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.

1 INTRODUCTION

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

https://orcid.org/0000-0001-7535-7969

https://orcid.org/0000-0002-5080-482X

https://orcid.org/0000-0002-6251-3667

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

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

scheduling.

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 RESEARCH BACKGROUND

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.

Reference

Focus

Sustainability

aspects covered

Nº of reviewed

articles

Period of review

(Giret et al.,

2015)

Sustainable manufacturing operations

scheduling

Economic and

Environmental

2007-2015

(Biel &

Glock, 2016)

Decision support models for energy-

efficient production planning

Economic and

Environmental

Up to 2015

(Akbar &

Irohara,

2018)

Scheduling for sustainable

manufacturing

Economic,

environmental,

and Social

Up to 2018

(Renna &

Materi, 2021)

Energy efficiency and sustainability in

manufacturing systems

Economic and

Environmental

186

2007- June 2021

(Khaled et al.,

2022)

Sustainability of Production Planning

Economic,

environmental,

and Social

2011-2021

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

planning.

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

objectives.

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

sustainability.

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.

3 REVIEW METHODOLOGY

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.

4 RESULTS

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

then.

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

methods.

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

methods.

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

Other

Parallel machine

Single machine

Optimization

problem

SO: Single-objective

(24)

AM: Approximation methods

(22)

EM: Exact methods

(2)

MO: Multi-objective

(97)

T1: Mathematical model

formulation and resolution

(8)

T2: Mathematical model

formulation and proposal of

resolution method

T3: Proposal of resolution

method

(13)

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

shape.

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 used” made 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

criteria”.

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

2021.

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.

5 DISCUSSIONS

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

Concerns

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

problem.

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,

2020).

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

6 CONCLUSIONS

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

applications.

ACKNOWLEDGEMENTS

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.

REFERENCES

Abedini, A., Li, W., Badurdeen, F., & Jawahir, I. S. (2020).

A metric-based framework for sustainable production

scheduling. Journal of Manufacturing Systems, 54,

174–185. https://doi.org/10.1016/j.jmsy.2019.12.003

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.

org/10.1088/1757-899X/337/1/012003

Akbar, M., & Irohara, T. (2018). Scheduling for sustainable

manufacturing: A review. Journal of Cleaner

Production, 205, 866–883. https://doi.org/10.

1016/j.jclepro.2018.09.100

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,

268. https://doi.org/10.1016/j.jclepro.2020.121540

Biel, K., & Glock, C. H. (2016). Systematic literature

review of decision support models for energy-efficient

production planning. Computers and Industrial

Engineering, 101, 243–259. https://doi.org/10.1016/

j.cie.2016.08.021

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, 146–168. https://doi.org/10.

1016/j.jclepro.2018.10.193

Cui, W., & Lu, B. (2021). Energy-aware operations

management for flow shops under TOU electricity

tariff. Computers and Industrial Engineering, 151.

https://doi.org/10.1016/j.cie.2020.106942

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.

https://doi.org/10.1016/j.jii.2021.100233

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. https://doi.org/10.1016/j.jclepro.

2019.119070

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). https://doi.org/10.3390/pr10030520

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, 515–525. https://doi.org/10.

1016/j.jclepro.2019.04.046

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). https://doi.org/10.3390/su1323

13323

Giret, A., Trentesaux, D., & Prabhu, V. (2015).

Sustainability in manufacturing operations scheduling:

A state of the art review. Journal of Manufacturing

Systems, 37, 126–140. https://doi.org/10.1016/j.

jmsy.2015.08.002

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. 135–188). IWA Publishing.

https://doi.org/10.2166/h2oj.2020.128

Jiang, T., Zhang, C., & Sun, Q. M. (2019). Green Job Shop

Scheduling Problem with Discrete Whale Optimization

Algorithm. IEEE Access, 7, 43153–43166.

https://doi.org/10.1109/ACCESS.2019.2908200

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). https://doi.org/10.3390/

en15020483

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. https://doi.org/10.1016/j.asoc.

2021.107526

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.

https://doi.org/10.1016/j.swevo.2020.100803

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,

228–238. https://doi.org/10.1016/j.jclepro.2017.01.011

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).

https://doi.org/10.1088/1755-1315/472/1/012050

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. https://doi.org/10.1016/j.jclepro.

2021.129538

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. https://doi.org/10.3390/app

11167366

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. 207–222). https://doi.org/10.1111/1467-

8551.00375

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, 1043–1058. https://doi.org/10.1016/

j.spc.2022.08.024

Xiao, Y., & Watson, M. (2019). Guidance on Conducting a

Systematic Literature Review. In Journal of Planning

Education and Research (Vol. 39, Issue 1, pp. 93–112).

SAGE Publications Inc. https://doi.org/10.1177/0739

456X17723971

Zhang, R. (2017). Sustainable scheduling of cloth

production processes by multi-objective genetic

algorithm with tabu-enhanced local search.

Sustainability (Switzerland), 9(10). https://doi.org/

10.3390/su9101754.

APPENDIX

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