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Layout optimization methods and tools: A systematic literature review

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Purpose: This article aims to show the methods used to optimize layout and tools that have been applied since 2010 in the most diverse production environments. Theoretical Reference: Due to the intense competitiveness and uncertainties in the current market, improving processes and increasing production efficiency by managing the layout of a facility can be one of the methods to benefit organizations. In this context, knowledge of the main tools and methods is essential in defining the conduct of studies. Design/methodology/approach: Research was carried out through a systematic literature review. The databases used were Science Direct and Portal Capes. Using keywords, a defined scope and the relevance of the theme, international articles were selected for reading and discussion. Findings: Through the review, it was possible to select 51 articles which were relevant to the topic. Due to the complexity of the layout management study, it was found that increasingly more algorithms, mathematical and computational models are being used to solve these NP-hardness problems. Research, Practical & Social implications: adjusting to new methods and ways of solving problems laying out facilities, although scenarios can be extremely varied, they can improve business results by making the process more efficient. Originality/value: The article compiles and briefly explains the methods found to optimize the layout, which is of great importance considering the knowledge that these models spread on production management practices. Keywords: Layout optimization. Algorithm. Method. Facility layout planning
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Recebido:
05/04/2021
Aprovado:
24/08/2021
Lucas Stelle Chemim1, Federal University of Paraná, Paraná, Brazil
Nicolle Christine Sotsek2, Federal University of Paraná, Paraná, Brazil
Mariana Kleina3, Federal University of Paraná, Paraná, Brazil
Objetivo: Este artigo tem como objetivo apresentar os métodos e ferramentas de otimização de leiaute que vem sendo
empregadas desde 2010 nos mais diversos ambientes produtivos.
Referencial Teórico: Devido à intensa competitividade e incertezas do mercado atual, aprimorar processos e aumentar a
eficiência da produção por meio do gerenciamento de um leiaute de instalações pode ser uma das formas de trazer benefícios às
organizações. Nesse contexto, conhecer as principais ferramentas e métodos é essencial para definir a condução do seu estudo.
Desenho/metodologia/abordagem: A pesquisa foi realizada por meio de uma revisão sistemática da literatura. As bases de
dados utilizadas foram Science Direct e Portal Capes. Por meio de palavras-chave do escopo definido e da relevância do tema,
os artigos internacionais foram selecionados para leitura e discussão.
Resultados: Por meio da revisão foi possível selecionar 51 artigos considerando sua relevância para o tema. Devido à
complexidade do estudo de gerenciamento de leiaute, descobriu-se que cada vez mais algoritmos, modelos matemáticos e
computacionais estão sendo usados para resolver esses problemas NP-difíceis.
Implicações de pesquisa, práticas e sociais: habituar-se a novos métodos e maneiras de resolver problemas de leiaute de
instalações, embora possam ser nos mais diversos cenários, pode melhorar os resultados de negócios tornando o processo mais
eficiente.
Originalidade / valor: O artigo compila e explica resumidamente os métodos encontrados para a otimização do leiaute, o que
é de grande importância tendo em vista que o conhecimento desses modelos dissemina práticas de gestão produtiva.
Palavras-chave: Otimização de leiaute. Algoritmo. Método. Planejamento de layout de instalações.
Purpose: This article aims to show the methods used to optimize layout and tools that have been applied since 2010 in the
most diverse production environments.
Theoretical Reference: Due to the intense competitiveness and uncertainties in the current market, improving processes and
increasing production efficiency by managing the layout of a facility can be one of the methods to benefit organizations. In
this context, knowledge of the main tools and methods is essential in defining the conduct of studies.
Design/methodology/approach: Research was carried out through a systematic literature review. The databases used were
Science Direct and Portal Capes. Using keywords, a defined scope and the relevance of the theme, international articles were
selected for reading and discussion.
Findings: Through the review, it was possible to select 51 articles which were relevant to the topic. Due to the complexity of
the layout management study, it was found that increasingly more algorithms, mathematical and computational models are
being used to solve these NP-hardness problems.
Research, Practical & Social implications: adjusting to new methods and ways of solving problems laying out facilities,
although scenarios can be extremely varied, they can improve business results by making the process more efficient.
Originality/value: The article compiles and briefly explains the methods found to optimize the layout, which is of great
importance considering the knowledge that these models spread on production management practices.
