Using an Interactive Schedule Simulation Platform to Assess and Improve Contingency Management...

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Using an interactive schedule simulation platform to assess and improve

contingency management strategies

Pei Tang a,, Amlan Mukherjee a, Nilufer Onderb

a Department of Civil and Environmental Engineering, Michigan Technological University, Houghton, MI 49931, United Statesb Department of Computer Science, Michigan Technological University, Houghton, MI 49931, United States

a b s t r a c ta r t i c l e i n f o

Article history:

Accepted 27 July 2013Available online 23 August 2013

Keywords:

Decision strategy

Construction contingency management

Interactive schedule simulation

Improvement

An understanding of the dynamic interactions between resources and the management decisions that control

themis critical to effectively manage contingencies during the execution phase of a project. Hence,the objective

of this paper is to study each resource decision within the context of sequences of decisions. This allows the con-

sideration of dynamic interactions between resources, and designs control responses that account for uncertain

outcomes andthat minimize contingencies. It is hypothesized that thesuccess of achieving project objectives and

priorities is dependent on understanding ways of developing coherent management decision sequences. For

given project priorities and outcomes, such decision sequences are dened as strategies. This paper proposes

andimplements a simulationbasedmethod to assessand improve alternativedecision strategies. Thetheoretical

contribution of this research is that it develops foundational simulation based methods that support the study of

construction decision-making as a dynamic control problem.

Published by Elsevier B.V.

1. Introduction

Managing uncertainties during the execution of a construction

project presents a difcult dynamic decision-making problem. External

events such as inclement weather, labor disturbances, or unexpected

rework profoundly affect the expected outcomes of a project. The

specic time in the schedule whensuchevents occur signicantly inu-

ences the extent of the impact. In addition, such events and the resource

decisions that are made to manage them have cascading impacts on

the schedule that may reinforce existing delays, and/or set in motion

unexpected feedbacks. For example, a decision to crash a specic

activity using over-time work, may cause the crews to intersect with

other spatially co-located activities and increase congestion on site.

This may not have the desired result of reducing the project duration.

An understanding of these interactions between resources and the

management decisions that control them becomes critical to effectively

manage contingencies during the execution phase of a project.

A key to study this decision-making problem is to examine decisions

within the context of their immediate impacts as well as how

they impact the broader scope of the project and the feedbacks they

generate. Existing research studied the impacts of decisions on specic

activity operations such as the tunneling [31], earth moving[17,11],

and concrete paving[12]. For example, the impacts of broken dozers

on the productivity or duration of earth moving were evaluated. How-ever, from the perspective of construction process, the impacts of

earth moving on other activities such as concrete paving have hardly

been studied. A construction process is consisted of activities to com-

plete the project, whose sequences are determined by the project

schedule. To bridge the gap, this paper suggests studying the decision-

making problems pertaining to the construction process. The impact

of each resource decision on the construction process is studied within

the context of sequences of decisions. It is hypothesized that the success

of achieving project objectives and priorities is dependent on under-

standing ways of developingcoherent managementdecision sequences.

The sequence of decisions is dened as decision strategies given project

priorities.

To study decision strategies, two approaches can be considered:

(i) by establishing general performance trends from a signicant num-

ber of historical construction decision data sets and case studies;

and (ii) by using simulation platforms that allow the exploration

of what-if scenariosand multiple deviations from theas-planned sched-

ule. While the rst approach is rooted in direct observation, the second

one is based on the modeling of the construction process. The rst is

limited by the data collection process that may prove to be practically

difcult. In addition, thecase studies include a limited numberof project

outcomes, therefore allowing only partial observability of the success or

failure of a decision. In comparison, simulation environments allow a

decision-maker to test different management decisions and explore

large spaces of simulated futures for behavior investigation. Although

validating such models can prove to be difcult, analyzing these

Automation in Construction 35 (2013) 551560

Corresponding author. Tel.: +1 906 4871952; fax: +1 906 4872943.

E-mail address:[email protected](P. Tang).

0926-5805/$ see front matter. Published by Elsevier B.V.

http://dx.doi.org/10.1016/j.autcon.2013.07.005

Contents lists available atScienceDirect

Automation in Construction

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simulated futures can provide signicant support to decision-making

and planning. As the empirical method and the simulation method

complement and support each other, testing alternative strategies in a

simulation environment takes the rst step towards the coupling of

two methods.

Therefore, this paper proposes and implements a simulation based

method that can be used to assess and improve the effectiveness of

alternative decision strategies given project uncertainties and priorities.

The

rst part of the paper introduces the simulation platform andthe underlying research method. The proposed analysis is intended to

support a priori analysis of projects. The second part illustrates the

application of the method to improve strategies for managing a high-

way construction project. The theoretical contribution of this research

is that it develops foundational simulation based methods that sup-

port the study of construction decision-making as a dynamic control

problem.

