Space zoning concept-based scheduling model for repetitive construction process.
Cho, Kyuman ; Hong, Taehoon ; Hyun, Chang Taek 等
Reference to this paper should be made as follows: Cho, K.; Hong,
T.; Hyun, C. T. 2013. Space zoning concept-based scheduling model for
repetitive construction process, Journal of Civil Engineering and
Management 19(3): 409-421.
Introduction
Along with the reduction of construction cost, the effort to reduce
construction duration has been a long-studied subject in the
construction industry. Methods like fast track, concurrent engineering,
and phased construction are continuously being studied to reduce the
duration of a construction project. Aside from studying macro-level
management techniques that mainly dealt with the high-level works in the
construction work breakdown structure (i.e. foundations, structure,
finishing works, etc.), research has been consistently conducted to
reduce the duration of a construction project and to improve
productivity by "planning" or "zoning" of a working
space in terms of micro-level. Workspace zoning results in effective
construction as it can reduce not only the construction duration through
the iteration and overlapping of the related activities but also the
congestion and interference among the work tasks or resources in a
project (Akinci et al. 2002; Cheung, O'Connor 1996; Guo 2002; Li,
Love 1998; Thabet, Beliveau 1994; Tommelein, Zouein 1993; Winch, North
2006; Yeh 1995; Zouein, Tommelein 2001). In spite of such advantages,
the existing studies focus on the development of a methodology for the
efficient implementing of space zoning, such as securing and efficiently
distributing a work space. Moreover, while a few research have tried to
develop a scheduling model that integrates space zoning concept into the
existing scheduling method (e.g. Critical Path Method, CPM and line of
balance, LOB), it turns out that there are several limitations for them
in terms of representing a characteristic of space zoning (Akinci et al.
2002; Cho, Eppinger 2005; Guo 2002; Smith, Morrow 1999; Thabet, Beliveau
1994; Winch, North 2006; Zouein, Tommelein 2001). Therefore, this study
aims at developing a Space zoning Concept-based scHEduling ModEl
(SCHEME) for repetitive construction processes that can overcome the
limitations of the existing network-based scheduling methods (i.e. CPM
and PERT) and LOB method for space zoning.
To build such a model, this study was conducted in three phases.
First, literature review including the concept of space zoning was
analyzed. Second, based on the results from literature review, SCHEME
for effective space zoning was developed. For developing the model, this
research adopts discrete-event simulation methods, so that the model can
represent the characteristic of space zoning appropriately. Finally, the
model that was developed in the second phase was applied to steel
structure construction, a representative repetitive work in high-rise
buildings that actively use space zoning, in order to verify and examine
the developed model.
1. State of the art
In general, space zoning is applied to repetitive construction
operations to reduce duration and minimize interference among the
different works involved (Akinci et al. 2002; Guo 2002; Thabet, Beliveau
1994; Winch, North 2006; Zouein, Tommelein 2001). When as many as i
activities in precedence relations are performed on one floor (i.e.
floor 1) with a traditional construction method (i.e. nonspace zoning),
as shown in Fig. 1(a), the construction time can be expressed by the sum
of the duration of each activity on floor 1. Using space zoning,
however, the total duration becomes shorter than the duration of the
case without space zoning, as shown in Figures 1(b) and 1(c). That is,
if one floor (i.e. floor 1) is divided into j zones and each activity is
performed by iteration and overlapping, the total duration is reduced
compared to the duration in which each activity proceeds in an orderly
manner. Figures 1(b) and 1(c) Show the concepts of ideal and actual
space zoning concept based on the combination of the durations of the
activities. If the duration of each activity is not identical on each
zone, as shown in Fig. 1(c), the float time occurs, in which the work of
a following zone does not start right after the work in the previous
zone ends.
[FIGURE 1 OMITTED]
The concept of such space zoning can be explained by the status of
the activities that occurs in the time flow. As shown in Fig. 2, when a
floor is divided into four zones and when four activities exist on the
floor, the work follows from "state 1" in Fig. 2(a) to
"state 4" in Fig. 2(d). In other words, state 1 is the state
in which activity 1 in zone 1 has been completed, and to go to state 2,
activity 1 moves to the work in zone 2, as indicated by the arrow. State
2 is the state in which activity 1 is completed in zone 2, and activity
2 is completed in zone 1. Activities 1 and 2 move to zones 3 and 2,
respectively. Therefore, using the space zoning concept, activities are
repeated in each zone. Moreover, the activities in the divided zones are
performed simultaneously, causing overlap among them.
There are a number of previous studies on space zoning. Winch and
North (2006) developed a decision-making support tool via the
identification and arrangement of work spaces for efficient
construction. Guo (2002) presented a solution to productivity loss due
to the constraints and path interference in a work space. Akinci et al.
(2002) discussed the types of time-space conflicts due to such space
constraints and, based on such discussion, proposed a method that
determines the precedence of activities. Finally, Zouein and Tommelein
(2001) analyzed the trade-off between time and space through the
adjustment of the activity duration and the proposed space-scheduling
algorithms, by adjusting the starting time of each activity. These
studies focus on the development of a methodology for the efficient
performance of space zoning, such as securing and efficiently
distributing a work space. There are few studies, however, on reducing
the construction duration and improving productivity through space
zoning. Furthermore, despite the fact that space zoning often focuses on
repetitive works in the actual construction field, previous studies did
not adequately address the problems of iteration and overlapping of
activities.
