key
driver in operational decision-making, enabling
manufacturers to optimize resources, enhance
efficiency, and minimize waste, among others. The
integration of sustainable production principles into
production planning and scheduling holds the
potential to advance the Triple Bottom Line
objectives of economic viability, social equity, and
environmental stewardship (Lu et al., 2017).
This paper presents a systematic and critical
review of academic literature aimed at examining the
extent to which sustainable production criteria have
been applied to address scheduling problems in
manufacturing systems.
The importance of conducting this review stems
from the need for manufacturing companies to
transition from a reactive approach to a proactive
Cavallieri, M., Viles, E. and Montoya-Torres, J.
Integration of Sustainable Production Criteria into Production Scheduling: A Systematic Search and a Critical Review.
DOI: 10.5220/0012306000003639
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems (ICORES 2024), pages 58-69
ISBN: 978-989-758-681-1; ISSN: 2184-4372
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
mode of sustainable production, recognizing that the
latter is essential for the long-term success of
businesses (Piwowar-Sulej, 2022). Through an
exploration and synthesis of existing knowledge, this
review aims to provide manufacturing companies
with valuable insights into the integration of
sustainability principles within the production
scheduling process.
While a few previous studies have examined
sustainability aspects concerning production
scheduling there remains a significant gap in providing
a comprehensive critical review of existing research
(Giret et al., 2015). In this comprehensive study, we
aim to bridge existing gaps by conducting a thorough
examination of 120 articles that incorporate
sustainability criteria into production scheduling.
Our objective is to deepen our understanding of
sustainable production in manufacturing,
encompassing economic, social, and environmental
dimensions. We will also examine recent trends,
evaluate commonly used sustainability indicators,
and analyse prevalent challenges in the field. Through
this critical assessment, we aim to provide valuable
insights for future research and practical applications,
making this review a valuable resource for
manufacturing companies seeking state-of-the-art
sustainable production practices in production
The remainder of the paper follows this structure.
Section 2 presents a background on the research topic,
while Section 3 outlines the review methodology.
Section 4 presents the results of the literature review.
Section 5 delves into the discussion, and Section 6
provides some concluding remarks.
2.1 Previous Reviews
In recent years, Sustainable Production has emerged
as a pivotal consideration in the domain of
manufacturing systems. It encompasses a holistic
approach aimed at achieving economic,
environmental, and social sustainability in
manufacturing processes (Abedini et al., 2020). To
realize the ideals of sustainable production,
manufacturers must address numerous challenges,
including those related to production scheduling.
Production scheduling plays a central role in
manufacturing operations (Khaled et al., 2022). It
involves the allocation of resources, such as
machines, labour, and materials, to tasks or jobs over
time to optimize various objectives, such as meeting
customer demands, minimizing production costs, and
maximizing resource utilization (Adhi et al., 2018).
However, it is crucial to recognize that production
scheduling problems are inherently complex, and
most of them are classified as NP-hard. This
complexity implies that is not possible to find optimal
solutions for large-sized datasets in reasonable
computational time (Adhi et al., 2018). Such
complexity arises due to the combinatorial nature of
scheduling, where numerous variables, constraints,
and objectives must be considered simultaneously.
To tackle the complexity of production
scheduling, researchers have developed a range of
resolution algorithms. These algorithms aim to find
near-optimal solutions within reasonable
computational time. The most used approaches
include heuristic and meta-heuristic algorithms. The
former are problem-solving strategies that do not
guarantee optimal solutions but provide good-quality
solutions quickly, while the latter are higher-level
strategies that explore the solution space efficiently
and can be adapted to various scheduling problems
(Janga Reddy & Nagesh Kumar, 2020).
The link between Sustainable Production and
production scheduling is evident when considering
the optimization of manufacturing processes with
sustainability objectives in mind. Sustainable
production scheduling aims to incorporate principles
of sustainability into the scheduling decisions.
Previous research in sustainable production and
production scheduling has been shaped by the work
of Giret et al. (2015), Biel & Glock (2016), Khaled et
al. (2022), Renna & Materi (2021), and Akbar &
Irohara (2018). These authors conducted extensive
literature reviews to examine how researchers are
integrating sustainability aspects into production
scheduling. Table 1 summarizes these reviews by
presenting the focus of the study, the sustainability
aspects considered, the covered period, and the
number of articles analysed.