2. Background

At the operational level, contingencies are managed by identifying

uncertainties associated with variables that dene critical activities,

such as cycle times in earthwork. Discrete event simulations are

used to simulate the impacts of uncertainties on the operations

[17,11]. Dynamic Planning Methodology (DPM)[22]uses the concepts

and methods in System Dynamics (SD) while taking advantage of

stochastic schedule analysis methods. It analyzes complex relationships

between production reliability, schedule pressure, and uncertainty in

activity durations to study change management and fast-tracking of

design/build construction projects [20,21,19,13,14,18]. Such methods

studied decisions by comparing variables such as cost and duration

rather than analyzing their sensitivity to decisions and uncertainties at

different points in the project. To study the decision-making problems

pertaining to the construction process, the gaps between existing

research and the objective of this research are:

Existing research studied the impacts of decisions on activity opera-

tions, neglecting the impacts of one activity on others. The objective

of this research is to study theimpacts of decisionson theconstructionprocess.

Existing research set up decision variables before simulation started.

This research requires a consideration of the decision-maker's

responses to the contingency situations such as schedule delay,

budget overrun,or critical path changes during the simulation process.

Existing research simulated decisions independently. This research

requires a consideration of the interactions between decisions.

The limitations of existing research are caused by the selection of

research subjects and simulation platforms. First, the existing research

studies isolated decisions and their impacts on project operations. It

neglects the impacts of decisions on project process as well as the feed-

backs and interactions between decisions and resources. To address

this gap, this paper proposes to study decision strategies, the sequences

of decisions, to manage project processes. This allows the considerationof dynamic interactions between resources, and designs control

responses that account for uncertain outcomes, and minimize contin-

gencies. Secondly, the use of non-interactive simulation platforms

does not allow a decision-maker to interactively monitor and control

the construction process. The activity cycle diagram based simulation

platforms such as AP3 [27], (SimCon) [6], PICASSO [29,30,28], and

SDESA[15]are able to simulate both construction operations and pro-

cess. They treat each activity as an instantaneous time point, making it

difcult to express multiple activities across multiple tasks which over-

lap in time. Once the simulation starts, the decision-maker cannot stop

the simulation and make resource decisions to manage the project as

the project evolves. Instead, this paper proposes an interactive simula-

tion platform that is based on the time interval method. The time inter-

val method simulates as-planned schedules on an interval basis. During

each interval, the project uncertainty is generated, decisions are

made, the activity productivity is calculated, and the work progress

is predicted.

The simulation platform is called Interactive Construction Decision

Making Aid (ICDMA). ICDMA[32]is based on the merger of two sep-

arate simulations, the Virtual Coach[25] and the work of TempOral

Network with Activities and Events (TONAE) [1,2]. The Virtual Coach

was developed from the formal models that drive the situational sim-

ulations. TONAE is the representation and reasoning about construc-tion management information of activities, events, and constraints. It

can be used to generate contingency plans, such as weather events,

and automatically estimate contingencies by exploring combinatorial

future spaces resulting from constraint violations. TONAE was applied

successfully to study the construction of a steel frame ofce[2]. How-

ever, the case study was performed in a non-interactive mode, which

did not allow a decision-maker to interactively monitor and control

the construction process. Based on Virtual Coach and TONAE, the co-

authors developed ICDMA, which is capable of emulating the con-

struction process, simulating the impacts of disruptive events and de-

cisions on the project process, and predicting the extreme scenarios of

a project.

3. Methodology: iterative processto assess and improve contingency

management strategies

Fig. 1describes an iterative process to assess and improve contin-

gency management strategies at the pre-construction phase of a pro-

ject, given project uncertainties and different project priorities. The

simulation based method itself is platform independent. Starting by

creating a simulation project from contractors' project documents, the

method tests a set of initial strategies on a simulation platform. As

each strategy presents multiple trade-offs, the best strategy is usually

not a nominal strategy, but instead a hybrid strategy that emerges

through the analysis of different nominal strategies given the project

priorities. The new strategies can be continuously generated and im-

proved by repeating the process. The method is designed so that it

can generate satisfactory strategies from any set of initial strategies

by iteratively exploring and learning from competing strategies.Starting with a list of suitably chosen strategies, a decision maker can

theoretically reduce the number of iterations. Historical records of

similar projects are not required but can be used as a validity check.

This paper applies the method using ICDMA, which consists of the fol-

lowing steps:

For a given project, a decision-maker rst establishes a simulation

project in ICDMA (using a resource loaded as-planned schedule and

an expected project environment), and develops a series of contin-

gency management strategies.

In the second step, the decision-maker implements each strategy to

manage the simulation project in ICDMA for a number of times in

the given project environment.