Keywords: Layout optimization. Algorithm. Method. Facility layout planning
Layout optimization methods and tools: A systematic literature review
ABSTRACT
1. lucaschemim@ufpr.br; https://orcid.org/0000-0001-8334-5909; 2. Rua Francisco H. dos Santos, nº. 210 - Centro
Politécnico / Setor de Tecnologia - Bairro: Jardim das Américas - Curitiba-PR - CEP: 81531-980 - Caixa Postal:
19011; nicolleramos@ufpr.br; https://orcid.org/0000-0001-8567-5522; 3 marianakleina@ufpr.br, https://orcid.org/
0000-0001-8108-8793.
CHEMIM, L.S.; SOTSEK, N.C.; KLEINA, M. Layout optimization methods and tools: A systematic literature review
GEPROS. Gestão da Produção, Operações e Sistemas, v.16, 4, p. 59 81, 2021.
DOI: http://dx.doi.org/10.15675/gepros.v16i4.2806
Editor Responsável: Prof. Dr. Hermes Moretti Ribeiro da Silva
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1. INTRODUCTION
The layout of industrial facilities, the location of equipment, machinery and even the
layout of furniture in professional environments are one of the fundamental aspects that
directly interfere in the productivity and possible companies’ success. According to Carlo et
al. (2013), competitiveness requires professionals to constantly improve manufacturing
practices and logistics. Within this search for improvement opportunities, the layout is an
integral part of the manufacturing and has a relevant impact on costs and minimization of
distances to the operational efficiency (SILVA et al., 2012). Based on Canen and Williamon
(1998), "the best movement of the material is not to move".
Tompkins et al. (2010) found that an ineffective layout design and material handling
represents between 20% and 50% of the total production costs. It’s estimated the problem of
placing departments that achieve an optimal solution reduces handling and operational costs
about 10%-30% and increase the production efficiency by removing unnecessary processes.
Errors in layout design can generate interruptions in supply, leading to internal and external
consumer dissatisfaction, production delays, causing confusing and unnecessary queues and
stocks, in addition to high costs related to inefficiency in creating synergy between the
physical arrangement set (KANNAN, 2010; SINGH; YILMA, 2013).
Manufacturing companies are redesigning their production systems to address new
production technologies or product changes, which require a comprehensive planning process
to form the final design changes (SCHUH et al., 2011). It is supposed a good layout design
gives companies better use and organization of available space and in the internal flow of
people and machines, facilities in the supervision of tasks and material handling, reduction of
time and stock between processes, and lower operating costs.
Taking into account these advantages, the methods and tools of layout management
are the study object of this article, which aims to understand the scenario and the principles
that guide it. Therefore, the purpose of this work is, through a systematic review of the
literature, examine that one’s applied and studied in the last 10 years in manufacturing
companies. Although the results are quite restricted to unequal area and single row layout
cases, the practical use of these features is likely to become even more common in future,
comprising over time a greater diversity of situations. The benefits of achieving a more
optimized process explain the importance of this review.
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The article sequence is organized as follows: section 2 presents a brief theoretical
referential on the subject; in section 3, the materials and methods used for research are
detailed, as well as the definition of databases and keywords, the creation of the article
collection and selection protocol and a result overview; in section 4, a quantitative analysis of
the results takes place; section 5 succeeds a more in-depth discussion about optimization
methods and tools, explaining the most frequent, it’s particularities and how they were tested
and applied; in section 6, finally, the conclusions regarding the research are described.
2. THEORETICAL FOUNDATION
According to Slack et. al. (2018), a physical arrangement is one of the most evident
characteristics of a productive operation, because it determines the shape and appearance of
its environments. It is possible to visually perceive some problems in the layout of
associations, such as the crossing of flows or excessive movement, but in order to propose an
improvement in the layout of a productive arrangement, it is necessary to follow a
methodology, using tools for this purpose. (SLACK et al., 2018).
The main layout rearrangement method is SLP - Systematic Layout Planning,
combined with improvement tools related to the process, products and ergonomic aspects of
the productive system. The second most used method consists only of the use of analysis tools
for the definition of the new layout. (NEGRÃO et al., 2019). However, the application of
conventional optimization methods to modern production lines is not only becoming too
complex, but also becoming inaccurate and unreliable (HO et al., 2019), decreasing the
quality that could be achieved using some more advanced method.
Improving the architectural layout for diverse objectives using rigorous mathematical
optimization methods gradually receives more attention by the researchers (ZAWIDZKI;
SZKLARSKI, 2020), trying to result in more optimized, accurate and effective technique.
Among the most used methods, the following stand out: Heuristic methods such as genetic
algorithms, coral reef, ant colony, particle swarm optimizations and local search strategies. In
addition to these, according to the same authors, more recently, to deal with the qualitative
aspects of the manufacturing environment, they are being used, among others: implementation
simulated annealing has been documented in, tabu search, biased random-key genetic
algorithm and mixed integer linear programming.