[FIGURE 2 OMITTED]
Meanwhile, network-based project-scheduling methods (i.e. PERT or
CPM) can model such iteration and overlapping characteristics only to a
limited extent (Cho, Eppinger 2005; Smith, Morrow 1999; Thabet, Beliveau
1994). In other words, the iteration of the works that occur in the
divided zone and on each floor cannot be effectively expressed by the
existing CPM or PERT method. Moreover, the existing CPM or PERT method
cannot effectively solve the overlapping of activity in the divided zone
and on each floor, which are caused by concurrent operations of
activities. Moreover, as pointed out by Smith and Morrow (1999),
network-based scheduling methods lack the function of predicting and
managing the duration and productivity change based on key scheduling
points (e.g. resource constraints, logical-precedence relationship, and
stochastic task duration).
The LOB method has been developed for more efficient schedule
management of projects with repetitive iteration and overlapping
processes (Arditi, Albulak 1986; Arditi et al. 2001, 2002; Halpin, Riggs
1992). Despite numerous studies for LOB, it is hard to apply the LOB
concept to space zoning scheduling due to following aspects: (1) space
constraints and (2) interaction among unit works:
(1) According to the existing research about LOB, basically LOB
pursues the optimization (or balancing) among the unit works through
consideration of production rate for those works, so that the unit works
can be conducted smoothly in terms of the minimization of the idle time
on each work. Thus, there is a limitation for recognizing space
dependencies in LOB, as appointed by Arditi et al. (2002). However,
space zoning method has been developed in optimization depending on
space constraints. Namely, in space zoning, the scheduling should be
established according to how the most effective production rate could be
achieved for the given space constraints.
(2) In LOB, firstly all production rates for each work should be
calculated, respectively, and then each of them could be accumulated as
an entire schedule. As such, it is hard to identify the influence
between each work, when the changes on the amount of the input resource
for each work may occur. Consequently, there is a limitation for
updating project schedule by increasing the production rate of selected
activities (Arditi et al. 2002). However, the space zoning concept in
this paper could identify the effect of resource variation on the
production rate of particular unit work, and furthermore it is available
to easily update the influence of such a unit work to other unit works
in terms of project schedule.
Additionally, the LOB technique is one of the deterministic
methodologies, and therefore, is (1) limited in assuming the uniform
production rate of each activity (Arditi et al. 2001), and (2) lacking
in the consideration of uncertainty, which is unavoidable in
construction works.
As shown above, it turns out that the existing scheduling methods
have a limitation for representing the characteristics of space zoning,
such as (1) iteration and overlapping and (2) resource and space
constraints. Thus, there is a need for developing a scheduling model
which has an approach for overcoming above constraints.
2. Development of SCHEME
Discrete event simulation is effective in calculating the
productivity and duration of repetitive construction processes, and in
fact, various successful applications of discrete-event simulation can
be witnessed in the construction field (Halpin, Riggs 1992; Hong et al.
2011; Lee et al. 2009). In other words, simulation methodologies have
been applied to construction projects to measure the productivity of
repetitive processes based on the resource constraints. Such
methodologies have considered iteration and overlapping, which are
difficult issues that the existing network-based CPM or PERT methods can
address (Adler et al. 1995; Browning, Eppinger 2002; Taylor III, Moore
1980). Unlike the LOB technique, the simulation methodology makes it
easy to determine changes in project schedule depending on the changes
of the resources and duration at the activity level, and to resolve the
uncertainty of a project. Therefore, based on the space zoning concept
as explained in Figures 1 and 2, SCHEME was developed in this study
adopting simulation techniques, especially modeling elements of CYCLONE
which is one of the well-recognized discrete-event simulation methods.
For more information regarding CYCLONE, including its modeling elements,
please refer to Halpin and Riggs (1992).
2.1. Model framework
2.1.1. Precedence constraints
The logical relationship among activities (i.e. precedence
relationship) can be modeled using the COMBI and QUE elements of
CYCLONE. The COMBI element of CYCLONE can start only after the precedent
conditions are met (Halpin, Riggs 1992). That is, as shown in
"A" (nodes 2, 5, 9, and 10) in Fig. 3, the work of activity 1
is a COMBI element (node 2). Therefore, in order to implement it, the
precedent conditions (i.e. nodes 1, 3, and 4) should have been prepared.
In addition, since activity 2 (node 10), which is a succeeding work of
activity 1, is also a COMBI element, the three precedent conditions
(nodes 9, 11, and 12) should have been prepared, in order to initiate
activity 2. Therefore, once the model begins, activity 2 (node 10) can
begin only after the "zone available" defined in node 1
completes activity 1 (nodes 2 and 5) and is in a "ready" state
after arriving at node 9. In this study, the precedence relationship
among activities is modeled based on the defined zone. Meanwhile,
"Done (nodes 5 and 13)" is a dummy node set for the precedence
relationship of a CYCLONE model and does not affect the measurement of
the actual duration and productivity.
[FIGURE 3 OMITTED]
2.1.2. Iteration
Iteration in space zoning occurs in two types: (1) the type in
which an activity is repeated while moving to the divided zones, and (2)
the type in which an activity is repeated by the floor. "B"
(nodes 9-16) in Fig. 3 is a model of the process in which activity 2 is
repeated by the number of zones defined in the model. In other words, by
connecting node 13 to the work loop (i.e. the path from node 13 to node
15, and returning to node 9), the model allows a repetitive work as many
times as the number of zones (i.e. the number indicated in node 1), each
of which is to be "ready" in node 9 after the completion of
activity 1. An accumulator is also used as the final node of the model,
as shown in Fig. 3, which is connected to node 1 so that activities are
repeated by the floor. In other words, this accumulator means that
activity n, the final activity, is completed in work zone 1, and as it
is connected to node 1 by the accumulator, activity 1 repeats their work
on zone 1 of the next floor.
2.1.3. Expansion of the model framework
As shown in Fig. 3, the developed SCHEME has the following
structures: (1) according to the flow of the "zone" resource
presented in node 1 as explained in "Precedence Constraints,"
the precedent and subsequent works were performed, and (2) as explained
in "Iteration," the model repeats each work cycle (i.e.