In the third step, the decision-maker compares and identies compet-ing strategies which are efcient in achieving desired objectives such

as a low project cost or a short project duration. If no outstanding

strategy is identied, restart from step oneto step three using a differ-

ent series of strategies.

In the fourth step, the decision-maker checks whether the out-

standing strategies meet the desired qualications. If they do,

these strategies are considered as satisfactory strategies. If not,

the decision-maker moves to the next step and investigates the

existence of general trends and patterns of outstanding strategies.

In the case of no patterns identied, restart from step one with dif-

ferent strategies.

In thefth step, the decision-maker generates new hybrid strategies

by learning from and combining the trends and patterns from the

fourth step.

552 P. Tang et al. / Automation in Construction 35 (2013) 551560


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For continuous improvement, the decision-maker iterates the aboveprocess with newly generated hybrid strategies until satisfactory

strategies are identied.

4. Simulation platform description

ICDMA is a general-purpose interactive simulation with the objec-

tive of exposing a decision-maker to rapidly unfolding events and the

pressure of decision making. The setup of a project in ICDMA requires

a resource loaded as-planned schedule and project environment

(Fig. 2). ICDMA emulates and advances the as-planned schedule on an

interval basis, which can be a day, week, or month unless otherwise

specied. The decision-maker takes on the role of a construction

manager to complete the project involving differing project priorities.

During each interval, the decision-maker is presented with randomexternal events that force the simulated project to deviate from its as-

planned schedule. The decision-maker has to respond to the events

by making resource related decisions. The consequences from the

decisions result in new scenarios for the decision-maker to respond.

This process continues until the completion of the simulated construc-

tion project.

4.1. Underlying algorithm

ICDMA provides a rich, state-based representation of construction

information and constraints that drives the construction management

domain. It is important to recognize that ICDMA is not a discrete event

simulation of a construction operation. Instead, it is a continuous time

advance simulation of the construction schedule comprising of an

emulator and a random event generator. The emulation engine[23]enables the simulation to advance from one time point to the next

contiguous time point in its as-planned schedule, rather than advancing

from one event to the next event. The inference technique and random

event generator [24] generate and simulate the random events that

disrupt the schedule. The two techniques together allow a decision-

maker to stop in the middleof the simulation to enter different resource

decisions to manage the project. A third critical component of ICDMA,

the querying algorithm [2], enumerates Monte Carlo samples of the

combinatorial future spaces in dynamic risk construction scenarios as

situations evolve. It allows ICDMA to explore a number of future project

outcomes at any time point during the simulation withconsideration of

the impacts of decisions made prior to that time point and the risks in

the future. For details about how each technique and algorithm works,

refer to the above papers.

4.2. Inputs in ICDMA

The resource loaded as-planned schedule provides a baseline to

complete the project, which requires the following information

(Fig. 2) to set up: (i) a list of activities and the estimated durations to

complete the project; (ii) material, labor, and equipment usages for

each activity; (iii) unit price of labor, equipment, and material;

(vi) logical relationship and constraints between activities to create

the as-planned schedule.

Project environment is dened as the expected disruptive events

specic to a project. Each event consists of the precondition,

postcondition, and probability. For example, the concrete testing fails

at a rate of 5%, and if that occurs, the concrete paving activity will stop

Fig. 1.Iterative process to assess and improve contingency management strategies.

553P. Tang et al. / Automation in Construction 35 (2013) 551560


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for one day to perform an extra test or tests. The pre-condition of

the event is the concrete paving activity being ready for operation. The

postcondition is that the concrete paving activity stops for one day.

The occurrence probability of the event is 5%. The precondition,

postcondition, and probability are set up by setting the values ofcorresponding variables. In the example of concrete testing, the post-

condition is dened by assigning the variable peproductivity to zero

and duration to one for the concrete paving activity.

4.3. Outputs from ICDMA

During each simulation process, a large amount of structured

simulated as-built data (Fig. 2) is collected. They include:(i) project

outcomes, describing the cost and schedule condition of the project;

(ii) decision information, recording the material, labor, and equipment

allocation and re-allocation; (iii) a record of disruptive events; and

(vi) querying results, including a number of predicted project total

costs and completion dates during the simulation. The number ofpredictions is determined prior to the beginning of the simulation.

5. Case study: highway reconstruction project

5.1. Project description and data collection

The case study involves a ten-mile highway concrete pavement re-

construction project in Southeast Michigan. This paper studied the re-

construction of the east bound section of a major interstate highway

in 2009. This section demonstrated the application of the proposed

method to improve strategies from a series of randomly developed

contingency management strategies. Existing project documents was

used to build the resource loaded as-planned schedule. And the record

of construction history was investigated to identify the actual project

environment.