Biel & Glock (2016) focus primarily on energy
efficiency within sustainable production planning. The
authors point out the surge in research in Energy-
Efficient Production Planning and highlight the need to
better integrate existing modelling approaches within
this emerging field. Giret et al. (2015) point out the
imbalances currently existing in research efforts to
address all three dimensions of sustainability. As
sustainability encompasses economic, social, and
environmental issues, these authors noted a
predominant focus on a specific input, namely energy.
Furthermore, they underscore the neglect of real-time
responsiveness in manufacturing operations, which is
often overlooked in sustainable production planning.
Integration of Sustainable Production Criteria into Production Scheduling: A Systematic Search and a Critical Review
Table 1: Previous literature reviews.
aspects covered
Nº of reviewed
Period of review
(Giret et al.,
Sustainable manufacturing operations
Economic and
(Biel &
Glock, 2016)
Decision support models for energy-
efficient production planning
Economic and
Up to 2015
(Akbar &
Scheduling for sustainable
and Social
Up to 2018
(Renna &
Materi, 2021)
Energy efficiency and sustainability in
manufacturing systems
Economic and
2007- June 2021
(Khaled et al.,
Sustainability of Production Planning
and Social
Continuing with energy-dominant studies, Renna
& Materi (2021) provide an overview of the
integration of renewable energy sources into
manufacturing systems. They categorize the studies
based on manufacturing system typology and energy-
saving policies and discuss the main approaches
proposed in the short-listed papers. The analysis helps
shed light on the diverse strategies and methodologies
applied in the field of sustainable production
The study conducted by Khaled et al. (2022) takes
a comprehensive approach to sustainable production
planning, emphasizing the consideration of multiple
sustainability indicators. Their study stands out for its
broader scope by considering all three sustainability
aspects. They recognize the need for future research
to address various optimization methods and the
challenge of balancing conflicting sustainability
Similarly, the literature review conducted by
Akbar & Irohara (2018) delves into the economic,
environmental, and social aspects of sustainable
production. It identifies sustainability indicators,
assesses production systems, and outlines future
directions. As some of the main findings, the authors
conclude that the integration of these factors into
scheduling models yields significant sustainability
improvements and that the use of sustainable
indicators empowers manufacturers to track progress
effectively. Additionally, the review highlights the
need for further research, especially in complex
manufacturing systems, emphasizing sustainability's
crucial role in shaping future scheduling practices.
2.2 Paper Positioning
While both Khaled et al. (2022) and Akbar & Irohara
(2018) explored the social aspects of sustainable
production scheduling, their studies have inherent
limitations. The work presented by Akbar & Irohara
(2018), dating back to 2018, may not fully encompass
the latest developments in integrating sustainability.
Indeed, as shown later in this paper, there has been a
surge in contributions since 2019. Likewise, Khaled
et al. (2022) made commendable efforts; however,
their study had a narrower scope, encompassing only
45 articles and identifying 8 indicators. Furthermore,
while Akbar provides a comparison between the
indicators that are being used together, how these
indicators are evolving is not considered.
Additionally, it's noteworthy that none of the
previously mentioned authors delve into the critical
aspect of applying indicator selection criteria in real-
world scenarios, which is needed for determining
whether the ongoing improvements align with the
overarching goal of enhancing the company's
These limitations highlight the necessity for a
more comprehensive understanding of sustainability's
role in production scheduling. The research question,
'How have the criteria of sustainable production been
employed to solve scheduling problems in
manufacturing systems?' remains pertinent and calls
for further investigation. Our research seeks to
identify the main gaps in this knowledge area by
exploring sustainable production scheduling. We
analyse 120 articles and identify recent trends,
common challenges, and evolving strategies by
offering a comprehensive view of sustainability in
production scheduling.
This study employed a systematic literature review
methodology, following the PRISMA guidelines, to
ICORES 2024 - 13th International Conference on Operations Research and Enterprise Systems
Figure 1: Literature review.
Figure 2: Employed keywords.
Figure 3: Inclusion and exclusion criteria.
address the research question mentioned previously.
This methodology is widely recognized in research
for its ability to provide insights into prior work,
identify research gaps, synthesize related studies, and
enable hypothesis testing, theory development, and
critical evaluation of existing research (Xiao &
Watson, 2019).
After the systematic literature review was
conducted, a critical analysis was performed to
address the research question. The objective of this
analysis is to thoroughly examine the collected
information, extracting insights and perspectives
from the literature to facilitate the development of
fresh theoretical constructs and novel viewpoints.
The five phases proposed by Tranfield et al.
