CIRP Annals - Manufacturing Technology 62 (2013) 483–486
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CIRP Annals - Manufacturing Technology
jou rnal homep age : ht t p: // ees .e lse vi er . com /ci r p/ def a ult . asp
A novel facility layout planning and optimization methodology
S. Jiang b, A.Y.C. Nee (1)a,b,*
a
b
Mechanical Engineering Department, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore
NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
A R T I C L E I N F O
A B S T R A C T
Keywords:
Manufacturing optimization
Augmented reality
Facility layout planning
This paper presents a novel factory planning system for real-time on-site facility layout planning (FLP).
Two facility layout planning modules are supported, viz., manual and automatic. In this system, a fast
modelling method has been developed where users can construct existing facilities as virtual primitive
models. A criterion and constraint definition mechanism is provided to define and customize the planning
criteria and constraints to suit specific requirements of different FLP tasks, and an Analytical Hierarchy
Process–Genetic Algorithm (AHP–GA) based optimization scheme is adopted for automatic layout
planning. Augmented reality (AR) is used to provide visualization of the layout process.
ß 2013 CIRP.
1. Introduction
Facility layout planning (FLP) refers to the design of the
allocation plans of the machines/equipment in a manufacturing
shopfloor. A well-designed manufacturing layout plan can reduce
up to 50% of the operating cost [1]. Traditionally, FLP is addressed
during the shopfloor design stage, i.e., prior to the construction of
the shopfloor. Algorithmic and virtual reality (VR) tools are the two
widely applied approaches. The algorithmic approaches focus on
the mathematical formulation of FLP using different models, e.g.,
the Quadratic Assignment Problem model, the Mixed Integer
Problem model, and the development of efficient algorithms to
solve these models, e.g., GA (genetic algorithm), SA (simulated
annealing), etc. However, due to the combinatorial complexity of
the FLP problem, it is almost impossible to find the best solution.
The VR-based tools provide an alternative approach to address FLP.
By creating a 3D virtual environment, the VR-based tools allow the
users to design layout plans manually based on their knowledge
and experience.
The development of the modern industry has posed new
challenges for FLP. To meet the fast-changing production targets,
enterprises nowadays need to reconfigure the existing shopfloor
layouts constantly to update their operations. FLP for existing
shopfloors have the following characteristics: (1) the presence of
existing facilities poses critical constraints; (2) the FLP task
normally tends to be small-scaled, e.g., removing and adding a
number of machines; and (3) the criteria used are often ad-hoc, and
specific to different tasks. The algorithmic approach and the VRbased tools are not efficient in handling these issues. Hence,
enterprises often settle with a less optimal layout plan.
By providing real-time information of the real environment, the
augmented reality (AR) technology [2–5] can provide a feasible
solution to FLP. Since the emergence of the AR technology, several
* Corresponding author at: Mechanical Engineering Department, Faculty of
Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore
117575, Singapore.
0007-8506/$ – see front matter ß 2013 CIRP.
http://dx.doi.org/10.1016/j.cirp.2013.03.133
AR-assisted FLP tools have been reported [6–8]. However, many of
these tools lack proper mechanisms to evaluate the layout plans
and this has greatly limited their usefulness.
This paper presents an improved AR-based methodology for FLP
of existing shopfloors. An on-site planning and optimization
method is proposed. By using the AR technology, information of
the existing facilities are obtained in real-time to formulate the
layout criteria and constraints. As shown in Fig. 1, the enhanced
sense of reality can facilitate the full utilization of the users’
experience, knowledge and intuition for identifying specific issues
to be addressed and examining the layout plans on-site. A system
named AFLP (AR-based FLP) has been developed to implement the
proposed methodology (Fig. 2).
In AFLP, an on-site modelling method has been developed to
obtain the geometric data of the existing facilities. These data,
together with the data that represent the facilities to be laid out,
are utilized to define the layout criteria and constraints calculated
in real-time for evaluation purposes. In the augmented shopfloor,
users can manipulate new facility layout intuitively until a good
evaluation has been achieved. In addition, an optimization scheme
is adopted to provide alternative layout plans.
Fig. 1. Using AR to facilitate FLP for existing shopfloors.
S. Jiang, A.Y.C. Nee / CIRP Annals - Manufacturing Technology 62 (2013) 483–486
484
In AFLP, this method is firstly applied to define a CS for the
shopfloor environment (Fig. 7(a)). The CS is established by
determining its origin and any two points on the x–y (or y–z, z–x)
plane. The location, pose and scale of the CS can be adjusted
manually. A global scaling factor SG is defined as in Eq. (1).