5.1.1. As-planned information

The following information was collected to develop a resourceloaded as-planned schedule:

Fourteen major activities and associated durations to complete the

east bound section (Table 1). The information was acquired from the

progress schedule (MDOT Form 1130), a document submitted to the

Michigan Department of Transportation (MDOT) by the contractor

before the project started.

Material, labor, and equipment usages for each activity. The bidding

documents provided the material quantity take-offs. Labor and equip-

ment usage was determined by the material quantity and the labor

crew productivity in RS-Means, heavy construction cost data 2009

[26].

Material, equipment, and labor unit prices were obtained from the

RS-Means. Actual spatial constraints between activities from the main contractor

(Table 1).

5.1.2. As-built information

In Michigan highway projects, MDOT requires the use of a software

called FieldManagerTM on all their construction and rehabilitation

contracts. Inspectors (on behalf of MDOT) use the software to record

daily general site information, contractor personnel and equipment,

and postings of material quantities. The daily record fromFieldManagerTM

was analyzed to identify the disruptive events, their probabilities, and

their impacts on the project. Details about the information processing

can be found in the co-authors' previous work [4]. Bad weather, labor

crew problems (equipment failure or worker illness), and concrete

testing failure were found to be the most inuential events responsible

Fig. 2.Simulation set-up and data collection.



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for project delays (Table 2). The as-built disruptive event information

was used to simulate the actual project environment in ICDMA. Inaddition, the record identied the as-built cost and completion date

as $20,277,970 and 129 working days for the case study project.

The as-built data can be compared against the simulated as-built

performance to verify the accuracy of ICDMA.

5.2. Step one: project set-up and strategy development

The as-planned information and disruptive event information were

input into the database to develop the simulation project. In addition,

another three variables were determined for this specic project:

(i)indirectcost wasdetermined to be 25%of thedirectcost;(ii) thesim-

ulation granularity was set to be one day interval, implying the

simulation advances the project schedule by one day at each simulation

step until the completion of the project; and (iii) 1000 complete future

queries every 15 days from the beginning of the project. For application

to real projects,the decision-maker candetermine appropriate valuesto

reectthe reality. As a result, theproject was scheduled to be completed

in 106 working days with an estimated cost of $28,111,603. The differ-

ences between the as-planned and the as-built costs were caused by

the use of different resources and resource unit prices. The as-built

cost used contractors' prices and the as-planned cost used RS-Means'prices. As the expected and actual activity durations and the quantity

of work to be completed were available, it is assumed that the project

cost estimates from RS-Means still provide a useful benchmark for

strategy comparison.Fig. 3shows the interface of the highway project

in ICDMA.

Once the project and uncertain environment were established, deci-

sion strategies were developed for testing and improvement. Each deci-

sion strategy consists of policies that determine the ordering, allocation,

and reallocation of space, material, labor and equipment resources.

These resource policies generate the sequences of decisions for different

project priorities. For example, in a project where timely completion is

critical, a nominal strategy will prioritize duration over cost. As a result,

a sequence of possible decisions will be made keeping an eye

on controlling project duration. Each resource allocation policy directly

affects activity productivity. For example, if the only paver on a site

breaks down, the productivity of paving activity will be reduced to

zero. Other unrelated activities' productivity remains unchanged. In

addition, a cost is inicted every time a resources policy is applied. To

understand how ICDMA deals withevents such as disruptions and deci-

sions, please refer to the co-author's previous work[2325,2,7].

Realistic applications can start with strategies that reect the reality

of the project being simulated. However, for the sake of this discussion,

the critical take away is how the proposed method can continuously

improve strategies by learning from and rening the initial strategies.

For illustration purposes, this research considers three strategy cate-

gories: one control strategy, three passive management strategies,

and three aggressive management strategies. Passive management

is based on the ad-hoc response to unexpected events. Aggressive

management aims to anticipate the future and prepare contingencyplans that minimize adverse consequences of unexpected disrup-

tions to the project schedule. Strictly speaking, the control strategy

is a special case of passive strategies. It manages the schedule by tak-

ing theminimum numberof actions to deal with interruptions and is

used as a baseline strategy to contrast with other strategies.

When applying the decision strategies, the decision-maker has to

decide what time to apply the resource policies. When using passive

strategies, the question is how much of a project delay can decision-

makers tolerate before they take action? It is hypothesized that the

appropriate decision time plays an importantrole in maximizing project

outcomes. This paper develops initial strategies by using the tolerance

of schedule delay (X) or acceleration (Y). Assuming that the tolerance

of schedule variance is 5% of the total project duration, the maximum

value of (X) and (Y) is 5 days (106 days 5% = 5 days) for this high-way reconstruction project. The strategies are:

Control Strategy: No actions are taken when the project falls behind.