(2003) to conduct the review were used: 1) question
formulation, 2) locating studies 3) study selection and
evaluations, 4) analysis and synthesis, and 5)
reporting and using the results. Figure 1 shows a
scheme of what each phase covers and in which
section of this paper each is developed.
Integration of Sustainable Production Criteria into Production Scheduling: A Systematic Search and a Critical Review
To select relevant studies, inclusion and exclusion
criteria were defined. The search was conducted
using the Web of Science and Scopus databases,
limiting the search to articles published in English
between 2000 and July 2023. Since the aim of the
study is to analyse how manufacturing companies
design or schedule their production processes through
optimization approaches, the search strategy involved
search strings using a combination of the groups of
keywords given in Figure 2. Throughout the process,
the inclusion and exclusion criteria were considered,
as explained in Figure 3. The search in both databases
initially yielded 942 references. After removing 308
duplicates, 634 studies were considered. These
studies were then screened against the title and
abstract based on their relevance to the research
question; this resulted in the exclusion of 408 studies.
The remaining 226 studies were assessed for full-text
eligibility. Out of these, 69 studies were excluded
because they did not meet the study design criteria.
Thus, a total of 157 studies were finally short-listed
for further analysis and classification. Out of the
studies included, 120 were specifically scheduling
problems while the remaining 37 studies were
classified as frameworks/theoretical studies or other
studies, such as problems related to layout
configurations, or process design, among others.
Finally, data extraction was conducted using the
Covidence software, facilitating the systematic
collection of relevant information from the selected
studies. This software streamlined the process of
managing and organizing the extracted data, ensuring
accuracy and consistency.
In this section, the research question formulated
previously is addressed through an analysis of the
short-listed papers. General information about the
included studies is presented first for context,
followed by a review of the sustainability indicators
considered in the scheduling problems.
4.1 Study Overview
In Figure 4, a notable trend is observed concerning
articles related to sustainable production within the
manufacturing sector, specifically focusing on
production scheduling. While scheduling problems
first appeared in 2008, it wasn't until 2013 that they
began to gain significant momentum. However, it
wasn't until 2019 that a substantial surge in published
articles became evident. In 2018, the first article
considering all sustainability dimensions was
published and only a few have been published since
Figure 4: Evolution of publications and sustainability
aspects over the years.
Figure 5: Publishing journals over the years.
Articles considering economic and environmental
aspects combined are predominant, which indicates
that research is still needed in this area.
As for the journals in which these articles were
published, a striking diversity was noted. 58 articles
have been published across five distinct journals, as
shown in Figure 5. Importantly, this accounts for
nearly 48% of the total short-listed articles,
underscoring the wide array of publication outlets
chosen by researchers in this field.
Concerning the sectors that are most advanced in
this theme, the metal sector and the engineering sector
are being prominently highlighted. However, the
majority (65%) of the studies were not specific in
terms of the sector, either being based on literature
reviews or having the problem presented in a general
manner without delving into the specific sector of the
production process. Figure 6 displays the distribution
of sectors in the remaining articles.
Directed toward the types of scheduling problems
that are most studied, job shop and flow shop
problems are brought to the forefront (see Figure 7).
The third-largest group corresponds to the category
2008 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Economic Economic; Environmental Economic; Social; Environmental Environmental Social
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Journal of Cleaner Production International Journal of Production Research
Sustainability Computers & Industrial Engineering
IEEE Access Others
ICORES 2024 - 13th International Conference on Operations Research and Enterprise Systems
"Other" indicating studies where the scheduling
problem is either not identified or the type of process
does not fit into the presented categories.
Figure 6: Distribution of sectors.
Figure 7: Types of scheduling problems.
Figure 8 provides a classification of the different
approaches used to formulate and solve the scheduling
problems. Among the studies focusing on a single-
objective (SO) problem, they were classified by exact
or approximate resolution method. Meanwhile, for
studies addressing multi-objective (MO) problems,
they can be classified into three distinct types. Type 1
(T1) studies involve formulating a mathematical
problem and subsequently solving it. Typically, they
employ methods such as epsilon-constrained
optimization, goal programming, or solver tools to
obtain an exact solution. Type 2 (T2) studies formulate
the mathematical model of the problem but propose a
resolution method. Typically, due to the complexity of
these problems, the resolution methods involve
heuristics or metaheuristics to find solutions. Finally,
Type 3 (T3) are studies that, without explicitly
formulating the mathematical model, suggest a
resolution method. These methods also tend to rely on
heuristics or metaheuristics for problem-solving.