"B" in Fig. 3), depending on the number of zones. Furthermore,
based on the user-defined number of work activities, the unit module of
the activity cycle, expressed as "B" and "C" in Fig.
3, can be "added on" flexibly according to the order of the
activities. In other words, if there is a total of four activities, the
activity cycle will have four unit modules, such as "B" or
"C" in Fig. 3. In addition, if there are additional work tasks
aside from the work (node 2) and movement (node 7) according to the
content of the activity, the activity cycle can be adjusted based on the
work loop part (i.e. nodes 6-8 or nodes 14-16) so as not to damage the
"precedence relationship".
2.2. Model constructs
2.2.1. Resource constraints
As explained with the space zoning concept and precedence
constraints, only after the preparation of the "space" with
the completion of the precedent works, the subsequent work could be
initiated. Therefore, the developed model considered "space"
as a core resource for running the model. As the number of input
activities and the duration of each activity in each zone are adjusted
according to the number of zones in this "space," the work
space in the developed model is a very important resource. As shown in
"D" in Fig. 3, the resources in the developed model are
defined as the crew or equipment (nodes 4 and 12) and as the material
(nodes 3 and 11) for each activity, as well as the space (node 1). The
"*" of "D" in Fig. 3 indicates the number of each
resource. For example, if there are four "*" in node 1, it
means that one floor has been divided into four working zones.
2.2.2. Activity duration
The duration of the activities in the CYCLONE model is described in
the COMBI and NORMAL elements (Halpin, Riggs 1992). As shown in Fig. 3,
nodes 2 and 7 are "activity working time" and "the
transition time to the next zone" on cycle of activity 1, and node
5 is a dummy variable for the aforementioned "precedence
constraints." According to Halpin and Riggs (1992), such activity
duration can be calculated using various methods as well as past
experience, estimates, the deterministic value by experts, and the use
of prediction models. A more detailed measurement process of duration
will be explained in "MODEL APPLICAION."
2.3. Model implementation
2.3.1. Calculating cycle time
As shown in Figures 1 and 2, the space zoning results in the cyclic
repetition of activities in the divided zones. Thus, the cycle time per
floor can be calculated by adding the early starting time (i.e.
[EST.sub.i1]) of the final activity and the time that it takes the
activity to work from zone 1 to zone j, as in Fig. 1(b) and 1(c).
Moreover, in the normal condition (i.e. Fig. 1c), a float time may exist
between zones. Therefore, one cycle time per floor with both i of
activities and j of zones ([CT.sub.ij]) can be estimated using the
following equation:
[CT.sub.ij] = [EST.sub.i1] + [j.summation over (n=1)][D.sub.in] +
[j.summation over (n=1)][F.sub.in], for n = 1 to j (1)
where [EST.sub.i1] = the early starting time of activity i in zone
1, [D.sub.in] = the duration of activity i in zone n (for n = 1toj), and
[F.sub.in] = the float time of activity i between the completion time of
zone n and the starting time of zone n + 1(for n = 1 to j). Meanwhile,
as shown in Fig. 1, [EST.sub.i1] is the time that it takes activity i to
start the work in zone 1, which can be expressed as the sum of the
working durations from activity 1 to activity i - 1 in zone 1.
Therefore, ESTU in Eqn (1) can be calculated using the following
equation:
[EST.sub.i1] = [T.sub.0] + [[i-1].summation over (n=1)][D.sub.n1],
for n = 1 to i - 1, (2)
where [T.sub.0] = the EST of activity 1 in zone 1, and [D.sub.n1] =
the duration of activity n in zone 1 (for n = 1 to i - 1). As shown in
Fig. 1, the float time in Eqn (1) is the delay time of activity i during
the working process of each zone. Therefore, the float time is the
difference between the completion time of zone j - 1 and the starting
time of zone j in each activity. Therefore, it can be calculated using
the following equation:
[j.summation over (n=1)][F.sub.in] = [[j-1].summation over
(n=1)]([ST.sub.in+1] - [FT.sub.in]), for n = 1 to j - 1, (3)
where [ST.sub.in+1] =the starting time of activity i in zone n + 1,
and [FT.sub.in] =the finishing time of activity i in zone n. The
duration of each activity on the developed model (i.e. Fig. 3) is
defined in the COMBI and NORMAL elements of each activity cycle.
Moreover, the "float time" is calculated by the combination of
the QUE and COMBI elements. For example, as explained in Precedence
Constraints, only when precedent activity 1 completes and zone 1 is
"ready" in node 9, the subsequent activity 2 should be begun.
Therefore, if the duration of activity 1 becomes delayed, then activity
2 will be in a queue state, and float time will occur. The model in this
study was developed in such a way that the waiting time becomes the
float time as expressed in Eqn (3).
2.3.2. Finding the optimal cycle time
As shown in Fig. 1(b), an ideal space zoning means that, due to the
duration ([D.sub.ij]) of each activity identical to the others, the flow
of work on each zone becomes smooth, without a float time (i.e.
[[summation].sup.j.sub.n=1][F.sub.in] [approximately equal to] 0).