Resource allocation policies: (a) labor policy: no extra workers are

replaced in any case; (b) equipment policy: equipment is xed the

next day if it breaks down and never use extra equipment; (c) space

policy: critical activities are prioritized when allocating space on site.

Three passive strategies: dened as a Catch Up_X Strategy where X is

the tolerance of schedule delay (X = One, Three, Five). For Catch

Up_X Strategy, resource allocation policies are applied to catch up

with the schedule every time the project is X days behind the as-

planned schedule. When approaching the end of the project, Catch

Up Strategies reduce the tolerance of schedule delay to one day

behind for timely completion. Resource allocation policies: (a) labor

Table 1

Project information to build resource loaded as-planned schedule.

NO. Activity description Duration Precedence

activities

Spatial distance

between activities

1 Strip topsoil 10 days n/a

2 Remove concrete

pavement

30 days 1 0.5 to 1 miles between stripping

topsoil and concrete

pavement removal

3 Grade subbase 26 days 2 n/a

4 Install drainage 18 days 2,3 0.5 to 1 miles between installingdrainage and concrete pavement

removal; 1 to 3 miles between

installing drainage and grade

subbase

5 Place open graded

drainage course

(OGDC) mainline

18 days 2,3,4 1 mile between grade subbase

and placing OGDC mainline

6 Pave east bound

mainline

32 days 5 1 mile between placing OGD C

mainline and paving east bound

mainline

7 Place OGDC ramps

and gaps

8 days 4,5,6 3 to5 miles between pavingeast

bound mainline and placing

OGDC ramps and gaps

8 Pave east bound

gaps and ramps

9 days 7 n/a

9 Place gravel

shoulder

4 days 4 2 t o 3 miles b etween p aving e ast

bound gaps and ramps andplacing gravel shoulder

10 Slope grading and

restoration east

bound

26 days 9 0.5 miles between placing g ravel

shoulder and slope grading and

restoration east bound

11 Stripe to open

pavement east

bound

3 days 9 10 miles between placing g ravel

shoulder and striping to open

pavement east bound

12 Relocate barrier

wall

10 days 10 10 miles between striping to

open pavement east bound and

relocating barrier wall

13 Re-stripe west

bound

3 days 12 n/a

14 All lanes open 1 days 12,13 n/a

Table 2

Project environment.

Disruptive

event

Precondition Postcondition Probability

Bad

weather

N/A Productivity reduces by half for each

activity on the bad weather day.

0.20

Equipment

failure or

worker

sick

N/A Random labor(s) is(are) sick or

equipment break(s) down when the

event occurs. The labor crew's

productivity reduces according to the

weight of the labor or equipment in the

labor crew.

0.12

Concrete

testing

failure

Paving

concrete is

being ready.

Productivity of paving activity reduces to

zero for one day waiting for clearance of

new test.

0.05



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policy: extra workers are hired and replaced in cases of illnesses and

project delay; (b) equipment policy: equipment is xed by the

mechanics immediately and extra equipment is used in case of

schedule delay; and (c) space policy: critical activities are prioritized

when allocating space on site.

Three proactive strategies: dened as a Crash_Y Strategy where Y is

the tolerance of schedule acceleration (Y = One, Three, Five). The

decision-maker assesses future risks and applies resource allocationpolicies to stay Y days ahead of the as-planned schedule. When

approaching the end of the project, the Crash_Y Strategy reduces the

desire of staying Y days ahead of the as-planned schedule to one day

ahead for timely completion. Resource allocation policies: (a) labor

policy: extra workers are hired and replaced in cases of illnesses or

to expedite the schedule; (b) equipment policy: equipment is xed

by the mechanics immediately and extra equipment is used to

expedite the schedule; and (c) space policy: critical activities are

prioritized when allocating space on site.

5.3. Step two: simulation experiment and validation

Each strategy was implemented for thirty ve runs to meet the

minimum requirement of statistical analysis. For the sake of uniformityand given the experimental nature of this research, one of the authors

was the single decision-maker running all the simulations.The simulated

as-built data at each decision control point was collected. In the case of

no disruptive events, the simulated as-built total cost was $28,111,603

and the duration was 106 days, which veried that the simulation plat-

form can emulate the as-planned schedule. In addition, the simulated

as-built results were compared to the actual as-built results for valida-

tion. The actual as-built record showed that it took 129 working days

to complete the project with a cost of $20,277,970. In the simulation

experiment, the Control Strategy took an average of 127.91 days

(Fig. 4) to complete the job, which was comparable to the as-built dura-

tion (129 days). In addition, the Control Strategy, Catch Up Strategies,

and Crash Strategies all completed the project within 112% of the as-

built cost.