The heuristics and metaheuristics commonly used
are the Genetic Algorithm and its variations (36
studies), Particle Swarm Optimization and its
variations (7 studies), and Simulated Annealing (4).
However, there is a vast number of different methods
being used, as well as modifications to those methods.
This indicates that research related to sustainable
scheduling is focused on improving resolution
Figure 9 showcases how the optimization
problems are being covered regarding the
sustainability aspects that are addressed. In a broad
overview, it becomes apparent that the multi-
objective approach is prevalent, along with a
predominant use of resolution methods categorized as
Type 2 and Type 3, which can be easily justified by
the NP-harness of most scheduling problems.
Specifically, those problems that address two or more
sustainability aspects are mainly formulated as multi-
objective problems. On the other hand, in the context
of single-objective approaches, the predominant
technique for resolution is through approximate
Figure 8: Classification of optimization problems.
Figure 9: Sustainability aspects and types of problems.
0 5 10 15 20 25 30 35 40
Flow shop
Job shop
Parallel machine
Single machine
SO: Single-objective
AM: Approximation methods
EM: Exact methods
MO: Multi-objective
T1: Mathematical model
formulation and resolution
T2: Mathematical model
formulation and proposal of
resolution method
T3: Proposal of resolution
Integration of Sustainable Production Criteria into Production Scheduling: A Systematic Search and a Critical Review
4.2 Sustainable Production Criteria
Included in Scheduling Problems
To reveal how sustainable production criteria are
integrated into scheduling problems, a categorization
process was carried out on the diverse indicators
found in the literature. These indicators, which often
measured similar aspects, were grouped. For instance,
indicators related to production time, such as
"Makespan," "Completion time," and others, were
consolidated and labelled as "Makespan" for analysis.
Similarly, environmental indicators concerning waste
were merged under the label "Waste" and social
indicators like "Training" and "Personnel skills" were
merged as "Skills and training." This categorization
approach was adopted to ensure a more organized and
accessible presentation of the results, which can be
found in the Appendix.
4.2.1 Evolution of Sustainability Indicators
in Scheduling Problems
Figure 10 shows how indicators related to
sustainability in scheduling problems have changed
over time. These indicators are divided into
economic, environmental, and social categories,
represented by blue, green, and orange dots next to
their names. It is essential to note that this review
exclusively focuses on studies related to
sustainability, excluding those solely centred on one
of the dimensions (e.g., only economic factors).
The order in which the indicators are listed
corresponds to their chronological appearance in the
literature. The ones listed at the beginning of the list
are those that emerged earliest, whereas those at the
end of the figure represent the most recent additions.
In the early stages, economic and environmental
indicators were primarily used. Although the first
article on this topic dates to 2008, it was only after
2013 that the gradual integration of environmental
indicators into scheduling problems began to take
The graph illustrates that "Energy consumption"
and “Makespan” saw a significant increase in their
utilization between 2019 and 2021. Additionally,
other commonly used economic indicators included
“Tardiness” and “Operating cost,” while within the
environmental indicators’ category, "GHG
Figure 10: Evolution of indicator utilization.
ICORES 2024 - 13th International Conference on Operations Research and Enterprise Systems
emissions" were also frequently employed. For
instance, “Material usedmade its debut in 2016 but
has not been widely used. Similarly, “Fresh water
consumption,” introduced in 2019, has been
employed sparingly, as has “Waste”, which has been
used only three times since 2019. Furthermore, recent
years have seen a shift toward considering supply
chains, as evidenced by the emergence of the
indicator “Supplier selection based on environmental
Turning to the social aspect, it is still in the early
stages of exploration. Before 2018, no social
indicators were considered. The first social indicator,
"Working hours/Productivity" was introduced and
has remained relevant until 2023. However, in 2019,
a broader range of social indicators began to be
considered, indicating an increasing interest in this
dimension. In 2019, indicators such as "Working
conditions" and “Customer complaints and returns”
emerged. Although the focus on employee well-being
is gradually increasing, it has not yet gained
significant momentum, with indicators like “Skills
and training” mentioned only once in 2020 and again
in 2023, and “Social benefits” and “Lost workday
injury and illness case rate” each appearing once in
4.2.2 Integration of Sustainability Indicators
in Scheduling Problems
Although the use of indicators is important to analyse,
when it comes to analysing sustainability, the focus
should be placed on the integration of indicators. To
gather this information, a 3D Bubble Chart was
created (see Figure 11). Each bubble within the figure
signifies a unique combination of indicators
employed across all the studies, with the size of the
bubble denoting the frequency of its utilization. The
indicators themselves are marked with numbers, and
a color-coded system has been implemented to
enhance clarity in discerning these combinations.