Meanwhile, [D.sub.ij] generally changes based on the amount of input
resources (Chang et al. 2007; Cho et al. 2011; El-Rayes, Moselhi 1998;
Hyari, El-Rayes 2006). Therefore, in order to achieve an ideal zoning
space, the input resource into each activity should be adequately
planned. The developed model calculated the optimal cycle time based on
the change in the resources. That is, by examining the change in the
cycle time while changing the amount of the resources included in the
model, the optimal cycle time of the established model can be
calculated. If there are i activities in zones and the equipment and
crew input in each activity are defined as [E.sub.1] to [E.sub.i] and
[C.sub.1] to [C.sub.i], respectively, the random resource combination
(RC) input into this construction operation can be expressed as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (4)
where [RC.sup.x] = the xth random RC; [C.sup.x.sub.i] = the xth
random crew number of activity i; and [E.sup.x.sub.i] = the xth random
equipment number of activity i. If it is assumed that the cycle time
([CT.sub.ij]) expressed in Eqn (1) is a functional formula f(x) and
"x" of random RCs ([RC.sup.x]) in Eqn (4) are inputted to
f(x), the duration changes due to the change in the resource quantity in
each activity, moreover which results in a change in the start and
complete time of each activity. Finally, the three variables (EST,
duration, and float time) in Eqn (1) change. Thus, the cycle time at
that point is calculated as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (5)
where [CT.sup.x] = cycle time by the xth random RC.
If one of the RCs, [RC.sup.k], shows the minimum cycle time value
among the various [CT.sup.x] values produced by Eqn (5), it turns out
that the RC [RC.sup.k] makes not only the flow of work on each zone
smooth, but also a float time minimized. Therefore, the minimum value of
cycle time and the equivalent RC could be the "optimal
solution" to which efficient space zoning is applied.
3. Model application
3.1. Case introduction
SCHEME was applied to the steel structure construction, where the
space zoning concept is often attempted to reduce the construction
duration of high-rise buildings. The case analyzed for the application
of the model to the steel structure construction is a high-rise building
with 67 floors and six underground floors, standing 263 m high and has a
total area of 223,146 [m.sup.2]. The building was completed in December
2003. The center of the building consists of steel-reinforced concrete
core walls, and the steel structure method was used for the slave. As
the shapes and amounts of the materials in all the zones were very
similar to one another, as shown in Table 1, the space zoning for the
steel structure construction was planned to three zones per floor. The
construction was composed of three activities (i.e. activity 1: column
erection; activity 2: girder and beam installation; activity 3: deck
plate installation), and they were iterated in each segmental zone as
well as each floor. On the steel structure construction of the high-rise
building analyzed, column erection, one of the three activities, was not
constructed floor-by-floor; as in those constructions of a normal
high-rise building, but constructed by "tiers (i.e. sets of three
or four floors each)." Moreover, based on these erected columns,
activity 2 (i.e. girder/beam installation) and activity 3 (i.e. deck
plate installation) were performed. From the ground level, the analyzed
building consists of a total of 24 tiers, from which 21 data-sets (i.e.
21 cases in Table 2) with similar sizes could be collected for this
study. Using these data-sets, the durations of the COMBI and NORMAL
elements, which constitute the model, were calculated, and the number of
input resources was determined.
3.2. Development of SCHEME for steel structure construction
Based on Fig. 3, SCHEME for steel structure construction, as shown
in Fig. 4, was developed in this study, using the results of the case
study. Since the analyzed case had three divided zones, three zone
resources were defined in node 1, as shown in Fig. 4. The case analysis
result shows that two crew were allotted to each activity on average
(i.e. one crew for installation and one for bolting or welding), as
described in nodes 4, 12, and 18, respectively. Meanwhile, lifting of
the materials (i.e. column, girder/ beam, and deck plate) to be inputted
in the analyzed case was performed at night, to reduce the work load of
a tower crane and avoid work interference from a tower crane. Moreover,
since the cycle time of the steel structure construction process in the
case study was calculated based on nine hours working per day, the
lifting process was not included in the model in Fig. 4.
[FIGURE 4 OMITTED]
For the model developed to represent the condition of the actual
site effectively, it is very important to clearly define the duration of
each activity (AbouRizk et al. 1994; Halpin, Riggs 1992). As shown in
Table 2 which shows (1) the most likely duration for conducting each
activity on one zone; and (2) cycle time for finishing a steel
construction work per one tier (i.e. time during which all activities
are iterated three times), column erection (node 2) is average to
consume 7.52 hours while girder/beam installation (node 10) and
deckplate installation (node 20) are average to consume 7.74 and 5.39
hours, respectively. Using these data-sets, the durations of nodes 2,
10, and 20 were calculated. In other words, once the distribution of 21
data-sets was analyzed using the result of the analysis, the duration of
each activity was calculated based on triangular distribution.
Triangular distribution is not largely affected by the number of sample
data, and its calculation method is simple, thereby making data
collection easy and accurate (Back et al. 2000; Moder et al. 1983; Hong
et al. 2011; Hong, Hastak 2007). For example, Table 3 shows the duration
data-set for simulating the case 2 in Table 2. Since the stochastic
method cannot be used to calculate the transition time from one zone to
the next (i.e. nodes 7,15, and 24), the deterministic value based on the
interviews with field managers was used instead. As described earlier,
since nodes 5, 13, and 21 were dummy nodes set for the precedence
relationship of the model, the duration of these dummy nodes was set to
a minimum (i.e. 0.0001 hour) so it would not affect the actual total
cycle time.
3.3. Model validation
It is crucial to examine whether the model developed reflects the
actual steel structure construction well. Thus, the model was verified
based on two aspects. First, whether the process of model using space
zoning concept and the one of actual construction can run identically or
not was examined by chronologically analyzing the events that had been
completed during the simulation operation. Second, the cycle time
resulting from the simulation of the developed model was compared to the
cycle time from the actual case.