5.4. Steps three and four: strategy assessment and patterns identication

5.4.1. Strategy assessment regarding project cost and duration

A total of 245 instances of simulated as-built project costs and

completion dates were collected for the seven strategies. Fig. 4shows

the average total cost and duration for each of the strategies. It is

observed that the Control Strategy completed the project at a higher

cost and longer duration than others. Different strategies might producedifferentcosts.Now, thequestionis whether there is statistical evidence

to support the observation.

Theone way ANalysis Of VAriance (ANOVA), an inferential statistical

test, wasusedto examine if the cost means were the same in the group.

It requires equal variances in the independent identically distributed

data [3]. The results are assumed to be independent and identically

distributed for each strategy when the number of experiments is large

because each simulation run was independent. The Levene's test was

used to test variance similarities, whose null hypothesis is that equal

variance exists. If this assumption turns out to be not applied, the

BrownForsythe test is used as an alternative version of F statistic. All

the tests were implemented in the Statistical Package for the Social

Sciences (SPSS). The test variables were 35 instances of simulated as-

built total costs from each strategy. The null hypothesis and alternativehypothesis of ANOVA are:

Null hypothesis H0: (Control Strategy) = (Catch Up_Five

Strategy) = (Catch Up _Three Strategy)=(Catch Up_One Strat-

egy) = (Crash_Five Strategy) = (Crash_Three Strategy) =

(Crash_One Strategy), where represents the average project

cost produced by each strategy;

Alternative hypothesis Ha: at least one of the means is different

from the rest;

The signicance level: = 0.05.

The Levene's test showed that there was not enough evidence

(0.509 N 0.05) to reject the null hypothesis (project's total costs of

seven strategies have equal variance). Therefore, as determined by

one-way ANOVA (F(6,238) = 34.807, p = 0.000), the null hypothesis

Fig. 3.Interface of simulated highway reconstruction project in ICDMA.



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was rejected, indicating that alternative strategies had different total

costs.

The next question was, which strategies outperformed others? ThePost Hoc test was performed in SPSS to identify the differences between

onegroup andthe rest. Thetest results showedthat theControl Strategy

had a different average total cost from other strategies, which afrmed

the initial observation and the results of ANOVA test. In addition, the

Post Hoc test also indicated that the Crash_Three Strategy and Catch

Up_Five Strategy outperformed other strategies in cost management

(the signicance value of the Crash_Three Strategy against the Catch

Up_Five Strategy was 0.122 N0.05, and the values of the Crash_Three

Strategy against other strategies were less than 0.05).

5.4.2. Strategy assessment using the querying algorithm

From the beginning of the project, the querying algorithm predicted

1000 total costs and completion dates every 15 days through the entire

simulation process. The querying results can be used as a metric toevaluate how project uncertainties evolve among different strategies.

The querying results were analyzed in boxplots by SPSS 16.0. Boxplots

[5] are an excellent tool for detecting and illustrating location and

variation changes between different groups of data. It identies the

inter-quartile range (the differences between the 25th and 75th

percentile), the median, and the extreme points (within 1.5 timesthe inter-quartile range from the upper or lower quartile). Points

that are outside of the ends of the lines extending from the inter-

quartile are potential outliers.Figs. 5 and 6present the distributions

of predictedtotalcostsand completion dates at different time points

through the process of the project for the Catch Up_Five Strategy,

Crash_Three Strategy, and Control Strategy. The outliers were

not shown because they were such a small quantity. The x-axis

represents the simulation time. The y-axis represents the projected

total costs or durations. For example, for the Catch Up_Three Strategy

onDay1in Fig. 5, the project is predicted to be completed at an average

cost of 30.2 million dollars. There is a 25% chance that the predicted

costs are less than 28.8 million dollars (the 25th percentile), and a

25% chancethat thepredictedcosts aregreater than30.5 million dollars

(the 75th percentile). Excluding the outliers, the maximum andminimum predicted total project costs are 31.5 and 28.9 million dollars

respectively. The graphs are interpreted qualitatively by analyzing the

shape and positional shift of boxplots as follows:

Fig. 4.Simulated as-built total costs and durations for seven strategies.

TimePoints

Day121Day106Day91Day76Day61Day46Day 31Day 16Day 1

ProjectedTota

lCost

32

31

30

29

Control StrategyCrash_Three StrategyCatch Up_Five Strategy

Strategies

Fig. 5.Predicted completion costs through the construction process.



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A shift to the lower right position over time indicates improving

performance and that the most likely, minimum and maximum

completion costs are decreasing over time.

A shift to the upper right position over time indicates worsening

performance and that the most likely, minimum and maximum

completion costs are increasing over time.

An increase in the inter-quartile range over time indicates increasing

uncertainty in the project with the possibility of threshold events

that may govern the project outcome signicantly.