Specifically, bubbles in orange, green, and blue
correspond to the exclusive utilization of social,
environmental, and economic indicators,
respectively. Meanwhile, the grey ones signify a
fusion of environmental and economic indicators, and
the red bubbles denote the holistic incorporation of all
three aspects.
Notably, the prevalence of studies solely
addressing one facet of sustainability is relatively
low. This observation underscores the importance of
adopting a multifaceted approach when addressing
sustainability concerns. Regarding the combinations
of indicators, it becomes evident that the grey spheres
Figure 11: Integration of sustainability indicators.
dominate both in terms of abundance and size. This
suggests that the most advanced and thoroughly
integrated aspects of sustainability often pertain to
economic and environmental considerations. More
specifically, the most frequently employed
combination of indicators comprises "Energy
consumption" and "Makespan." Additionally,
"Energy consumption" is commonly paired with
"Operating cost" and "Tardiness." In contrast, the red
bubbles, symbolizing the integration of all three
sustainability aspects, are noticeably smaller in size
when compared to their grey counterparts. This
distinction underscores the relative infrequency of
such comprehensive sustainability approaches within
the analysed studies. The largest among the red
bubbles signifies the integration of "Makespan",
"Energy consumption" and "Working
hours/productivity." Furthermore, "Makespan"
Integration of Sustainable Production Criteria into Production Scheduling: A Systematic Search and a Critical Review
appears in three out of the four subsequent red
bubbles in terms of size. Turning our attention to the
realm of social indicators, the ones most frequently
employed are "Working hours/productivity" and
"Working conditions". These indicators are pivotal in
shaping the holistic perspective of sustainability
adopted within the analysed studies.
In this section, a discussion of the results is
undertaken. Firstly, the integration of sustainability
indicators into production scheduling is explored.
Following that, insights into the prevalent resolution
methods are provided. Finally, drawing from our
analysis of 120 articles, we deliberate on potential
directions for future research.
5.1 Sustainability Indicators
Sustainability indicators are being actively integrated
into scheduling problems, driven by the overarching
goal of enhancing the sustainability of production
processes. The subsequent subsections delve into a
detailed examination of the incorporation of the social
pillar, evaluate opportunities for enhancements in
economic and environmental indicators, and
scrutinize how studied with real-life applications
consider and select sustainability indicators.
5.1.1 Emerging Social Sustainability
The diversity of indicators used to assess
sustainability in production planning underscores the
multifaceted nature of this field. While economic and
environmental indicators continue to play a central
role, the exploration of social indicators is still in its
early stages. Although the focus on employee well-
being is gradually gaining momentum, it has not yet
achieved widespread adoption, as indicated by the
limited utilization of indicators like “Skills and
training”, “Social benefits” and “Lost workday injury
and illness case rate”. These results are in line with
the revision conducted by Akbar & Irohara (2018)
five years ago, who identified that only one (out of 50
considered articles) included minimizing noise level
as an objective function, and another one included
accident rate as a constraint. Khaled et al. (2022) also
highlight that the social pillar is the least addressed
pillar and mention that indicators such as customer
satisfaction and employee health and safety are
suitable indicators to incorporate into scheduling
problems (although customer satisfaction can be
considered also an economic indicator). In the articles
analysed, the growing awareness of the need to
address social aspects within production scheduling
has been highlighted by five authors. Out of these
studies, only the first one proposes a specific social
indicator to consider in future work: “balance of
workers’ workload”, meanwhile the others just
mention the need to incorporate the social pillar but
do not address how. This reflects that although the
direction is known, the path is not clear.
5.1.2 Improvement in Economic and
Environmental Indicators
There are mainly two types of indicators that are well
integrated into most of the scheduling problems,
which are Makespan” and “Energy consumption”.
Although energy-related indicators are widely used,
An et al. (2020) and other eight articles mention the
need to go deeper into the calculation of the energy
consumed, either by including the time of use (TOU)
electricity price policy or similar schemes, by
including machine operating modes or speeds or by
refining the relationship between energy
consumption and CO
emissions. However, the
appearance of other environmental indicators related
to water consumption, material consumption, and
waste generated shows that only including aspects of
energy consumption is not enough to evaluate the
environmental sustainability of production processes.