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The development of the modern industry has poses new challenges for facility layout
planning (JIAN; NEE, 2013). To meet the fast-changing production targets, enterprises
nowadays need to reconfigure the existing shopfloor layouts constantly to update their
operations. Therefore, in addition to algorithm tools, virtual reality has also been widely used.
By creating a 3D virtual environment, the VR-based tools allow the users to design layout
plans manually based on their knowledge and experience (JIAN; NEE, 2013).
3. METHODOLOGICAL PROCEDURES
The integral development of the research and consequent studies were carried out in
accordance with the approach known as systematic literature review (SLR). SLRs offer
readers comprehensive knowledge of the literature in a field through a holistic and organized
précis that adheres to standard protocols (AFROOZ; NAVIMIPOUR, 2017; AHMAD et al.,
2018; MEHTA; PANDIT, 2018).
The progress of the study, which aims to exhibit the qualitative and quantitative
panorama of the methods and tools for layout optimization, was divided into three main
stages explained below, following the approach developed by Kitchenham et al. (2009). First,
the research questions must be defined. Then, the protocol development must be started,
reducing the possibilities of biases through search arguments. Selection criteria and quality
filters must be described before starting data extraction. The steps are described below:
Review planning: where the theme and the collection protocols that would be used
were defined, with identification of the need for a review.
Conducting the research: with clarification of the parameters used for the selection and
evaluation of articles, data extraction and data syntheses.
Dissemination and reports, when the content is reviewed and the study bibliometrics
occurs.
3.1 Definition of databases and keywords
In this stage, the main Brazilian portal, the periodical CAPES, and the international
platform Science Direct were chosen as the main databases for searching relevant articles.
Gathering academic and scientific works in their collections that cover vast areas of
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knowledge, the platforms were selected and used for the quality of the works that went
through a rigorous filtering process before being published. Besides that, most of the articles
found were also inserted in other portals, however, CAPES and Science Direct were already
known. This familiarity and fluency of the Portuguese language by the authors, in CAPES
case, facilitated their use. In addition, the authors had free access to all the content available
on the channels, since the entry on the sites was made through the Internet Protocol Address
of Federal University of Paraná, which allowed access to the platform's signed content for
being part of the participating institutions.
Subsequently, the search keywords that would achieve an adequate result according to
the objective of the study were defined. Within the scope of search strings, the conjunction
“OR” suggested the possibility of a synonym also find good results and the conjunction
“AND” was used to combine two groups of words. The first one varied between “Layout”,
“Layout Rearrangement”, “Layout Project”, “Layout Reformulation”, “Layout Design”,
“Layout Planning”, “Physical Arrangement”, “Layout Optimization”, “Unequal Area Facility
Layout Problem”, “Layout Solving”, “Plant Layout Study” until reaching the most accurate
result defined as “Layout (Optimization OR Planning OR Solving)”. The second started with
“Methodology OR Method OR Mechanism OR Tool” and ended in “Novel (Tool OR Method
OR Methodology)”.
3.2 Creation of the article collection and selection protocol
In a second step, through the dynamic reading of the articles, those that were relevant
to the purpose of the work were selected. It should be noted that filters were applied to make
the studies feasible. Among them, only articles published between the years 2010 and 2020,
in journals classified as A1, A2, B1, B2 or B3 according to the Sucupira platform would be
selected. The platform makes available on its channel the evaluation and recognition of
journals according to some criteria applied to Engineering III topic, where industrial
engineering is located. The indexing of journals in the database InCites Journal Citation
Reports (JCR) and Scopus, the national or international coverage of the works, scientific or
not, and well-defined editorial policies are criteria used for classification. Besides that, results
of the search were sorted for “relevance” before reviewing abstracts, due to the length of the
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search string, including articles from multiple disciplines, such as Engineering, Energy,
Sustainability, Aerospace Science, Architecture and others.
The number of results was quite high, so, when the articles stopped making sense in
relation to the searched topic, the searches were stopped. In Science Direct database, which
offered the possibility of applying another filter to the title, abstract and keywords, the set
“Layout AND Optimization AND (Tool OR Method)” was also used. After that, the snowball
method was applied to the references of the selected articles, aiming to include the relevant
ones that could have been excluded from the selection during the research. Obtaining new
articles through references cited by the authors created a loop and stopped only when articles
applied to the theme were no longer identified. The same filters mentioned above (date and
Sucupira classification) were used. The selected articles were organized in an Excel
spreadsheet.
After finishing all the process mentioned above, a new search for articles published in
2021 occurred, to guarantee the presentation of the latest state of the art articles considering
the same conditions as applied before. They were not counted in the quantitative analyses, and
their contents were only addressed in the qualitative part.