3.3.1. Chronological list
Fig. 5 shows the simulation result from the developed model based
on actual case 2 (i.e. data-set in Table 3), in terms of completing
steel construction work on one zone. Fig. 5 chronologically listed the
events that were completed during the simulation, in terms of the COMBI
(i.e. nodes 2, 7, 10, 15, 20, and 24) and NORMAL (i.e. nodes 5, 13, and
21) elements with defined durations. Through the analysis of the
initiation and completion of the events, it was examined whether the
operation of the developed SCHEME is identical to the actual work
process. The key events in chronological order are as follows:
(1) Works on zone 1: as shown in Fig. 5, once the simulation
started, "column erection (node 2)" was working on zone 1
until 6.9 hours (i.e. chronological list 1), and then "girder/beam
installation (node 10)" continuously was proceeding until 13.8
hours (i.e. chronological list 4), as shown in "A" in Fig. 5.
Thereafter, "deck plate installation (node 20)" was working on
zone 1 until 18.7 hours (i.e. chronological list 10-12);
(2) Works on zone 2: after "column erection (node 2)"
finished on zone 1, that work was initiated on zone 2 at 7.2 hours (i.e.
chronological list 7), as shown in "B" in Fig. 5. Then
"girder/beam installation (node 10)" began on zone 2 at 16.3
hours (i.e. chronological list 10-12);
(3) Works on zone 3: after "column erection (node 2)"
finished on zone 2, that work on zone 3 started at 16.6 hours (i.e.
chronological list 10-12). As shown in "C" in Fig. 5, during
the time from 16.6 hours to 18.7 hours, all three works were
simultaneously working on zone 1, 2, and 3, respectively;
(4) Movement between each zone: once "column erection (node
2)" finished, the labor for column erection was moving from zone 1
to zone 2 until 7.2 hours (i.e. chronological list 2 and 3). And this
labor crew started to move from zone 2 to zone 3 at 16.3 hours, after
finishing the column erection at zone 2 (i.e. chronological list 8 and
9). Likely, the labor for girder/beam installation moved from zone 1 to
zone 2 at 13.8 hours (i.e. chronological list 5 and 6).
[FIGURE 5 OMITTED]
The simulation results clearly demonstrate that in terms of
iteration and overlapping, the work process of the developed model has
been identical with the work process using space zoning, as shown in
Figures 1 and 2. Furthermore, it can be verified that the developed
model runs identically with the work process of the actual case.
3.3.2. Comparison between the actual data and the simulation result
Based on the previously collected 21 data-sets, the reliability of
the developed model was verified by comparing: (1) the cycle time of
actual case, and (2) the simulation result from the developed model, in
terms of cycle time for finishing a steel construction work on each
tier. Since there is no need to estimate the optimized cycle time in the
process of comparing two values, the process explained in Eqns (4) and
(5) was not performed. As shown in Table 2 and Fig. 6, the average value
of the actual working cycle time was 38.29 hours, while the average
cycle time from simulation result was 35.85 hours. The comparison of the
cycle time of the actual case and the cycle time estimated by the model
showed that (1) the average difference between the two values was 2.44
hours, and (2) the average prediction power of the developed model was
93.89% (i.e. 93.89 = 100-6.11, refer to Table 2). Therefore, it is
determined that the developed model predicts actual situations well. In
addition, generally since the inputted resources in the simulation model
were distributed ideally as the simulation progressed, the duration by
the simulation model became smaller than that in the actual case
(Halpin, Riggs 1992; Van Slyke 1963). As shown in Table 2 and Fig. 6, it
turns out that the simulation results are smaller than the values from
the actual case. Finally, these comparisons reveal that the model
developed in this study is shown to be reliable.
3.4. Optimizing cycle time
The developed model can easily predict the construction duration
and present the optimal cycle time with variations in input resources.
Table 4 shows, for example, the explanation based on case 2 of Table 2
and 3. To calculate the optimal cycle time, which was explained mainly
by Eqns (4) and (5), the variation scope of the crew resources that were
inputted into the developed model was set to 1-5. Meanwhile, with
respect to reflecting the condition of the actual case, the number of
zones was fixed to three zones while examining the change in duration
based on the change in the crew resources. Since the variation scope of
each crew was set from 1 to 5, a total of 125 RCs [i.e. RC in Eqns (4)
and (5)] could be produced (i.e. 125 = 5 x 5 x 5). Shown in Table 4 is
the result of the examination of how duration changed according to 125
RCs. The result shows that the optimal duration at the 48th RC was
23.5017 hours. Once the developed model is implemented, the cycle time
could be mainly determined by durations for each work and idle time for
each labor crew. This aspect does not guarantee that the more resource
is used, the less cycle time is, because it would be possible to yield
more idle time by more resource. This principle can address how the 48th
RC could be selected as an optimal solution, even though the resource
amount of the 48th RC is lower than one of 125th RC. Therefore, when two
crews in activity 1, five crews in activity 2, and three crews in
activity 3 were distributed in the analyzed steel structure construction
case, the most efficient construction in terms of duration could be
undertaken.
3.5. Effect of model application
To observe the effect of the model application, the model was
examined in terms of the cycle time for constructing one floor. Based on
the other steel structure construction case, the differences in cycle
time in two cases were analyzed: when space zoning was applied to the
work, and when it was not. The analyzed case was an office building with
30 stories from the ground, on which space zoning was not applied. From
22 floors with similar amounts of work, the 22 data-sets including the
input resource and duration of the steel structure construction per
floor were collected. Moreover, based on the conditions of the analyzed
case (i.e. activity duration and resource input quantity), the model in
Fig. 4 was revised, and the simulation results and the actual cases were
compared.
Shown in Fig. 7 is the result of the comparison. The average
duration of the construction of one floor in the actual case was 53.07
hours. When space zoning was applied to the case with identical
conditions (i.e. the simulation results), the average duration of the
construction of one floor was 31.92 hours. Therefore, it was determined
that space zoning reduced the construction duration of one floor by
39.84% (39.84 =(53.07 - 31.92)/53.07) on average. This indicates that it
is effective for a project manager to perform space zoning using the
developed model.