Forexample, in Fig. 5, the boxes of the Control Strategy moved in the

upper rightdirection, indicating ineffective managementas the comple-

tion costs were predicted to increase over time. The Catch Up_Five

Strategy outperformed the Crash_Three Strategy in maintaining lower

predicted average cost in therst 46 days. In Fig. 6, the shift of boxplotsto the upper right direction implied that for the Control Strategy, the

completion durations increased over time. The Crash_Three Strategy

maintained lower predicted average durations than the Catch Up_Five

Strategy over the entire simulation process. After Day 61, the inter-

quartile range reduced more for the Crash_Three Strategy than the

Catch Up_Five Strategy, implying that the Crash_Three Strategy had a

better control over duration deviationsthan the Catch Up_Five Strategy

when approaching the project completion.

5.5. Stepve: new strategy generation and evaluation

Assessment results from step three and four were summarized as:

(i) the Control Strategy had the worst performance in managing contin-

gency situations; (ii) generally, Crash Strategies outperformed Catch UpStrategies in managing completion durations; (iii) the Crash_Three

Strategy and Catch Up_Five Strategy were the most cost efcient strate-

gies; (vi) the Catch Up_Five Strategy maintained lower predicted

average costs than the Crash_Three Strategy in the early half of the

construction phase; and (v) the Crash_Three Strategy had a better

control of duration variance than the Catch Up_Five Strategy in the

late half of the construction phase. In addition, differences between

three Crash Strategies (Fig. 4) indicated that an optimum Y might

exist for Crash Strategies to achieve the best cost management.

By combining the strategy assessment results, new hybrid strategies

are generated. For example, the Catch Up_Five Strategy can be used in

the early stage of the project to save interests because of the lower

expenditures in the early phase. If timely completion is critical, Crash

Strategies can be used, especially in the later construction phase. The

number of ways to combine assessment results is determined by the

decision-maker with regard to the specic objectives. In this study,

the satisfactory hybrid strategies were expected to be cost and duration

efcient while minimizing the deviations of predicted costs and

durations through the construction process. Therefore, a new Hybrid

Strategy was created with the expectation to inherit the advantages of

the Catch Up_One Strategy and Crash_Three Strategy. Another two

Crash Strategies were created to further test the hypothesis that an

optimum Y exists in the family of Crash Strategies to achieve better

project outcomes.

Hybrid Strategy: in the early half of the construction phase, imple-

ment Catch Up_Five Strategy; in the remaining construction phase,

use Crash_Three Strategy.

Crash_Y Strategy (Y = Two, Four), where Y is the tolerance of sched-ule acceleration. The decision-maker assesses future risks and applies

resource allocation policies to stay Y days ahead of the as-planned

schedule.When approaching theend of theproject, theCrash Strategy

reduces the desire of staying Y days ahead of the as-planned schedule

to one day ahead for timely completion. Resource allocation policies:

(a) labor policy: extra workers are hired and replaced in cases of

illnesses or to expedite the schedule; (b) equipment policy: equip-

ment is xed by the mechanics immediately and extra equipment is

used to expedite the schedule; (c) space policy: critical activities are

prioritized when allocating space on site.

The two-samplet-test wasused to examine if themeans of simulated

as-built total costs were different between the new strategies and the

initial strategies. The Hybrid strategy, Crash_Two Strategy, and

Crash_Four Strategy were compared against the Crash_Three Strate-gy, which had the lowest simulated as-built project cost. The null

hypothesis is that twostrategies have thesamemean cost. The alter-

native hypothesis is that the cost means of strategies are different.

The signicance level was set as = 0.05. The two-sample t-test

was performed assuming both equal and unequal variances. The

Levene's test was used to test the equal variances in data. All the

signicance values in the Levene's test were greater than 0.05,

indicating that there was not enough evidence to reject the null hy-

pothesis (equal variance exists). Therefore, the results of thet-test

that assumed equal variances were used.

When comparing the simulated as-built total costs of the

Crash_Three Strategy and Hybrid Strategy, the signicance value

of t-test was 0.056 (N0.05), thus not providing evidence to reject

the null hypothesis and indicating that the Crash_Three Strategy

TimePoints

Day121Day106Day91Day76Day61Day46Day 31Day 16Day 1

ProjectedTo

talDuration

135

130

125

120

115

110

105

Control Strategy

Crash_Three Strategy

Catch Up_Five Strategy

Strategies

Fig. 6.Predicted completion dates through the construction process.