3.3 Results overview
The previously selected keywords combination, “Layout (Optimization OR Planning
OR Solving) AND Novel (Tool OR Method OR Methodology)”, which triggered relevant
results to the research objective, was inserted in two databases: Science Direct and Periodical
CAPES. According to the systematic literature review methodology, the keywords, without
applying filters, resulted in 36621 articles found in Science Direct and 24930 in CAPES.
From the moment the date (2010-2020) and classification filters were applied according to the
Sucupira platform (A1, A2, B1, B2, B3), the results were reduced in CAPES to 18541 studies.
In Science Direct, in addition to these filters, the keyword “Layout AND Optimization AND
(Tool OR Method)” was applied to the title, summary and keywords of the articles, reaching
437 works. Of the total of 18978 articles, 44 articles were selected through dynamic reading,
of which 17 were excluded either because they were repeated or because they were not
classified on the Sucupira platform. Added to the remaining 27, 20 articles were found by
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Snowball Sampling, resulting in a sample of 47 reviewed articles for the construction of this
study. Besides that, 4 articles from 2021 had their contents studied.
4. RESULTS
Analyzing the quantitative results, the predominance of some countries in the research
on methods and tools for layout optimization is notable. This is due to the fact that they
belong to the countries that receive the most investments in research and development, have
group studies focuses on the topic and, in the case of China, have large industrial and
manufacturing parks. China stands out, with 9 articles published in this area, followed by
Spain, with 7 works done (see fig.1). The dominance, if portrayed by continents, becomes
even more evident, since Asia published 25 articles related to the theme, followed by Europe
with 18 publications. America appears with only 4 works related to the subject.
Figure 1 - Publications by country.
Note: Country reflects the location of the institute to which the first author is affiliated.
Following the same patterns mentioned above, regarding the understanding of the
research status, traced through the review of the articles, the most frequent authors are also
Spanish and Chinese. Among those who took part in more than one research in relation to
layout optimization methods and tools, Laura García Hernández, who currently works at the
Department of Rural Engineering doing researches in Computer Engineering, Engineering
Education and Industrial Engineering and Lorenzo Salas-Morera, who works in the Area of
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Project Engineering doing research in Interactive Evolutionary Algorithms and Educational
Technology, from the University of Córdoba (Spain), are the most relevant. Jingfa Liu, from
the School of Information Science and Technology (China), comes soon afterward in order of
expressiveness (See figure 2).
Figure 2 - Most Frequent Authors
Source: The authors.
The selected articles were published in several journals, however, the leading sources
were European Journal of Operational Research (9 publications), followed by Expert Systems
with Applications (8 publications) and, then, Journal of Manufacturing Systems (5
publications). (See figure 3)
Figure 3 - Publications per Journal
Source: The authors.
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Regarding to the years of publication of the selected articles, the last three years
analyzed show a growth trend, signaling, perhaps, that research and new discoveries on the
layout methods and tools subject will be among studies carried out in the coming years, due to
such importance and influence in the context of company performance and the emergence of
new technologies. (See figure 4)
Figure 4 - Number of Articles Published by Years
Source: The authors.
Finally, to obtain an overview of the found results, the keywords detailed in each of
the articles studied were gathered, for later construction of a word cloud that represents the
main focuses of the research. An attractive arrangement of randomly positioned words were
created by “Word it out” online software, where the most important and cited words are
bigger than the others. It is perceived, through the directly proportional relationship between
the size of the word and the number of times it was repeated, great emphasis on possible
combinations that would result in Layout Optimization and Facility Problem. In addition,
some words directly related to algorithms and mathematical models used to search for optimal
solutions are identified, such as Genetic Algorithm, Ant Colony, Pareto-optimal, Simulation,
Modeling, Augmented Reality, Multi Objective, and Particle Swarm Optimization. (See
figure 5)
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Figure 5 - Authors keywords
Source: The authors.
5. DISCUSSIONS
Through a systematic review of the scientific literature, 51 articles were selected for
the study. Next, important concepts will be presented that permeated the studies and ensured
that the works had the due reliability. A brief explanation of the main methods found will be
carried out at the end, as well as possible directions and recommendations for future research
in the area.
5.1 Research topic
The facility layout problem is the problem of placing facilities in a certain shop floor
so that facilities do not overlap each other and are satisfied with some given objectives (LIU
et al., 2018). It is an NP-hard combinatorial optimization problem related to maximize the
performance and minimize the operating, material handling and rearrangement costs. The
conventional facility layout problem is concerned with arranging a number of interacting
facilities, such as machines or departments (CHE et al., 2017), being studied within three
different contexts: single row (along one side of the path), double row (in two sides or two
parallel rows of a central corridor or aisle) or multi-row (in some fixed numbers of rows).