In particular, Piroozfard et al. (2018) mention that
future research lines should incorporate indicators
related to the use of water, meanwhile, Feng et al.
(2020) mention contemplating material consumption
and waste generation. Regarding the indicator
“Supplier selection based on environmental
performance”, although it has only been used once,
the literature reveals a trend in including similar
indicators. Six articles point out the possibility of
considering transportation by measuring fuel
consumption or including additional time. Moreover,
more and more authors are realizing the importance
of considering factors of the supply chain that affect
production scheduling. In particular, Fülöp et al.
(2022) mention the need to incorporate aspects from
the whole production line into the problem and Feng
et al. (2020) go for a further approach, wanting to
include aspects from the whole supply chain into its
5.1.3 Real-Life Applications
Out of the 120 articles included in this analysis, only
20 of them provide a real-life application in a
ICORES 2024 - 13th International Conference on Operations Research and Enterprise Systems
manufacturing company. Through the analysis of
these studies, some insights about the selection of
sustainability indicators and their alignment with a
company's unique needs and objectives emerge.
One notable finding is that several studies lack
clear justification for their choice of sustainability
indicators. Seven studies provide general
sustainability-related reasons to justify their selection.
Eight studies justify their indicator selection by
referencing the energy-intensive nature of the sector
under study. While sector-specific considerations are
important, they should be complemented by a deeper
understanding of each company's distinct requirements
and sustainability objectives. Finally, only a small
number of studies, five in our analysis, consider the
specific needs, preferences, or goals of the companies
they investigate when selecting sustainability
indicators. Specifically, Coca et al. (2019) evaluated all
the inputs of the production process to find which
aspects they should consider. This gap indicates that a
significant portion of the research may not be
effectively aligned with what would truly enhance a
company's sustainability profile. Instead, many studies
tend to rely on typical or conventional energy-related
indicators, overlooking the unique circumstances of
each company.
Also, the studies showcase a significant absence
of standardized sets of indicators from which
researchers could choose to evaluate sustainability
comprehensively. Only Coca et al. (2019) and
Fathollahi-Fard et al. (2021) mention the use of ISO
guidelines to choose the indicators they consider in
their scheduling problem, but these guidelines are
specific to energy efficiency or workers conditions.
The use of a guideline that covers all sustainability
aspects and that permits a holistic understanding of it
is lacking in the studies analysed. This absence of a
standardized framework for indicator selection means
that the choice of indicators used is often not made
critically and deliberately, and there is limited
understanding of their potential impact on various
aspects of the company.
Regarding an evaluation of the companies’
sustainability performance, there is a lack of
evaluation of how the chosen sustainability indicators
may impact other critical aspects of the company.
This absence of holistic assessment means that
potential trade-offs or synergies between
sustainability goals and broader business objectives
are often not considered.
5.2 Resolution Methods
Regarding the resolution methods, heuristic and
metaheuristic methods are the most used to reach
solutions to scheduling problems, regardless of the
number of objectives considered. In the short-listed
studies, these methods were used 105 times since as
the problem becomes more complex and objectives
are added, these resolution methods can provide
solutions in reasonable computational time. Indeed,
these resolution approaches are the main ones used in
those studies that consider all three sustainability
objectives. Within this group, the most used method
is the Genetic Algorithm and its variants. In addition,
swarm-based methods are widely used. The use of
hybrid methods, which combine at least two methods
from the categories analysed, is also abundant
(Zhang, 2017).
Although there is a great diversity of algorithms
that have been used, many of them are used only once
or twice. As attempts are made to introduce additional
constraints, conflicting objectives, and multi-
dimensional goals, the necessity to formulate more
robust resolution methods emerges (Giret et al.,
2015). Based on the analysis of articles, there was a
general trend to use a resolution method as a basis and
improve it to obtain better results quickly. Forty-nine
articles mention that the resolution algorithm needs
improvement, which highlights the direction that
future research is taking. The improvements they
mention are related to improving the efficiency of the
resolution method to be able to analyse a more
complex problem (Fu et al., 2019; Lu et al., 2021).