Sc ¼
Fig. 2. User interface of the AFLP system.
2. AFLP system architecture
The AFLP system (Fig. 3) consists of four modules, viz., the user
interaction module, the modelling module, the evaluation
module, and the optimization module. PTAM [9] is adopted for
camera tracking and a virtual space is augmented in the shopfloor
environment, where 3D models that represent the facilities to be
laid out are rendered. The modelling module adopts a fast realtime modelling method, enabling the users to rebuild the existing
facilities using primitive models, e.g., blocks and pillars. The
evaluation module provides a set of models and functions for the
users to define the FLP criteria and constraints. To meet the
specific requirements of different FLP tasks, the users can
customize the evaluation in terms of the number and the contents
of the criteria and constraints. During the manual planning
process, as the users manipulate the virtual models of the new
facilities, the evaluation module provides the real-time feedback
to help the users make decisions. For automatic planning, the
prioritization technique is used to combine the criteria to
formulate single objective optimization models and GA is used
to obtain optimized results. Among the layout plans obtained
from both planning scenarios, the users can choose the most
preferred plan as the final layout.
Evaluation module
New facilities
Criterion model
Criteria
Existing facilities
Constraint function
Constraints
Prioritization
schemes
User interaction
Optimization
On-site modeling
Inputting
AHP
Primitives
Transforming
GA
Manual planning
Automatic planning
Alternative layout plans
Fig. 3. Architecture of the AFLP system.
2.1. On-site modelling
For AR-based applications to achieve real-time interaction, the
reconstruction of the real environment is a crucial challenge [6,7].
In AFLP, a fast modelling method is developed to allow users to
rebuild real objects as primitives. In this method, a primitive model
is built by defining the key points, e.g., the vertices of a plane. In AR,
a key point is defined by calculating the 3D coordinate of a point in
the world coordinate system (CS). For a point X, given two imageto-world transformation matrices MA (Frame A) and MB (Frame B)
and the corresponding coordinates in the image CS, its coordinates
in the world CS can be determined. This process can be simplified if
the targeted point is located on a known plane, e.g., the x–y plane,
and by locating the point in one frame, the coordinates can be
obtained.
Ls
Lw
(1)
Lw is the length of the axis of the CS and Ls is the same length
measured in the system unit. The global scaling factor is used to
scale all the necessary measurements to the actual dimensions.
Through the definition of the key points, primitive models can
be constructed. In AFLP, four types of primitives are supported,
including planes, blocks, discs and pillars (a block/pillar can be
constructed by extruding a volume based on a plane/disc). This
modelling method is used to define the planning space and to
reconstruct the existing facilities. The planning space is a 3D
volume of the shopfloor containing all the free space and existing
facilities to be considered in FLP. To reconstruct an existing facility
in AR, the users can construct approximate primitive models and
refine them manually by translating, rotating, scaling, etc., until
they represent the facilities accurately.
2.2. Criteria and constraints
In this research, the criteria refer to the objectives of the FLP
tasks, e.g., the minimization of the material handling cost, and the
constraints are regarded as the restrictions for realizing ideal
layout plans, e.g., the physical interference between the facilities.
For FLP of an existing shopfloor, the criteria often tend to be
specific to the requirements of the tasks; the users may only be
able to identify and address the aspects for FLP when they are in
the shopfloor. To resolve this issue, the evaluation module provides
a set of mathematical models for the users to define the criteria and
customize their contents manually (Fig. 4). The following criterion
models (CM) are integrated:
a. CM#I: Data flow optimization (Eq. (2)) is used to model the data
flow optimization problems, which includes the optimization of
material handling cost, the personnel, the information flow, etc.
cij, dij and vij are the unit cost, the distance and the volume of the
data transferred from facility i to facility j respectively. Two
methods for distance calculation, viz., the Euclidean distance
and the rectilinear distance, are supported. cij and vij need to be
collected and input by the user.
CMI ¼ min=max
n
X
c i j di j v i j
(2)
i; j¼1
b. CM#II: Space utilization (Eq. (3)) is used to assess the 3D space
occupied by the group of facilities, which are selected by the
users. The measurement uses the ratio between the volume of
the bounding box that contains all the selected facilities (Vu) and
the volume of the design space (VDS).