Most of the selected articles refer to the study of layout problems with single row and unequal
areas.
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5.2 Unequal area facility layout problem
Deal with the layout of departments in a facility, encompassing a class of extremely
difficult and widely applicable multi-objective optimization problems with constraints arising
in diverse areas and meeting the requirements for real-world applications (LIU et al., 2019). It
is commonly encountered in industry practice, being a relevant task in manufacturing and is a
hard optimization problem, in which traditional optimization techniques do not obtain good
results (HERNANDEZ et al., 2020). Have been identified in the select articles, various kinds
of heuristics and meta-heuristics to solve such problems over the past several decades. Most
of them study either the quantitative single objective (material handling) or multi-objective
Unequal Area Facility Layout Problems, which are usually converted into a single objective
problem by using a weighted-sum method (LIU et al., 2020).
5.3 Testing and application
The approaches used or identified by each author in studies that sought to find
efficient solutions to layout problems were tested, most of the time, comparing results with
state-of-art methods and benchmark problems in representative instances, performance
metrics and hypothetical data of the literature or experimental studies in companies. In some
cases, virtual manufacturing work cells were developed and utilized to evaluate the proposed
methodology, even as computational experiments on a real-life or randomly generated
instances. The methods were mainly implemented in manufacturing plants, but closely related
research was conducted in construction sites, family houses and job shops.
5.4 Solution methods and tools
The articles show that there is no single best approach to solve the FLP. It needs to
select a method according to the characteristics of the problem such as size, linearity, and
non-linearity (MOSLEMIPOUR et al., 2012). Because of that, some authors combine two or
more algorithms that solve the same problem, looking for cluster desired features of each one
and reconcile different combinatorial restrictions and objectives simultaneously. A better
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overall algorithm can be found by a Hybrid study. In addition, classic approaches are very
slow or deviate considerably from optimal solutions. The development of heuristic techniques
comes against that, aiming for faster and more accurate resolutions.
In this context, there is a high importance and relevance to the use of algorithms in
order to rearrange and optimize layouts, since it corresponds to the vast majority of the works
gathered. Besides that, several papers seek to achieve Pareto-optimal solutions, related to use
a multi-objective constrained genetic algorithm that achieves the best result among those
studied. In this case, there is no way to improve the value of any of its objective criteria
without degrading the performance of other aspects of the model. Slicing Tree
Representations or Structure were also widely used to easily represent the problems without
restricting the solution space, dividing a facility in proportion to the areas of the departments.
Investigating the content of the selected articles, there is a considerable relevance of
some methods compared to others less cited. Due to the frequency they are applied and
studied in the literature conglomerate, each one with its respective particularities, these
approaches will be discussed in greater detail below:
Genetic Algorithm: is a stochastic search technique that mimics the mechanisms of the
Darwinian evolution based on the concept of the survival of the fittest (GOLDBERG,
1989). The process starts with an initial random population of N individuals generated,
named chromosomes. Iterations and operations evolve it successively, choosing the
higher fitness chromosomes by evaluating. The crossover and mutation procedure
combine two present chromosomes and try to improve a single one, respectively. The
best fittest values survive as elite children. The usual process is repeated until a
specified number of iterations is reached. There are several variants of genetic
algorithms proposed, but, in general, they have the ability to combine with other
algorithms and find the best solution for different kinds of layout problems. However,
can be slow, the convergence is not guaranteed and depends on evaluation
performance (MOSLEMIPOUR et al., 2012). Juan M. Palomo-Romero et al. (2017)
proposed an island model to solve problems such as premature convergence, lack of
diversity, or high computational cost. Aiello et al. (2013) proposed a multi-objective
and an electre method that optimized the several objective functions simultaneously
and allows the decision maker to express his preferences on the basis of the
knowledge of candidate solution set. Aiello et al. (2012) suggested a GA based upon
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the slicing structure where the relative locations of the facilities on the floor are
represented by a location matrix encoded in two chromosomes. Gonçalves and
Resende (2015) presents a biased random-key to determine the order, dimensions and
strategy of placement, and a linear programming model. Caputo et al. (2015) used a
genetic algorithm including pumping costs and safety issues. Hernandez et al. (2013)
introduced expert knowledge to support the decision maker and adjust process
qualitative preferences. Vitayasak et al. (2016) focused on a backtracking search
algorithm to solve stochastic demand problems. Ning et al. (2019) proposed a hybrid
genetic algorithm-ant colony optimization model to obtain trade-off solutions,
reducing noise pollution by planning construction. Jiang and Nee (2013) adopted an
Analytical Hierarchy ProcessGenetic Algorithm for automatic layout planning. Datta
et al. (2011) applied a permutation-based to arranging a number of facilities on a line
with minimum cost.