Other studies mention the importance of comparing
the resolution algorithms with other existing ones for
benchmarking (Gao et al., 2021; Marimin & Farhan,
5.3 Future Research Lines
Finally, adding up to the future lines already
mentioned, some other ones have been identified in
this research. Introducing dynamic and uncertain
events was mentioned by thirty-five studies. Related
to practical applications, Feng et al. (2020) mention
that it is limited and that some advanced theories have
not been verified in real cases. Y. Z. Li et al. (2021)
and Jiang et al. (2019) consider that further studies
should include more practical constraints and
restrictions that meet the actual industrial conditions.
Uncertain events like machine failure, the arrival of
new jobs, cancellation of jobs, and rush orders are
aspects that should be considered (Cui & Lu, 2021).
Dynamic scheduling has also been addressed by
authors for future research accompanied using real-
time data (Fülöp et al., 2022), among others.
Integration of Sustainable Production Criteria into Production Scheduling: A Systematic Search and a Critical Review
The main goal of this systematic review was to fill the
knowledge gap about integrating sustainability into
manufacturing scheduling. The analysis of 120
studies in this area contributes to the understanding of
the current state of research in this field and provides
insights for future research and practice, guiding
manufacturing companies towards a proactive stance
in embracing sustainable production practices. Three
main conclusions can be drawn from this study,
which are explained below.
First, the review underscores the need for
integrating economic, environmental, and social
indicators in production scheduling. While economic
and environmental metrics like "Makespan" and
"Energy consumption" are common, social indicators,
including employee well-being and safety, are less
integrated. The analysis reveals gaps in real-life
applications, indicator justifications, standardization,
and holistic assessments, highlighting the need for
practical and strategic sustainability management
aligned with companies' unique goals.
Second, resolution methods for scheduling
problems are predominantly dominated by heuristic
and metaheuristic techniques, with Genetic
Algorithms being a prevailing choice. Regarding the
industrial sectors, sustainable production research has
been prominently centred on the metal and
engineering sectors. However, to ensure the
continued relevance of future research, it is
imperative to validate advanced theories through real-
world industrial applications and incorporate
practical constraints.
The final insight that this study provides is
regarding future research. It should prioritize the
effective integration of sustainability indicators into
the objective functions of scheduling models. This
integration should be based on holistic sustainability
frameworks that encompass all three sustainability
dimensions. Studying the alignment of sustainability
indicators with a company's specific needs and
objectives is crucial.
Nevertheless, it's important to acknowledge the
limitations of this review. The reliance on published
articles may have overlooked valuable insights from
other document types and real-world software
The work of the third author was partially funded
under grant INGPHD-51-2022 from Universidad de
La Sabana, Colombia. The authors employed
artificial intelligence (AI) tools to improve English
language readability and summarize text.
Subsequently, the authors reviewed and edited the
content as necessary, assuming full responsibility for
the publication's content.
Abedini, A., Li, W., Badurdeen, F., & Jawahir, I. S. (2020).
A metric-based framework for sustainable production
scheduling. Journal of Manufacturing Systems, 54,
Adhi, A., Santosa, B., & Siswanto, N. (2018). A meta-
heuristic method for solving scheduling problem: Crow
search algorithm. IOP Conference Series: Materials
Science and Engineering, 337(1). https://doi.
Akbar, M., & Irohara, T. (2018). Scheduling for sustainable
manufacturing: A review. Journal of Cleaner
Production, 205, 866883.
An, Y., Chen, X., Zhang, J., & Li, Y. (2020). A hybrid
multi-objective evolutionary algorithm to integrate
optimization of the production scheduling and
imperfect cutting tool maintenance considering total
energy consumption. Journal of Cleaner Production,
Biel, K., & Glock, C. H. (2016). Systematic literature
review of decision support models for energy-efficient
production planning. Computers and Industrial
Engineering, 101, 243259.
Coca, G., Castrillón, O. D., Ruiz, S., Mateo-Sanz, J. M., &
Jiménez, L. (2019). Sustainable evaluation of
environmental and occupational risks scheduling
flexible job shop manufacturing systems. Journal of
Cleaner Production, 209, 146168.
Cui, W., & Lu, B. (2021). Energy-aware operations
management for flow shops under TOU electricity
tariff. Computers and Industrial Engineering, 151.
Fathollahi-Fard, A. M., Woodward, L., & Akhrif, O.
(2021). Sustainable distributed permutation flow-shop
scheduling model based on a triple bottom line concept.
Journal of Industrial Information Integration, 24.
Feng, Y., Hong, Z., Li, Z., Zheng, H., & Tan, J. (2020).
Integrated intelligent green scheduling of sustainable
flexible workshop with edge computing considering
uncertain machine state. Journal of Cleaner
Production, 246.
