2 a review of different approaches to the facility layout problems

Int J Adv Manuf Technol (2006) 30: 425–433
DOI 10.1007/s00170-005-0087-9
ORIGINAL ARTICLE
S. P. Singh . R. R. K. Sharma
A review of different approaches to the facility layout problems
Received: 28 September 2004 / Accepted: 9 March 2005 / Published online: 12 November 2005
# Springer-Verlag London Limited 2005
Abstract Here, an attempt is made to present a state-of-
the-art review of papers on facility layout problems. This
paper aims to deal with the current and future trends of
research on facility layout problems based on previous re-
search including formulations, solution methodologies and
development of various software packages. New develop-
ments of various techniques provide a perspective of the
future research in facility layout problems. A trend toward
multi-objective approaches, developing facility layout soft-
ware using meta-heuristics such as simulated annealing
(SA), genetic algorithm (GA) and concurrent engineering
to facility layout is observed.
Keywords Survey of facility layout problems .
Combinatorial optimization . Quadratic assignment
problem (QAP) . Mixed integer programming (MIP)
1 Introduction
Determining the physical organization of a production
system is defined to be the facility layout problem (FLP).
Where to locate facilities and the efficient design of those
facilities are important and fundamental strategic issues
facing any manufacturing industry. Tompkins and White
[1] estimated that 8% of the United States gross national
product has been spent on new facilities annually since
1955, that does not include the modification of existing
facilities. Francis and White [2] claimed that from 20 to 50
percent of the total operating expenses in manufacturing
are attributed to materials handling costs. Effective facil-
ities planning could reduce these costs by 10 to 30 percent
annually. For FLP, the most common objective used in
mathematical models is to minimize the materials handling
cost, which is a quantitative factor. Qualitative factors such
as plant safety, flexibility of layout for future design
changes, noise and aesthetics [2] can also be considered.
They must be carefully considered in the context of the
FLP. This paper gives a review of different approaches to
the FLP, viz. formulations, solution methodologies and
current as well as emerging trends. This paper aims to
endorse readers who want to explore facility layout re-
search and layout packages; it is an active area in which
nearly 140 papers have been published on the FLP over the
last 20 years. A detailed review of each and every software
package is not carried out here but the references are
provided.
The paper is structured as follows: In Section 2 an
overview of the FLP along with the formulations is de-
scribed. Solution methodology is addressed in Section 3.
Current trends and further scope of work are discussed in
Section 4 followed by a conclusion.
2 Overview of facility layout problem
The FLP is a well studied combinatorial optimization
problem which arises in a variety of problems such as
printed circuit board design; layout design of hospitals,
schools, and airports; backboard wiring problems; type-
writers; warehouses; hydraulic turbine design; etc. The
focus of this review work is on the facility layout of
industrial (manufacturing) plants, which is concerned with
finding the most efficient arrangement of ‘n’ indivisible
facilities in ‘n’ locations. Minimizing the material handling
cost is the most considered objective but Mecklenburgh [3]
and Francis et al. [4] gave qualitative as well as quantitative
objectives for FLP. Reduced material movement [5, 6]
lowers work-in-process levels and throughput times, less
product damage, simplified material control and schedul-
ing, and less overall congestion. Hence, when minimizing
material handling cost, other objectives are achieved
simultaneously. The output of the FLP is a block layout
that specifies the relative location of each department.
Detailed layout of a department can also be obtained later
by specifying aisle structure, and input/output point
locations which may include flow line and machine layout
problems. This paper is a survey of block layout. This
section deals with the description of formulations of FLP.
In the following sub-sections, Section 2.1 describes
QAP, graph theoretic approach is given in Section 2.2 and
MIP formulation for FLP is provided in Section 2.3.
S. P. Singh (*) . R. R. K. Sharma
Department of Industrial and Management Engineering,
Indian Institute of Technology Kanpur,
Kanpur, 208016, India
e-mail: sprsingh@iitk.ac.in
Fax: +91-512-2597553

2.1 QAP model
FLP has been generally formulated as a QAP introduced by
Koopmans and Beckman [7] which is NP-complete [8–10]
and one of the frequently used formulations to resolve FLP.