Conclusions
Many researchers have studied efficient space zoning to reduce the
construction duration and interference among the different construction
works. These studies, however, were dependent on the network-based
scheduling methods using the space zoning concept in attempting to
reduce the construction duration, making it difficult to reflect
"iteration" and "overlapping," the two
characteristics of space zoning. This study was conducted for the
purpose of developing a scheduling model using the space zoning concept,
to overcome the limitations of the existed studies and to reduce the
construction duration by maximizing productivity. Using CYCLONE, one of
the popular discrete-event simulation methods, SCHEME was developed in
this study, which was then used to come up with a simulation of steel
structure construction, a representative construction operation in which
space zoning was often applied. It was shown that the developed model
reflects the characteristics of the actual construction processes where
space zoning is used (i.e. iteration and overlapping), and that the
simulation time for completing the steel structure construction work is
similar to that in the actual case. It was also shown that applying the
developed model to space zoning results in superior performance in terms
of the reduction of construction duration in cases with no space zoning.
There are main contributions of the developed model: (1) from the
academic point of view, the input resource-based model was developed for
repetitive construction works using space zoning to easily estimate and
reduce the project duration. The established model enables flexible
expansion according to the zoning plan of the working space and the type
of activities. Moreover, the proposed model was developed with proper
consideration of uncertainty, and thus, can produce more reliable
estimations; (2) from the practical point of view, the application of
the developed model allows (i) easy updates of the duration based on the
changes of construction conditions, and (ii) smooth management of the
project because the user can easily recognize the effect of the changes
of resources inputted to each work on the duration. Therefore, it is
expected that SCHEME will yield excellent results in repetitive
construction operations in actual construction projects in terms of
productivity and construction duration.
It should be noted, however, that this study considered only the
construction duration in performing space zoning and in presenting the
optimal cycle time. Therefore, for more efficient space zoning in
construction projects, further studies considering cost aspects should
be conducted in the future. Moreover, since space zoning should yield
both congestion in construction phase and difficulty in planning, the
developed model in this paper is not sure of successful space zoning
implementation. Therefore, further research to ensure the high
engineering and construction management skills for achieving the
successful space zoning implementation is necessary.
doi: 10.3846/13923730.2012.757561
Acknowledgements
This research was supported by Basic Science Research Program
through the National Research Foundation of Korea (NRF) funded by the
Ministry of Education, Science, and Technology (No. 20120008837).
References
AbouRizk, S. M.; Halpin, D. W; Wilson, J. R. 1994. Fitting beta
distributions based on sample data, Journal of Construction Engineering
and Management ASCE 120(2): 288-305.
http://dx.doi.org/10.1061/(ASCE)0733-9364(1994) 120:2(288)
Adler, P. S.; Mandelbaum, A.; Nguyen, V.; Schwerer, E. 1995. From
project to process management: an empirically-based framework for
analyzing product development time, Management Science 41(3): 458-484.
http://dx.doi.org/10.1287/mnsc.4L3.458
Akinci, B.; Fischer, M.; Levitt, R.; Carlson, R. 2002.
Formalization and automation of time-space conflict analysis, Journal of
Computing in Civil Engineering ASCE 16(2): 124-134.
http://dx.doi.org/10.1061/(ASCE)0887-3801(2002) 16:2(124)
Arditi, D.; Albulak, M. Z. 1986. Line-of-balance scheduling in
pavement construction, Journal of Construction Engineering and
Management ASCE 112(3): 411-424.
http://dx.doi.org/10.1061/(ASCE)0733-9364(1986) 112:3(411)
Arditi, D.; Tokdemir, O. B.; Suh, K. 2001. Effect of learning on
line-of-balance scheduling, International Journal of Project Management
19(5): 265-277. http://dx.doi.org/10.1016/S0263-7863(99)00079-4
Arditi, D.; Tokdemir, O. B.; Suh, K. 2002. Challenges in
line-of-balance scheduling, Journal of Construction Engineering and
Management ASCE 128(6): 545-556.
http://dx.doi.org/10.1061/(ASCE)0733-9364(2002) 128:6(545)
Back, W E.; Boles, W. W.; Fry, G. T. 2000. Defining triangular
probability distributions from historical cost data, Journal of
Construction Engineering and Management ASCE 126(1): 29-37.
http://dx.doi.org/10.1061/(ASCE)0733-9364(2000) 126:1(29)
Browning, T. R.; Eppinger, S. D. 2002. Modeling impact of process
architecture on cost and schedule risk in product development, IEEE
Transactions on Engineering Management 49(4): 428-442.
http://dx.doi.org/10.1109/TEM.2002.806709
Chang, C.-K.; Hanna, A. S.; Lackney, J. A.; Sullivan, K. T. 2007.
Quantifying the impact of scheduling compression on labor productivity
for mechanical and sheet metal contractor, Journal of Construction
Engineering and Management ASCE 133(4): 287-296.
http://dx.doi.org/10.1061/(ASCE)0733-9364(2007) 133:4(287)
Cheung, M. Y.; O'Connor, J. T. 1996. ArcSite: enhanced GIS for
construction site layout, Journal of Construction Engineering and
Management ASCE 122(4): 329-336.
http://dx.doi.org/10.1061/(ASCE)0733-9364(1996) 122:4(329)
Cho, S.-H.; Eppinger, S. D. 2005. A simulation-based process model
for managing complex design projects, IEEE Transactions on Engineering
Management 52(3): 316-328. http://dx.doi.org/10.1109/TEM.2005.850722
Cho, K.; Hong, T.; Hyun, C. 2011. Scheduling model for repetitive
construction process of high-rise buildings, Canadian Journal of Civil
Engineering 38(1): 36-48. http://dx.doi.org/10.1139/L10-108
El-Rayes, K.; Moselhi, O. 1998. Resource-driven scheduling of
repetitive activities on construction projects, Construction Management
and Economics 16(4): 433-446. http://dx.doi.org/10.1080/014461998372213
Guo, S.-J. 2002. Identification and resolution of work space
conflicts in building construction, Journal of Construction Engineering
and Management ASCE 128(4): 287-295.
http://dx.doi.org/10.1061/(ASCE)0733-9364(2002) 128:4(287)
Halpin, D. W.; Riggs, L. S. 1992. Planning and analysis of
construction operations. New York: John Wiley & Sons, Inc. 400 p.