Coral Reef Optimization Algorithm: was recently proposed by Salcedo-Sanz et al.
(2014) and imitates the evolution of coral reefs and the different processes occurring
in these ecosystems (HERNANDEZ et al., 2019). Briefly, the process consists in:
firstly, an initial population of the reef is randomly generated. Then, new solutions
(larvae set) are created from the ones belonging to the reef in order to compete for a
place in the reef. If the larva has a better health function value or the spot is empty,
larvae present in the water settle in the reef. After that, a fraction of the corals with
better fitness present in the reef duplicate themselves and are released to the water,
trying to settle using the same conditions above. Finally, the worse evaluation fraction
is depredated. Hernandez et al. (2020) proposed this method with substrate layers to
solve the unequal area facility layout problem. According to her, it increased the
diversity generation within the algorithm, which is helpful to improve the exploration
of the searching space, avoiding to fall into local minima, consequently, finding better
solutions with less computational cost. Hernandez et al. (2020) confirmed the
excellent performance of the proposed algorithm in solving unequal-area facility
layout problems.
Ant System/Colony Optimization: is a bio-inspired optimization algorithm based on
simulating the behavior of natural ants that succeed in finding the shortest paths from
their nest to food sources by communicating via a collective memory that consists of
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pheromone trails (LIU; LIU, 2019). Ant Colony Optimization algorithms have been
used to find the shortest tour in the traveling salesman problem and needs to be
converted into the problem of finding the shortest path on a weighted graph. Jingfa
Liu and Jun Liu (2019), Wong and See (2010) and Komarudin and Wong (2010)
applied the method and its variants to update the layouts and add the diversity of
solutions. Moslemipour et al. (2012) declared the Ant Colony Optimization is robust
and flexible in dynamic environments but it is not easy for coding and has
disadvantage in parameters initializations by trial and errors or at random.
Particle Swarm Optimization: works by having a population (called cloud or swarm)
of individual candidate solutions (particles). These particles are moved around in the
search-space according to a few simple formulae (ZHANG, Y., 2015). The standard
Particle Swarm Optimization is very simple and easy to implement (MOSLEMIPOUR
et al., 2012). The new location of each particle is determined by a velocity term, which
reflects the attraction of global best and its own best during the history of the particle
and random coefficients (SAHAB et al., 2013). When improved positions are being
discovered these starts guiding the swarm flow. The process is repeated until the local
best swarm and global leader particle are identified. The method, used by
Samarghandi et al. (2010), Guan et al. (2019), Guan et al. (2020) and Liu et al. (2018)
is fast, cheap and there are few parameters to adjust (MOSLEMIPOUR et al., 2012).
Mixed integer linear programming: It is a mathematical optimization program in
which the objective function and the constraints are linear and the variables are
integers and continuous. Zhang et al. (2017) formulated the integrated optimization
problem with the objective of minimizing the total cost of production and warehouse
operations. Guan et al., (2019), used a mixed integer linear programming model with
three objectives: minimization of overall material handling costs, minimization of
number of workshops, and maximization of utilization ratio of workshop floor. Anjos
and Vieira (2017) identified this approach as the main contributions to the facility
layout problems area.
In addition to the approaches used above, other tools and solutions, identified with
lesser degree of use within the scope of the selected articles, can be manipulated in order to
help solving the problems of rearrangement and layout optimization. With the advent of new
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GEPROS. Gestão da Produção, Operações e Sistemas, v.16, n. 4, p. 59 - 81, 2021.
technologies tools, the use of realistic visualization, augmented reality, big data, machine
learning and data envelopment tends to become increasingly common. Lindskog et al. (2017)
show how to reduce the time required for planning and implementing the redesign by
supporting the process with realistic visualization looking for analyze and evaluate planned
changes prior to implementation. Nee et al. (2012) emphasize the importance of designing
and providing intuitive and effective human interfaces, as well as suitable content
development in order to make augmented reality a powerful tool in the manufacturing
engineering field. Lee et al. (2011) constructed a computer-simulated mixed reality
environment for virtual factory layout planning integrates real objects, such as real images,
with the virtual objects of a virtual manufacturing system, minimizing the cost of
implementing virtual objects and enhances the user’s sense of reality. Manzini et al. (2018)
integrated different computational tools addressing the design of the system, the optimization
of the layout, the planning of reconfiguration actions as well as production planning.