Consequently, even a powerful computer cannot handle a
large instance of the problem. The objective can be to either
minimize time, cost, traveling distance, and/or flows. Con-
sequently, various heuristics have been proposed thus far to
solve large instances of QAP and a review of these heuristic
is given in Section 3.2. Equivalent linear integer formula-
tions and heuristics have developed for solving the QAP
but they are limited to particular problems [11–13]. Lawler
[14] and Christofides et al. [15] demonstrated the equiv-
alence of the QAP problem to a linear assignment problem
with certain additional constraints. The following formu-
lation is adopted from Koopmans and Beckman [7].
MinTF ¼ 1=2 Ã
Xn
i¼1
i6¼k
Xn
j¼1
j6¼l
Xn
k¼1
Xn
l¼1
Fik Ã Djl Ã Xij Ã Xkl (1)
Xn
j¼1
Xij ¼ 1 for all i ¼ 1:::n (2)
Xn
i¼1
Xij ¼ 1 for all j ¼ 1:::n (3)
Xij=1 if facility “i” is located/assigned to location “j”.
Xij=0 if facility “i” is not located/assigned to location “j”.
Fik is the flow between two facilities i and k.
Djl is the distance between two locations j and l.
Constraint 1 (Eq. 1) is a restriction that only one facility
can be located at one location, and constraint 2 (Eq. 2)
ensures that each location can only be assigned to one
facility. The objective is to minimize the total flow among
facilities i=1 to n and k=1 to n. As all indices are summed
from 1 to n, each assignment will be counted twice; hence
the need to multiply by 1/2.
2.2 Graph theory model
In the graph theoretic approaches each department or ma-
chine (ignoring the area and shape of the departments at the
beginning) is defined as a node within a graph network.
These rely on a predefined desirable adjacency of each pair
of facilities [16, 17]. In other words, it can be said that in
graph theoretic approaches, it is assumed that the desirabil-
ity of locating each pair of facilities adjacent to each other
is known. Like QAP approaches, unequal area problems of
even small size cannot be solved optimally [18]. Various pa-
pers have been published on this subject where different mod-
els and algorithms’ characteristics have been explored [19–
21]. A review of the results of graph theoretic approaches
can be found in Foulds [17] and Hassan and Hogg [16].
2.3 MIP model
MIP has received some attention as a way of modeling the
FLP. Montreuil [22] first formulated FLP as MIP where a
distance-based objective was used in a continuous layout
representation that was an extension of the discrete QAP.
Hegaru and Kusiak [23] developed a specialized case of
this MIP. Lacksonen [24] proposed a two-step algorithm
for solving the FLP while assuming variable area which
can solve a general dynamic facility layout with varying
departmental areas assuming that all are rectangular.
Lacksonen [25] then extended the proposed model to
deal with unequal areas and rearrangement costs. However,
the model could only be optimally solved for small prob-
lems. Kim and Kim [26] considered the problem of
locating input and output (I/O) points of each department
for a given block layout with the objective of minimizing
the total transportation distance. A new branch-and-bound
algorithm was proposed that seems to perform efficiently
even for large-size problems. However, the simultaneous
solution of the block problem and the I/O points layouts
has not yet been solved. Barbosa-Povoa et al. [27] pro-
posed a mathematical programming approach for the gen-
eralized facilities detailed layout problem.
A detailed MIP for FLP can be found in Montreuil [22].
Although this MIP approach holds much promise, cur-
rently only FLP of size six or less [18] are optimally
solvable. The objective is based on flow time rectilinear
distance between centroid of two departments.
3 Solution methodology
In this section various solution methodologies, e.g. exact
procedures, heuristics and meta-heuristics available to
solve facility layout problems optimally or near to optimal,
are discussed in detail. Exact procedures that can give
optimal solutions to facility layout problems are discussed
in Section 3.1. Section 3.2 briefly describes heuristic meth-
ods used to solve facility layout problems. Meta-heuristics
available to solve facility layout problems are given is
Section 3.3. Section 3.4 is devoted to artificial intelligence
approaches applied to solve the facility layout problems.