Hong, T.; Hastak, M. 2007. Simulation study on construction process
of FRP bridge deck panels, Automation in Construction 16(5): 620-631.
http://dx.doi.org/10.1016Zj.autcon.2006.10.004
Hong, T.; Cho, K.; Hyun, C.; Han, S. 2011. Simulation based
Schedule control model for core wall construction of high-rise building,
Journal of Construction Engineering and Management ASCE 137(6): 393-402.
http://dx.doi.org/10.1061/(ASCE)CO.1943-7862. 0000300
Hyari, K.; El-Rayes, K. 2006. Optimal planning and scheduling for
repetitive construction projects, Journal of Management in Engineering
ASCE 22(1): 11-19. http://dx.doi.org/10.1061/(ASCE)0742-597X(2006)
22:1(11)
Lee, S.; Han, S.; Pena-Mora, F. 2009. Integrating construction
operation and context in large-scale construction using hybrid computer
simulation, Journal of Computing in Civil Engineering ASCE 23(2): 75-83.
http://dx.doi.org/10.1061/(ASCE)0887-3801(2009) 23:2(75)
Li, H.; Love, P. E. D. 1998. Site-level facilities layout using
genetic algorithms, Journal of Computing in Civil Engineering ASCE
12(4): 227-231. http://dx.doi.org/10.1061/(ASCE)0887-3801(1998)
12:4(227)
Moder, J. J.; Philips, C. R.; Davis, E. W 1983. Project management
with CPM, PERT and precedence diagramming. 3rd ed. New York: Van
Nostrand Reinhold Company. 389 p.
Smith, R. P.; Morrow, J. A. 1999. Product development process
modeling, Design Studies 20(3): 237-261.
http://dx.doi.org/10.1016/S0142-694X(98)00018-0
Taylor III, B. W.; Moore, L. J. 1980. R&D project planning with
Q-GERT network modeling, Management Science 26(1): 44-59.
http://dx.doi.org/10.1287/mnsc.26.L44
Thabet, W. Y.; Beliveau, Y. J. 1994. Modeling work space to
schedule repetitive floors in multistory buildings, Journal of
Construction Engineering and Management ASCE 120(1): 96-116.
http://dx.doi.org/10.1061/(ASCE)0733-9364(1994) 120:1(96)
Tommelein, I. D.; Zouein, P. P. 1993. Interactive dynamic layout
planning, Journal of Construction Engineering and Management ASCE
119(2): 266-287. http://dx.doi.org/10.1061/(ASCE)0733-9364(1993)
119:2(266)
Van Slyke, R. M. 1963. Monte Carlo methods and PERT problem,
Operations Research 11(5): 839-860.
http://dx.doi.org/10.1287/opre.11.5.839
Winch, G. M.; North, S. 2006. Critical space analysis, Journal of
Construction Engineering and Management ASCE 132(5): 473-481.
http://dx.doi.org/10.1061/(ASCE)0733-9364(2006) 132:5(473)
Yeh, I.-C. 1995. Construction-site layout using annealed neural
network, Journal of Computing in Civil Engineering ASCE 9(3): 201-208.
http://dx.doi.org/10.1061/(ASCE)0887-3801(1995) 9:3(201)
Zouein, P. P.; Tommelein, I. D. 2001. Improvement algorithm for
limited space scheduling, Journal of Construction Engineering and
Management ASCE 127(2): 116-124.
http://dx.doi.org/10.1061/(ASCE)0733-9364(2001) 127:2(116)
Kyuman CHO (a), Taehoon HONG (b), Chang Taek HYUN (c)
(a) Department of Architectural Engineering, Chosun University,
Gwangju, Korea
(b) Department of Architectural Engineering, Yonsei University,
Seoul, Korea
(c) Department of Architectural Engineering, University of Seoul,
Seoul, Korea
Received 4 Aug. 2011; accepted 11 Oct. 2011
Corresponding author. Taehoon Hong
E-mail: hong7@yonsei.ac.kr
Kyuman CHO. Assistant Professor at the Department of Architectural
Engineering of Chosun University. Before joining Chosun University, he
was a Postdoctoral research fellow at Purdue University and a research
assistant professor at the University of Seoul and worked as a
practicing engineer in the construction industry. He has published 36
journal papers and peer-reviewed conference papers, related to
Construction Engineering and Management. His primary research interests
include time and cost optimization for construction projects,
construction scheduling, and project delivery system.
Taehoon HONG. Associate Professor at the Department of
Architectural Engineering of Yonsei University, Seoul, Korea. He is a
corresponding member of editorial board in the Journal of Management in
Engineering, ASCE and is also a member of academic or practical
institute such as AIK, KSCE, ASCE, KICEM, and KCVE. His main research
interests include Life Cycle Cost analysis, Life Cycle assessment,
infrastructure asset management, facility management, and construction
project cost control.
Chang Taek HYUN. Professor at the University of Seoul, Seoul,
Korea. In particular, he has conducted extensive research in CEM fields
and published over 300 journal papers and peer-reviewed conference
papers, and several books and other publications. Currently, he is a
vice-president of the Korean Institute of Construction Engineering and
Management and a chairman of the Korea Construction VE Research
Institute. He has been helping the construction industry, especially in
the area of Value Engineering and Life Cycle Cost, construction contract
and claim, project delivery system, and program management.
