Applying Stochastic Linear
Scheduling Method to
Pipeline Construction
Fitria H. Rachmat
Bechtel Corporation, Texas, U.S.
Lingguang Song & Sang-Hoon (Shawn) Lee
University of Houston, Texas, U.S.
Agenda
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Linear Construction
Linear Scheduling Method (LSM)
Research Problem & Objectives
Stochastic LSM (SLSM)
Case Study
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Pipeline Construction
Data Collection
Automated Input Modeling
SLSM Modeling
Outputs
• Conclusions
Linear Construction Projects
• Characteristics
– Involve a large number of repetitive activities
– Activities occur in succession
– Subject to uncertainty and interruptions
– E.g. high-rise, pipeline, and highway projects
• Project Success
– Effective project scheduling/control
– Ensure continuous work flow w/o interruptions
Pipeline Construction “Assembly Line”
Linear Scheduling Method (LSM)
• LSM
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• Benefits
Designed for linear construction
2D time-space graph
Production line = repetitive task
Line slope = productivity
Location
Formwork
Floor 2 - 2
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Easily model repetitive tasks
Both time & space data
Visualize time/space buffers
Visualize work continuity
Rebar
Time Buffer
Interruption
Space
Buffer
Floor 2 - 1
July 1
July 2
Electrical
Calendar
A Demo of LSM
Section 2B
Pour Section Layout
Section 1B
Traditional Bar Chart Schedule
Schedule Delay - Elimination
Floor 2
Formwork
Rebar
2B
Electrical
Concreting
1B
LSM Chart
Pour section layout
Research Problem & Objectives
Current Look-ahead Scheduling Practice
Historical data
Personal experience
Deterministic schedule
(CPM or LSM)
Proposed Look-ahead Scheduling Method
• Use real project data
Collect actual
project data
Automated
input modeling
Stochastic LSM
simulation
• Include uncertainty
• Accurate schedules
Stochastic Linear Scheduling
Method (SLSM)
• Actual productivity data collection
• Automated input modeling
– Determine distributions of activity productivity
• Simulation Modeling
– Simulation: a mathematic-logic model of a real
world system
– A linear project can be modeled using “Project”
and “Activity” elements in SLSM
• Simulation experiments & outputs
A Case Study
• Case Study
– Construction of ~130 miles of 30” pipeline
• Procedure
– Data collection
– Automated input modeling
– Simulation models
– Output schedules
Data Collection
Sample Actual Productivity Data
Date
Task
Station
Footage
Productivity (ft/d)
From
To
9/15 Stringing
5484+00
5636+00
15,000
15,000
9/16 Stringing
5636+00
5705+83
6,983
6,983
9/17 Stringing
5705+83
5806+00
10,017
10,017
9/18 Stringing
5806+00
5972+00
16,600
16,600
9/19 Stringing
5972+00
6140+00
16,800
16,800
Automated Input Modeling
• Input modeling
– Determine the underlying statistical distribution’s
of an activity’s productivity rate
Automated using
BestFit ®
Automated Input Modeling
Parameters for Fitted Distribution
Actual Productivity Data
Fitted distribution
Input Modeling Outputs
Task Name
Statistical Distributions
Surveying
Exponential with mean =16629
Clearing
Exponential with mean = 9527
Grading
Normal with mean = 2874 and standard deviation = 1363
Trenching
Triangular with low limit = 670, most likely = 1809, and high
limit = 10720
Stringing
Normal with mean = 4837 and standard deviation = 3011
Bending
Beta with a = 2.3, b = 3.4, low = 670, and high = 13812
Welding
Beta with a = 1.2, b = 1, low = 700, and high = 9800
Lower-in
Normal with mean = 5882 and standard deviation = 3033
Tie-in
Exponential with mean = 2007
Backfill
Beta with a = 1.2, b = 2.9, low = 804, and high = 15758
Clean up
Normal with mean = 3688 and standard deviation = 1221
SLSM Modeling
• Establish a “Project” element
• Determine work scope
• Add “Task” elements
• Productivity rate
• Time & space buffer
• Start time
Experiment & Outputs
Comparison of baseline schedule & simulated look-ahead schedule
Experiment & Outputs
Uncertainty analysis of
project total duration
Individual activity
performance range
Conclusions
• Actual project data can be used to enhance
look-ahead scheduling accuracy
• Automated input modeling makes simulation
more accessible to industry practitioners
• SLSM successfully incorporates uncertainty in
traditional LSM method.
Thank You & Questions
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