3.1 Exact procedure
Branch and bound methods are used to find an optimum
solution of quadratic assignment formulated FLP because
QAP involves only binary variables. Only optimal solu-
tions up to a problem size of 16 are reported in literature.
Beyond n=16 it becomes intractable for a computer to
solve it and, consequently, even a powerful computer can-
not handle a large instance of the problem.
426

3.2 Heuristics
A comprehensive investigation of the FLP literature
includes examining heuristics. Heuristic algorithms can
be classified as construction type algorithms where a
solution is constructed from scratch and improvement type
algorithms where an initial solution is improved. Con-
struction based methods are considered to be the simplest
and oldest heuristic approaches to solve the QAP from a
conceptual and implementation point of view, but the
quality of solutions produced by the construction method is
generally not satisfactory. Improvement based methods
start with a feasible solution and try to improve it by inter-
changes of single assignments. Improvement methods can
easily be combined with construction methods. CRAFT
[28] is a popular improvement algorithm that uses pair-
wise interchange. A survey of a few well known heuristics
which are popular as layout software are provided in
Table 1 along with the algorithm used. These heuristics are
classified as adjacency and distance based algorithms. For
instance, MATCH [29] and SPIRAL [30] are adjacency
based while CRAFT [28], SHAPE [31], LOGIC [32],
MULTIPLE [33], and FLEX-BAY [34] are distance based
algorithms (descriptions are not provided but interested
readers can refer to the cited papers).
The difference between these two algorithms lies in the
objective function. The objective function for adjacency
based algorithms is given as
max
X
i
X
j
rij
À Á
xij (4)
where xij is 1 if department ‘i’ is adjacent to department ‘j’
and else 0. The basic principle behind this objective
function is that the material handling cost is significantly
reduced if the two departments have adjacent boundaries.
The objective function of distance based algorithms is
given as
Min TCð Þ ¼ 1=2 Ã
Xn
i¼1
i6¼k
Xn
j¼1
j6¼l
Xn
k¼1
Xn
l¼1
Cik Ã Djl Ã Xij Ã Xkl
(5)
The underlying philosophy behind this objective func-
tion is that the distance increases the total cost of traveling.
Cik can be replaced by Fik depending on the objective.
Equation 6 is used as an objective function when the
facility layout is designed for multi-floor.
min
Xn
i¼1
i6¼k
Xn
j¼1
j6¼l
Xn
k¼1
Xn
l¼1
CikH Ã DjlH þ CikV Ã DjlV
À Á
ÃXij Ã Xkl
(6)
Where, CikH and DjlH stand for horizontal material
handling cost and horizontal distance, respectively. The
same meanings are applicable for CikV and DjlV but in
vertical directions.
3.3 Meta-heuristics
Various meta-heuristics such as SA, GA, and ant colony are
currently used to approximate the solution of very large
FLP. The SA technique originates from the theory of
statistical mechanics and is based upon the analogy be-
tween the annealing of solids and solving optimization
problems. Burkard and Rendl [42] derived SA for QAP. A
Table 1 List of facility layout packages [33, 35]
S.No References Name of package
1 Dr. Gordan Armour CRAFT
2 Seehof and Evans ALDEP
3 Dr. Moore James CORELAP
4 Michael P. Deisenroth PLANET
5 Teichholz Eric COMP2
6 Kaiman Lee COMPROPLAN COMSBUL
7 Robert C. Lee CORELAP8
8 Robert Dhillon DOMINO
9 Teichholz Eric GRASP
10 Dr. Johnson T.E. IMAGE
11 Dr. Warnecke KONUVER
12 Dr. Warnecke LAYADAPT
13 Raimo Matto LAYOPT
14 John S. Gero LAYOUT
15 Dr. Love R.F. LOVE*
16 Dr. Warnecke MUSTLAP2
17 Dr. Vollman Thomas OFFICE
18 McRoberts K. PLAN
19 Anderson David PREP
20 Moucka Jan RG and RR
21 Dr. Ritzman L.P. RITZMAN*
22 Dr. Warnecke SISTLAPM
23 Prof. Spillers SUMI
24 Hitchings G. Terminal Sampling Procedure
25 Johnson [36] SPACECRAFT
26 Tompkins and Reed [37] COFAD
27 Hassan, Hogg
and Smith [31]
SHAPE
28 Banerjee et al. [38] QLAARP
29 Tam [39] LOGIC
30 Bozer, Meller,
and Erlebacher [33]
MULTIPLE
31 Tate and Smith [34] FLEX-BAY
32 Foulds and Robinson [40] DA (Adjacency Based)
33 Montreuil, Ratliff
and Goetschalckx [29]
MATCH (Adjacency Based)
34 Goetschalckx [30] SPIRAL (Adjacency Based)
35 Balkrishnan et al. [41] FACOPT
* Indicates that the names of packages are based on author’s name
427

most recent survey of SA based facility layout papers is
tabulated in Table 2.