Fülöp, M. T., Gubán, M., Gubán, Á., & Avornicului, M.
(2022). Application Research of Soft Computing Based
ICORES 2024 - 13th International Conference on Operations Research and Enterprise Systems
on Machine Learning Production Scheduling.
Processes, 10(3).
Fu, Y., Tian, G., Fathollahi-Fard, A. M., Ahmadi, A., &
Zhang, C. (2019). Stochastic multi-objective modelling
and optimization of an energy-conscious distributed
permutation flow shop scheduling problem with the
total tardiness constraint. Journal of Cleaner
Production, 226, 515525.
Gao, S., Daaboul, J., & Le Duigou, J. (2021). Process
planning, scheduling, and layout optimization for
multi-unit mass-customized products in sustainable
reconfigurable manufacturing system. Sustainability
(Switzerland), 13(23).
Giret, A., Trentesaux, D., & Prabhu, V. (2015).
Sustainability in manufacturing operations scheduling:
A state of the art review. Journal of Manufacturing
Systems, 37, 126140.
Janga Reddy, M., & Nagesh Kumar, D. (2020).
Evolutionary algorithms, swarm intelligence methods,
and their applications in water resources engineering: A
state-of-the-art review. In H2Open Journal (Vol. 3,
Issue 1, pp. 135188). IWA Publishing.
Jiang, T., Zhang, C., & Sun, Q. M. (2019). Green Job Shop
Scheduling Problem with Discrete Whale Optimization
Algorithm. IEEE Access, 7, 4315343166.
Khaled, M. S., Shaban, I. A., Karam, A., Hussain, M.,
Zahran, I., & Hussein, M. (2022). An Analysis of
Research Trends in the Sustainability of Production
Planning. Energies, 15(2).
Li, Y. Z., Pan, Q. K., Gao, K. Z., Tasgetiren, M. F., Zhang,
B., & Li, J. Q. (2021). A green scheduling algorithm for
the distributed flowshop problem. Applied Soft
Computing, 109.
Lu, C., Gao, L., Gong, W., Hu, C., Yan, X., & Li, X. (2021).
Sustainable scheduling of distributed permutation flow-
shop with non-identical factory using a knowledge-
based multi-objective memetic optimization algorithm.
Swarm and Evolutionary Computation, 60.
Lu, C., Gao, L., Li, X., Pan, Q., & Wang, Q. (2017).
Energy-efficient permutation flow shop scheduling
problem using a hybrid multi-objective backtracking
search algorithm. Journal of Cleaner Production, 144,
Marimin, & Farhan, M. N. (2020). Sustainable flexible flow
shop scheduling optimization in flexible packaging
industry using genetic algorithm. IOP Conference
Series: Earth and Environmental Science, 472(1).
Piroozfard, H., Yew Wong, K., & Tiwari, K. (2018).
Reduction of carbon emission and total late work
criterion in job shop scheduling by applying a multi-
objective imperialist competitive algorithm.
Piwowar-Sulej, K. (2022). Environmental strategies and
human resource development consistency: Research in
the manufacturing industry. Journal of Cleaner
Production, 330.
Renna, P., & Materi, S. (2021). A literature review of
energy efficiency and sustainability in manufacturing
systems. In Applied Sciences (Switzerland) (Vol. 11,
Issue 16). MDPI AG.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a
Methodology for Developing Evidence-Informed
Management Knowledge by Means of Systematic
Review. In British Journal of Management (Vol. 14,
Issue 3, pp. 207222).
Viles, E., Kalemkerian, F., Garza-Reyes, J. A., Antony, J.,
& Santos, J. (2022). Theorizing the Principles of
Sustainable Production in the context of Circular
Economy and Industry 4.0. Sustainable Production and
Consumption, 33, 10431058.
Xiao, Y., & Watson, M. (2019). Guidance on Conducting a
Systematic Literature Review. In Journal of Planning
Education and Research (Vol. 39, Issue 1, pp. 93112).
SAGE Publications Inc.
Zhang, R. (2017). Sustainable scheduling of cloth
production processes by multi-objective genetic
algorithm with tabu-enhanced local search.
Sustainability (Switzerland), 9(10).
The categorization of the revised indicators can be
seen in the following link: Appendix 1. The full list
of revised articles can be seen in the following link:
Appendix 2.
Integration of Sustainable Production Criteria into Production Scheduling: A Systematic Search and a Critical Review