CMII ¼ min
Vu
V DS
(3)
c. CM#III: Distance maximization/minimization (Eq. (4)) is used
to define distance-based criteria, e.g., maximum distances
between certain facilities, minimum distance for frequent
facility maintenance, etc. di is the distance between the facilities
considered (both the Euclidean and the rectilinear distances are
supported) and c is the cost per unit length which needs to be
collected and input manually.
m
X
CMIII ¼ min=max di c
(4)
i¼1
Besides the criterion models, the evaluation module provides a
set of constraint functions (CF) for the users to impose necessary
constraints on selected facilities. Unlike the definition of the
criteria, the constraints define the rules for the individual facility.
S. Jiang, A.Y.C. Nee / CIRP Annals - Manufacturing Technology 62 (2013) 483–486
Select a CM
Criterion model
New/existing facilities
Target facilities
Relevant data
Parameters
A new
criterion
Fig. 4. Procedure of defining a criterion.
The parameters used in the rules are stored as the constraint
information in the facility data. Each CF has a feedback action, e.g.,
cancelling the current movement command if the collision
detection is positive (Fig. 5). As the constraints are examined
for each frame, the feedback actions serve as real-time simulation
which can greatly facilitate the manual planning process. In AFLP,
the following constraint functions are provided:
a. CF#I: Collision detection is used to examine any possible
interference between the facilities. For each new facility to be
placed, its bounding box is formed based on the model of the
facility. During the planning process, if one of the vertices of the
bounding box is detected to be located within the bounding box
of another facility, collision is detected. The feedback action is to
cancel the current transformation command (Fig. 7(d)).
b. CF#II: Orientation constraint imposes restrictions on the poses of
the facilities, e.g., certain facilities have to be installed in a
specific orientation. To impose this constraint, the users need to
initialize the CF#II parameters in the facility data. During the
planning process, the following steps will be performed: (1)
calculate the rotation matrix r0 from the default orientation to
the required orientation; and (2) obtain the current orientation
matrix rt and calculate the rotation matrix rCF = rort1. The
feedback action is a rotation command to apply rCF to the facility
to achieve the correct orientation.
c. CF#III: Space constraint redefines the bounding boxes of the
facilities. When a facility is to be installed in a shopfloor, certain
space may be needed for purposes of maintenance, safety issues,
etc. This constraint is defined to allow the users to resize the
bounding box of a facility interactively.
d. CF#IV: Location constraint defines the valid regions for locating a
facility. To initialize the location constraint, the users need to
define a planar surface in the shopfloor, e.g., the floor, and the
contacting surface of the facility, e.g., the footprint. For manual
planning, the feedback action is to cancel the most recent
transformation commands. The location constraint is more
useful during an automatic planning process.
485
priorities to the criteria. Different weighting schemes can produce
layout plans with varied characteristics, which will be very
valuable for decision making.
As shown in Fig. 6, to initialize automatic planning, the users
will be prompted to make pair-wise comparison between the
defined criteria. The comparison results are then processed using
AHP for refinement and a weighting scheme is produced. Next, the
GA will be used to load the plan and use Eq. (5) to formulate a single
objective optimization problem.
m
X
C i C imin
min ai pi C
C imin
imax
i¼1
(5)
m is the number of the criteria defined by the users. For the ith
criterion, pi is the priority value; ai is 1 if the criterion is a
minimization problem or 1 if otherwise; Ci is the measurement
value of the criterion; Cimax/Cimin is the maximum/minimum value
that the criterion can achieve. During the first execution of the
optimization module, the algorithm will perform an initial run to
obtain estimated values for Cimax and Cimin.
Optimization module
Criteria data
First time?
No
Applying Pj
Prioritization
scheme j
AHP
Genetic algorithm
START
Layout plan Lj
Yes
Calculate Cimax
and Cimin
Fig. 6. The flow of the algorithm in the optimization module.
3. Implementation and case study
A simplified FLP task is conducted and illustrated in Fig. 7. In
this task, three new facilities are to be installed, namely, a lathe, a
bench drill press and a display monitor. Table 1 shows the
constraints to be imposed on the facilities, and Table 2 shows the
criteria to be considered in the task.
2.3. AHP–GA based optimization
During the manual planning process, human intuitiveness is
explored to facilitate the production of successful layout plans.
However, these layout plans may tend to be subjective and can be
further improved. In this context, the algorithmic optimization
module is developed to produce alternative layout plans. By using
the criterion models to define multiple criteria, the FLP problems
can be formulated as MADM (Multiple Attribute Decision Making)
models [10]. Effective approaches to MADM include the weightedsum approach, the Pareto ranking approach, etc.