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($29,269,459) and Hybrid Strategy ($29,420,561) had a comparable

amount of total project costs. Comparing the average accumulated

as-built cost during the simulation process, Fig. 7 shows that

the Hybrid Strategy inherited the properties of the Catch Up_Five

Strategy in maintaining lower costs in the rst half of the construc-

tion. After that, the Hybrid Strategy inherited the property of early

project completion from the Crash_Three Strategy. In addition,

the t-test showed that the Crash_Two Strategy and Crash_Three

Strategy had the same as-built total cost (0.475 N

0.05), but theCrash_Four Strategy and Crash_Three Strategy had a different as-

built total cost (0.035 b0.05). Therefore, aggressive strategies that

attempt to stay two or three days ahead of the as-planned schedule

were identied as the most cost efcient strategies (Fig. 8). The

evaluation of three new strategies conrmed that the proposed

method generated better strategies from the original seven strate-

gies. For continuous improvement, the analysis process can be iter-

ated to generate new better strategies using the Hybrid Strategy

and Crash_Two Strategy.

6. Discussion

This paper proposes a simulation based method that can be used to

assess and improve the effectiveness of alternative decision strategies

given project uncertainties and priorities. In the case study, the method

was implemented to design better strategies for managing a highway

construction project. By testing and analyzing seven initial contingency

management strategies, new improved decision strategies were gener-

ated on the criteria of low project costs, early project completion dates,

and robust risk control. The hypothesis is validated that appropriately

designed decision strategies can better manage the projects. When

planning complex projects with limited historical information to

depend on, the proposed simulation and analytical methods can be

used for speculative exploration of various contingency management

strategies by using information no more than project documents, as

illustrated by the case study.

The reliability and validity of the results from using the illustrated

method are worth discussing. Given the speculative nature of theproposed simulation method, inductive validation methods that strive

to minimize the difference between model outcomes and system

performance do not apply. At any given time, the proposed method

simulates a distribution of project outcomes. Any parameter character-

izing this distribution cannot be fairly compared with an actual project

realization, as the latter is a single instance from the actual distribution

of outcomes that cannot be observed [16]. In addition, as complex

construction projects are non-prototypical and subject to unique

circumstances, it is likely that the general best practice strategies

across all projects cannot be inductively identied.

A plausible approach lies in the project specic solution that is based

in Popperian falsication. The proposed method is such an approach

that generates satisfactory strategies based on how a project responds

to different strategies. As new evidence becomes available, the strate-

gies are further conditioned or falsied, leading to an iterative continu-

ous improvement process. This strikes a balance between employing

experimental simulation approaches and longitudinal empirical obser-vation. Dreyfus [8] establishes that for complex non-prototypical

domains such as construction this speculative approach is more

valid in developing an understanding of how overarching principles

and policies dene system behavior. In addition, Genova[10]defends

the need and importance of such speculative research, arguing

that validation in such cases requires that the proposed methods be

(i) illustrated to stakeholders who are involved and willing to

use them, and (ii) reproducible and open to improvement by other

academic researchers.

Although the results are fundamentally project specic, the proposed

method provides a general way to generate, test, and improve alternative

strategies at the pre-construction phase of a project. By involving the

decision-maker into the interactive decision-making process, the paper

generated better contingency management practices using the analytical

methods with ICDMA. This current laboratory experimental research will

lay the foundation for future work that intends to directly involve stake-

holders. There are various limitations to this research at this current

point: (i) a knowledge of the relationship between resource policies and

activity's productivity needs to be established; (ii) strategy assessment

metrics, strategy assessment outcomes, criteria to determine satisfactory

strategies, and strategy re-generation demand more standardization

and less arbitrariness; and (iii) a disruptive event database for different

types of construction projects needs to be built.

7. Conclusion

As a departure from existing research that studies isolated decisions,

this paper studies decision strategies to manage construction process.

A method has been designed to generate satisfactory strategies at thepre-construction phase from any series of nominal decision strategies.

This is a supplement to the traditional contingency management [9]

which budgets contingency money to address emergencies. As each

project is unique, the goal of such a decision-making process is to

further the comprehension of how the project and the specic problem

at hand behaves and to improve the decision strategies through an

iterative continuous improvement process, rather than merely

predicting the exact project outcomes. Practically speaking, the

Fig. 7.Accumulated as-built costs through the construction process.



10/10

proposed method lays the foundation to design robust construction

schedules accompanied by effective management strategies that canbe used to control the project during its execution.

Acknowledgment

This work is supported by the Michigan Department of Transporta-

tion under Contract No. #080741 and the National Science Foundation

under Grant No. SES-0624118. Any opinions, ndings and conclusions

or recommendations expressed in this material are those of the authors

and do not necessarily reect views of the Michigan Department of

Transportation and the National Science Foundation.

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29.70

Total Cost (Million $)

Total Cost Duration

Completion Time (Days)

29.65

29.60

29.55

29.50

29.45

29.40

29.35

29.30

29.25

29.20

Crash_One Strategy Crash_Rwo Strategy Crash_Three Strategy Crash_Four Strategy Crash_Five Strategy

140.00

135.00

130.00

125.00

120.00

115.00

110.00

105.00

100.00

Fig. 8.Comparison of simulated as-built total costs for crash strategies.


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