GA gained more attention during the last decade than
any other evolutionary computation algorithms; it utilizes a
binary coding of individuals as fixed-length strings over
the alphabet {0, 1}. GA iteratively search the global opti-
mum, without exhausting the solution space, in a parallel
process starting from a small set of feasible solutions
(population) and generating the new solutions in some
random fashion. Performance of GA is problem dependent
because the parameter setting and representation scheme
depends on the nature of the problem. Tavakkoli-
Moghaddam and Shayan [43] analyzed the suitability of
genetic operator for solving FLP. Table 3 provides recent
papers on GA based FLP.
Tabu search (TS) is an iterative procedure designed to
solve optimization problems. Helm and Hadley [44]
applied TS to solve FLP. The method is still actively re-
searched, and is continuing to evolve and improve.
Recently, a few papers have appeared where an ant
colony algorithm has been attempted to solve large FLP.
Talbi et al. [45] applied ant colony to solve QAP.
3.4 Other approaches
Other approaches which are also currently applied to FLP
are neural network, fuzzy logic and expert system.
Tsuchiya et al. [72] had proposed near-optimum parallel
algorithm for solving the QAP using two-dimensional
maximum neural network for an N-FLP. Knowledge based
expert system has also been applied by Malakooti and
Tsurushima [73], Abdou and Dutta [74], Heragu and
Kusiak [75] and Sirinavakul and Thajchayapong [76] to
Table 2 Survey of SA based
FLP papers
S. No. Reference Year QAP MIP Heuristic
1 Kirkpatrick et al. [46] 1983 √ Simulated annealing
2 Burkard and Rendl [42] 1984 √ Simulated annealing
3 Wilhelm and Ward [47] 1987 √ Simulated annealing
4 Kaku and Thomson [48] 1986 √ Simulated annealing
5 Connolly [49] 1990 √ Simulated annealing
6 Laursen [10] 1993 √ Simulated annealing
7 Tam [32] 1992 √ Simulated annealing
8 Heragu and Alfa [50] 1992 √ Simulated annealing
9 Kouvelis et al. [51] 1992 √ Simulated annealing
10 Jajodia et al. [52] 1992 √ Simulated annealing
11 Shang [53] 1993 √ SA and AHP
12 Souilah [54] 1995 √ Simulated annealing
13 Peng et al. [55] 1996 √ Simulated annealing
14 Meller and Bozer [56] 1996 √ Simulated annealing
15 Azadivar and Wang [57] 2000 √ Simulated annealing
16 Baykasoglu and Gindy [58] 2001 √ Simulated annealing
17 Misevicius [59] 2003 √ Simulated annealing
18 Balakrishnan et al. [41] 2003 √ √ SA and GA
Table 3 Survey of GA based
FLP papers
S. No. Reference Year QAP MIP Heuristic
1 Tam [39] 1992 √ Genetic algorithm
2 Banerjee and Zhou [60] 1995 √ Genetic search
3 Tate and Smith [34] 1995 √ GA
4 Kochhar and Heragu [61] 1998 √ √ Extension of GA
5 Islier [62] 1998 GA
6 Rajshekaran et al. [63] 1998 √ √ GA
7 Mak et al. [64] 1998 √ GA
8 Mckendall et al. [65] 1999 √ √ GA nested approach
9 Kochhar and Heragu [66] 1999 √ GA
10 Gau and Meller [67] 1999 √ √ GA
11 Azadivar and Wang [57] 2000 √ GA and simulation algorithm
12 Al-Hakim [68] 2000 GA
13 Ahuja [69] 2000 √ Genetic algorithm
14 Wu and Appleton [70] 2002 √ GA
15 Lee, Han and Roh [71] 2003 √ GA, Dijkstra algorithm
16 Balakrishnan et al. [41] 2003 √ √ GA and SA
428

Table 4 Survey of Papers
where other approaches are ap-
plied to solve FLP
S.