Among the articles published in 2021, that represents the latest state of the art works
found, it is worth mentioning 4 relevant studies that relate to the research topic. Cubukcuoglu
et al. (2021) presents a heuristic optimization algorithm (Iterated Local Search) developed in
a CAD environment for Quadratic Assignment Problem in a hospital context, as well as its
implementation as a computational design tool. The tool calculates distances using graph
traversal techniques, minimizing the internal transportation processes between interrelated
facilities demonstrated in 3D. Chinnathai et al. (2021) proposed a novel Data-Driven Scale-up
Model (DDSM) that builds upon kinematic and Discrete-Event Simulation (DES). It identifies
a near-optimal configuration with a multi-objective Genetic Algorithm (GA) supporting
decision-making activities, with significant savings in time, cost and effort in a manufacturing
line. Ozden et al. (2021) present a computational software system using a meta-heuristic for
facilitating the design of warehouse layouts to near-optimality, considering the average
walking distance of the picker. Guo et al. (2021) put forward a method of flexible cellular
manufacturing based on digital twin. It continuously optimizes the method, based on
decoupling event mechanisms and multi-objective constraints.
Table 1 summarizes the main methods determined in the articles selected in this
review, each one cited at least 3 times among the papers. Note the most applied method was,
by far, Genetic Algorithms with 14 applications. Then, local search appears with 6 studies.
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Layout optimization methods and tools: A systematic literature review
GEPROS. Gestão da Produção, Operações e Sistemas, v.16, n. 4, p. 59 - 81, 2021.
Ant colony optimization, mixed integer linear or non-linear programming and particle swarm
optimization presents 4 publications, while, lastly, coral reef optimization were identified in 3
cases. Together, they represent more than half of all compiled works.
Table 1 - The methods
Articles
Genetic
Algorithm
Coral Reef
Optimization
Algorithm
Ant System/Colony
Optimization
Particle Swarm
Optimization
Mixed integer
linear or non-
linear
programming
Local
Search
4
x
5
x
7
x
8
x
9
x
10
x
11
x
12
x
13
x
14
x
15
x
16
x
18
x
x
19
x
x
20
x
x
x
22
x
25
x
29
x
31
x
x
32
x
42
x
x
44
x
47
x
48
x
55
x
56
x
x
57
x
x
Total
14
3
4
4
4
6
Source: The authors.
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Layout optimization methods and tools: A systematic literature review
GEPROS. Gestão da Produção, Operações e Sistemas, v.16, n. 4, p. 59 - 81, 2021.
Note: Articles not mentioned in the table correspond to those that also carried out systematic reviews/survey of
this content, were used as reference or their methods had fewer citations, being less expressive. This does not
diminish the importance of your findings. Simulated annealing and tabu search, per example, didn’t appear a lot,
but they are mentioned in the theorical references of the articles as knowing methods.
It is worth mentioning that all these studies present their own peculiarities, adjusting
the algorithms used to suit their realities. Among these specificities that seek to find optimal
solutions, the combination of models and the use of computational and technological tools
stand out. Convex and semidefinite optimization frameworks, niching and e-constraint
method, Wang-Landau sampling algorithm, CAD, bay structure, neural networks, between
others, are some of the many diverse methods and tools discovered in the study that contribute
to the development of best practices in facility rearrangement.
5.5 Advantages
In general, the results found in all articles gathered follow the same pattern. According
to Jankovitz et. al (2011), his methodology consistently produces competitive and often
improved layouts for well-known large instances when compared with other approaches in the
literature. The decomposition-based algorithm developed by Guan et al. (2020), also, is able
to find the optimal solutions in much less time compared with two exact methods. The set of
these two parameters, solution quality and the speed of results generation, well describe the
benefits of using these unconventional models, bringing organizations more efficiency and,
consequently, advantages over their competitors.
6. CONCLUSION
The review of the articles gathered in this study allowed us to conclude that the use of
mathematical methods, algorithms and computational models was highly relevant when
applied to layout optimization problems. Most of them combined with other tools aiming to
reach optimal solutions for each situation. In order to spread beneficial management
practices, it is recommended getting used to these models, as they improve the efficiency of
organizations making them more competitive in the fierce market.
The status and content of surveys are typically focused on unequal areas and single
row cases. Furthermore, they are not flexible to the most diverse layout scenarios that can be
found, being efficient in cases where the characteristics of the problem classes are similar.
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Advantages achieved when new technologies assist in the layout development process reveal
that revolutionary tools such as augmented reality will be widely used to simulate events and
plan changes, avoiding errors and rework.