Table 1. Case introduction
Space Activity Quantity
zoning
Zone Zone Zone Average
1 (pcs) 2 (pcs) 3 (pcs) weight
(ton)
Zone 1 Column 10 9 9 4.75
erection
Zone 2 Girder/ 15/23 15/26 16/36 0.68/0.43
beam install
Zone 3 Deckplate 9 10 11 0.42
install
Table 2. Report of actual data-set and simulation results
Case Data-sets of actual case
Column Girder/beam Deckplate Cycle
erection installation installation time (a)
(a) (node 2) (a) (node 10) (a) (node 20) (b)
Case 1 6.41 5.34 6.01 30.77
Case 2 7.01 5.70 4.02 28.13
Case 3 6.10 6.36 4.02 25.04
Case 4 9.43 7.57 4.43 36.59
Case 5 9.50 11.27 5.76 52.61
Case 6 6.86 6.10 4.79 29.95
Case 7 8.07 5.72 4.34 29.56
Case 8 6.44 9.76 4.36 46.72
Case 9 9.41 6.58 4.86 34.02
Case 10 6.38 5.52 7.48 34.32
Case 11 6.70 6.46 5.03 31.10
Case 12 8.47 7.18 6.04 36.05
Case 13 6.18 6.18 7.70 35.97
Case 14 6.51 6.45 5.03 30.90
Case 15 7.35 5.52 5.03 28.94
Case 16 10.01 8.81 5.00 41.43
Case 17 6.13 7.23 6.75 36.75
Case 18 5.79 10.43 6.84 53.20
Case 19 7.33 10.48 5.86 50.94
Case 20 7.68 10.50 4.79 49.60
Case 21 10.18 13.31 5.10 61.46
Average 7.52 7.74 5.39 38.29
Case Simulation Difference Percentage of
results (b) (a - b) difference
(c) [(a - b)/a x 100]
Case 1 29.80 0.97 3.14
Case 2 25.01 3.12 11.09
Case 3 24.08 0.96 3.83
Case 4 35.00 1.59 4.34
Case 5 52.43 0.18 0.34
Case 6 28.30 1.65 5.51
Case 7 27.40 2.16 7.31
Case 8 42.12 4.60 9.85
Case 9 30.90 3.12 9.18
Case 10 34.30 0.02 0.07
Case 11 28.60 2.50 8.04
Case 12 34.10 1.95 5.42
Case 13 35.50 0.47 1.31
Case 14 28.40 2.50 8.10
Case 15 28.30 0.64 2.22
Case 16 40.30 1.13 2.73
Case 17 34.20 2.55 6.93
Case 18 47.34 5.86 11.02
Case 19 47.66 3.28 6.45
Case 20 43.99 5.61 11.30
Case 21 55.21 6.25 10.16
Average 35.85 2.44 6.11
(a) Most likely durations for constructing each activity
on one zone.
(b) Construction times during which all activities are
iterated three times.
(c) Simulation times using the developed model based on
actual data-set.
Table 3. Duration input data of case 2 for simulation
Node no. Name Duration (hours) (a) Remark
Minimum Most Maximum
likely
2 Column 5.21 7.01 8.31 Work node
erection
10 Girder/beam 4.23 5.70 7.12
installation
20 Deckplate 3.71 4.02 5.23
installation
7, 15, 24 Move to 0.3 (b)
next zone
5, 13, 21 Done 0.0001 Dummy node
(b) (c)
(a) Durations for constructing each activity on one zone.
(b) Deterministic value by experts of the analyzed case.
(c) Node for modeling the logical relationship.
Table 4. Case introduction
RC * No No. of No. of No. of Cycle time
crew 1 ** crew 2 ** crew 3 **
1 1 1 1 24.0529
2 1 1 2 24.9532
3 1 1 3 24.5549
... ... ... ... ...
47 2 5 2 25.2685
48 2 5 3 23.5017
49 2 5 4 25.6246
... ... ... ... ...
123 5 5 3 25.6410
124 5 5 4 25.5755
125 5 5 5 26.1438
Note: * RC = Resource Combination
** Crew 1, 2, and 3 represent the crew of activity
1, 2, and 3, respectively.
Fig. 6. Comparison between actual case and
simulation result
Case Number Actual case Sim. Results
1 29.80 30.77
2 25.01 28.13
3 24.08 25.04
4 35.00 36.59
5 52.43 52.61
6 28.30 29.95
7 27.40 29.56
8 42.12 46.72
9 30.90 34.02
10 34.30 34.32
11 28.60 31.10
12 34.10 36.05
13 35.50 35.97
14 28.40 30.90
15 28.30 28.94
16 40.30 41.43
17 34.20 36.75
18 47.34 53.20
19 47.66 50.94
20 43.99 49.60
21 55.21 61.46
Actual case, 38.29 hours
Sim. results, 35.85 hours
Note: Table made from line graph.
Fig. 7. Comparison between nonspace zoning and
space zoning
Case Number Non space zoning Space zoning
1 54.00 29.01
2 47.16 28.93
3 57.81 32.54
4 65.04 28.36
5 64.49 30.67
6 68.23 33.11
7 40.81 31.32
8 54.46 32.36
9 63.77 32.75
10 48.49 31.71
11 51.27 31.68
12 44.27 29.56
13 43.17 33.71
14 49.26 33.78
15 50.69 31.22
16 41.27 33.15
17 52.12 32.50
18 53.27 32.93
19 57.04 31.65
20 53.54 34.21
21 53.81 32.89
22 53.51 34.25
Non space zoning, 53.07 hours
Space zoning, 31.92 hours
Note: Table made from line graph.