No.
Reference Year QAP MIP Heuristic Techniques
1 Dutta and Sahu [78] 1982 √ √
2 Murtagh et al. [79] 1982 √ √
3 Foulds [80] 1983 √ Graph theory
4 Herroelen and Vangils [81] 1985 Flow dominance theory
5 Fortenberry and Fox [82] 1985 √ Pair-wise exchange
6 Hammouche and Webster
[83]
1985 Graph theory (theoritical approach)
7 Foulds and Giffin [84] 1985 √ Graph theory
8 Green and Al_Hakim [85] 1985 √ √
9 Rosenblatt [86] 1986 √ Dynamic programming
10 Kaku and Thomson [48] 1986 √ Simulated annealing
11 Hassan et al. [31] 1986 √ √ Construction
12 Foulds et al. [87] 1986 √ Graph theory
13 Grobelny [88] 1987 √ Fuzzy approach
14 Evans et al. [89] 1987 √ Fuzzy set theory
15 Urban [90] 1987 √ √
16 Rosenblatt and Lee [91] 1987 √ √
17 Jacobs [92] 1987 √ Graph theory
18 Montreuil et al. [29] 1987 Graph theory
19 Hassan and Hogg [16] 1987 Graph theory
20 Grobelny [93] 1988 √ Fuzzy approach
21 Kaku et al. [94] 1988 √ √
22 Kumar et al. [77] 1988 Expert system, pattern recognition
23 Smith and Macleod [95] 1988 √ L. R. and B and B
24 Malakooti and Tsurushima
[73]
1989 Expert system, rule based
25 Malakooti [96] 1989 √ √
26 Heragu and Kusiak [97] 1988 √ √
27 Heragu and Kusiak [75] 1990 √ Knowledge approach
28 Abdou and Dutta [74] 1990 Expert system
29 Houshyar and McGinis [98] 1990 √ √ Cut approach
30 Al-Hakim [99] 1991 Graph theory
31 Heragu and Kusiak [23] 1991 √ √ Unconstrained opt.
32 Kaku et al. [100] 1991 √ √
33 Hassan and Hogg [101] 1991 √ Graph theory
34 Logendran [102] 1991 √ √
35 Burkard et al. [103] 1991 √ QAP_LIB
36 Camp et al. [104] 1992 √ √ Penalty function
37 Leung [105] 1992 √ Graph theory
38 Kaku and Rachamadya [106] 1992 √ √
39 Rosenblatt and Golany [107] 1992 √ √
40 Goetschalckx [30] 1992 √ √ Graph theory
41 Harmonosky and Tothero
[108]
1992 √ √ Pairwise, construction
42 Askin and Mitwasi [109] 1992 √ √
43 Balakrishnan et al. [110] 1992 √ √
44 Al-Hakim [111] 1992 Grapht theory
45 Lacksonan and Enscore
[112]
1993 √ B and B, cutting plane, D.P.
46 White [113] 1993 √ Branch and bound; convex
programming
47 Yaman et al. [114] 1993 √ √
48 Das [115] 1993 √ √
49 Raoot and Rakshit [116] 1991 √ Fuzzy based
429

tackle various issues related to FLP such as multi-
objective, the issue of optimizing material handling
equipment, etc. Kumar et al. [77] applied expert system
to handle qualitative constraints via a symbolic manipula-
tion structure. A survey of papers where these methodol-
ogies have been applied to solve FLP is given in Table 4.