In AFLP, a weighted-sum approach in the AHP–GA method is
adopted, where the weighting scheme allows the users to assign
Evaluation module
Facility data
Facility index
Constraint info.
Geometric info.
Processing
Computing unit
Positive?
No
Next frame
Yes
Feedback action
Fig. 5. The working mechanism of the constraint function.
Fig. 7. Using AFLP to address the FLP task.
S. Jiang, A.Y.C. Nee / CIRP Annals - Manufacturing Technology 62 (2013) 483–486
486
Table 1
Constraints to be imposed on the facilities.
Display monitor (Facility#0)
CF#II orientation constraint: the base facing the floor.
CF#IV location constraint: on the walls.
CF#I collision detection.
Bench drill press (Drill#2/Facility#1)
CF#II orientation constraint: the back facing the walls.
CF#IV location constraint: on top of wooden bench.
CF#I collision detection.
Lathe (Facility#2)
CF#III space constraint for operation purposes.
CF#IV location constraint: on the ground floor.
CF#I collision detection.
Table 2
Three criteria required in the task.
Criterion
C1: minimize material
handling cost
(pcs/day/unit cost)a
C2: minimize personnel
flow (persons/day)
C3: minimize space
between facilities
a
Contents and data (collected a priori)
From Drill#1/Drill#2 to the lathe: 80/3
From the lathe to the inspection room: 100/2
From Drill#1 to the inspection room: 10/2
From Drill#1/Drill#2 to the lathe: 50
From the lathe to the inspection room: 10
From Drill#1 to the inspection room: 30
The space occupied by the two bench drill
presses and the lathe
The unit cost is a relative value.
In the augmented shopfloor environment, the enhanced sense
of reality helps the users to identify an additional layout issue as
Criterion#4 (C4): the display monitor is to be located near the
power supply. Criterion models are used to define these four
criteria: CM#I for C1 and C2, CM#II for C3, and CM#III (with the
rectilinear distance) for C4.
Table 3
Quantitative comparison of the two layout plans.
Criterion (unit)
Weight
Criterion model
Plan A
Plan B
C1
C2
C3
C4
0.34
0.34
0.21
0.10
CM#I
CM#I
CM#II
CM#III
3267.29
493.29
0.35
10.04
2254.13
417.67
0.27
7.75
(unit cost)
(pers. m)
(N.A.)
(m)
During the manual planning process, the evaluation module
provides updated quantitative evaluation of the layout plans.
Simultaneously, the AR environment enables the users to employ
their intuitiveness to assess the same plans from qualitative aspects.
With the aid of real-time information, the users’ knowledge and
experience are well utilized. After a manual design is achieved (Plan
A), the users invoke AHP and a weighting scheme is produced as: C10.34, C2-0.34, C3-0.21 and C4-0.10. The optimization module loads
the priority values and generates an alternative layout plan (Plan B).
The two plans are rendered on-site and examined by the users. As
shown in Fig. 8, there is a change in the location of F2 between Plan A
by manual planning and Plan B using AHP–GA. This change has
created an impact on the material and the personnel flow, which can
consequently lead to improvements in Plan B (Table 3) as a result of
the algorithmic optimization performed.
Automatic planning can typically outperform manual planning
with the use of AHP–GA, whereas manual planning can incorporate
users’ experience, e.g., personal preference and heuristics, which
automatic planning has difficulty in addressing. The decision on
the selection of the final plan lies with the users.
4. Conclusion
This paper presents an AR-based system tailored for FLP for
existing shopfloors. An on-site modelling method has been
developed for reconstructing the existing facilities so as to obtain
their geometric information. The evaluation module adopts this
information to define the criteria and constraints, and the users can
customize the evaluation content to better meet the needs of the
FLP tasks. The defined criteria and constraints are used to support
both the manual planning and the automatic planning to facilitate
decision-making. The effectiveness of the system is demonstrated
using a case study. The weaknesses of the AFLP system include the
following aspects. Firstly, it can become ineffective for large-scaled
and complex layouts, e.g., FLP with a large number of facilities.
Manual planning can become equally challenging and difficult.
Further, to realize the full potential for assisting manual planning,
user expertise and experience on FLP is still needed. In addition,
different optimization algorithms can be integrated to generate
alternative layout plans to help the users in making decisions.
Future research will be conducted to address these issues.
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Fig. 8. The layout plans obtained from the two planning scenarios.
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