Taking into account that all methods and tools present some type of disadvantage or
weak point in relation to their application, it is up to future research to develop new
combinations of algorithms and harmonize the capabilities of each tool to achieve
increasingly better results. Presenting solutions that are flexible and encompass several
different scenarios can be interesting too, whereas the works always refer to a single type of
layout. In addition, inserting new performance measures will make jobs richer and more
qualified, as well as paying attention to poorly studied contexts, like multi-row FLP problems,
and future technologies that meet these needs.
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Facility layout problem (FLP) which deals with the layout of facilities within a given plant floor is an NP-hard combinatorial optimization problem. This paper studies multi-objective unequal-area facility layout problems (UA-FLPs) with the flexible bay structure (FBS), whose objectives refer to the material handling cost, the closeness relationship, the distance requirement and the aspect ratio of facilities. In recent years, some successes have been achieved by multi-objective evolutionary algorithms (MOEAs) for solving various kinds of optimization problems with multiple conflicting objectives. However, traditional MOEAs face a great challenge in the convergence and diversity of solutions for UA-FLPs. In this paper, a novel MOEA called the configuration space evolutionary (CSE) algorithm is developed to solve the UA-FLPs with multiple objectives. We consider a mating pool called a configuration (solution) bank in the CSE, and use evolutionary operations (selection, novel crossover and mutation) to produce new configurations of the pool. By introducing a measure of the radius dspace of the configuration bank, whose value is gradually reduced to narrow the search space, the convergence of solutions in the CSE is controlled. A method of the nearest and farthest candidate solution based on objective function normalization is combined with the fast non-dominated sorting to choose the Pareto-optimal solutions, which is good for the algorithm to keep diversity of the obtained solutions. The main contributions of this study lie in the use of a mechanism of evolution of population in the algorithm based on a configuration bank, and the use of a selection strategy based on the nearest and farthest candidate solution method, in order to improve the convergence and diversity of solutions. Experiments are carried out on eight different representative instances and performance metrics from the literature. Compared with the existing MOEAs, the CSE is able to find the better results and show better performance. The numerical experiments confirm the effectiveness of the CSE for solving multi-objective UA-FLPs.
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This paper proposes a novel approach to address the Unequal Area Facility Layout Problem (UA-FLP), based on the combination of both an Island Model and a Coral Reefs Optimization (CRO) algorithm. Two different versions of this Island Model based on Coral Reefs Optimization Algorithm (IMCRO) are proposed and applied to the UA-FLP. The structure of flexible bays has been selected as effective encoding to represent the facility layouts within the algorithm. The two versions of the proposed approach have been tested in 22 UA-FLP cases, considering small, medium and large size categories. The empirical results obtained are compared with previous state of the art algorithms, in order to show the performance of the IMCRO. From this comparison, it can be extracted that both versions of the proposed IMCRO algorithm show an excellent performance, accurately solving the UA-FLP instances in all the size categories.
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The concurrent layout and scheduling problem is an extension of the well-known job-shop scheduling problem with transport delays, in which, in addition to the decisions taken in the classic problem, the location of machines must be selected from a set of possible sites. The aim of this study was to solve this problem by using a local neighborhood search algorithm (LNSA). This algorithm used a random local neighborhood, where neighbors were produced by simple operators commonly used in metaheuristics for scheduling problems. The solution coding used in the LNSA enabled all the calculated solutions to be valid schedules for the problem. The findings of the study were compared with those obtained by Ranjbar, who proposed the benchmark problems. Our method obtained a minor average relative percentage compared to the best-known results. Furthermore, it achieved a smaller makespan in the problems with higher computational complexity, which may aid companies that require customized manufacturing.
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The double row layout problem (DRLP) is a common pattern of facility layout problem, which has practical applications in flexible manufacturing systems. The double row layout problem is vital to save transportation cost and enhance productivity. Nevertheless, it is very hard to handle the DRLP because of its characteristic of combination of combinatorial and continuous aspects. In this paper, a decomposition-based algorithm is proposed to solve the DRLP. We decompose the DRLP into two subproblems. In the first subproblem, the adjustable clearances between adjacent facilities are temporarily ignored. A first improvement based local search is applied to optimize the sequences of facilities on double rows. During this process, the facilities of double rows are placed starting at different abscissas rather than starting at the same abscissa for each arrangement. A property of the objective function of the DRLP is used to obtain the optimal difference between two starting abscissas. In the second subproblem, a particle swarm optimization is applied to optimize the adjustable clearances between adjacent facilities under the condition that the sequences of facilities are fixed. Our proposed algorithm is evaluated on 59 test instances and compared with the state-of-the-art methods. The experimental results demonstrate the high competitiveness of our proposed algorithm.