4 Current trends and future scope of work
This section addresses the issues related to current trends in
the area of FLP and also future research directions. Section
4.1 deals with the currents trends in facility layout followed
by future scope of work.
4.1 Current trends
A summary of current trends during the last two decades is
reviewed here where more than 100 papers are classified as
per the facility classification scheme shown in Fig. 1.
Papers in various tables are given in chronological order
along with the solution methodology and formulation used
Table 4 (continued) S.
No.
Reference Year QAP MIP Heuristic Techniques
50 Raoot and Rakshit [117] 1994 √ Fuzzy based
51 Urban [118] 1993 √ √
52 Montreuil et al. [119] 1993 √ Graph theory, LP
53 Bozer et al. [33] 1994 √
54 Boswell [120] 1994 √ Graph theory based
55 Sirinaovakul [76] 1994 √ Knowledge based expert
56 Langevin et al. [121] 1994 √ √
57 Trethway and Footle [122] 1994 √
58 White [123] 1996 √ Lagrangian relaxation
59 Badiru and Arif [124] 1996 Fuzzy theory
60 Chiang and Kouvelis [125] 1996 √ Tabu Search
61 Watson and Giffin [21] 1997 √ Vertex splitting algo.
62 Meller [126] 1997 √ √
63 Lacksonan [25] 1997 √ √ Branch and bound
64 Bozer and Meller [127] 1997 √
65 Sarker et al. [13] 1998 √ √
Zetu et al. [128] 1998 Virtual reality(Theoritical approach)
66 Urban [129] 1998 √ Dynammic programming
67 Chan and Sha [130] 1999 √ √
68 Smith and Helm [131] 1999 Virtual reality (Theoritical approach)
69 Dweiri [132] 1999 Fuzzy based
70 Helm and Hadley [44] 2000 √ √ Tabu-search based
71 Knowles and Corne [133] 2002 √ Multi-obj. approach
72 Kim and Kim [134] 2000 √ √
73 Barbosa-Povoa et al. [27] 2001 √ √
74 Al-Hakim [135] 2001 Maximally planer graph
75 Wang and Sarker [136] 2002 √ √
76 Chan, Chan and Ip [137] 2002 √ √
77 Diponegoro and Sarker [138] 2003 √ √
78 Castillo and Peters [139] 2003 √ √ Extended distance based
Facility Layout
Static (or Dynamic) Layout
QIP, MIP, GRAPH THEORY
Solution Methodologies to
solve FLP
Models
Fig. 1 Classification scheme of facility layout problems
430

to model FLP that helps to provide a clear understanding of
various aspects of FLP.
4.2 Future scope of work
By observing all tables it has been found that research on
the FLP is not converging but is somewhat diverging. Now,
AI can be used apart from developing heuristic to solve
large sized FLPs; and more investigation into the multi-
objective function rather than single objective function is
required in order to include more relevant layout criteria.
Every two years the Material Handling Institute of
America [18], along with other sponsoring industries and
government agencies, organizes consortium on material
handling research where researchers are asked to present
their research. It is found that there is a lack of application
of concurrent engineering in FLP with respect to the choice
of the material handling system which in turn shows that
the current facility layout design is irrespective to the
choice of material handling system. It has been concluded
that the same facility layout design may not be appropriate
for all periods since the demand can never remain the same.
Hence, research should be towards a stochastic facility
layout rather than a static one.
There is emerging research into applying meta-heuristic
such as SA, GA and tabu search to solve large FLP. But, the
final result depends on the initial solution (or population)
taken. Therefore, more research is required to develop
good heuristic to generate good initial feasible solutions.
5 Conclusion
The trends of facility layout research over the past two
decades are presented in this paper. Recent facility layout
papers are identified and summarized along with the
solution methodology used. Various algorithms as well as
computerized facility layout software are addressed. A
further scope of work that is needed in the facility layout
area is also suggested.
Acknowledgements The communicating author wishes to express
his sincere thanks to Prof. B.J. Davies and anonymous referees for
their constructive suggestions which has led to considerable
improvement in the quality of this manuscript.
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