Applying Stochastic Linear Scheduling Method to Pipeline Construction

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Applying Stochastic Linear Scheduling Method to
Pipeline Construction
ICCEM . ICCPM 2009

• Fitria H. Rachmat
• Bechtel Corporation, Texas, U.S.
• Lingguang Song Sang-Hoon (Shawn) Lee
• University of Houston, Texas, U.S.

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Agenda

• Linear Construction
• Linear Scheduling Method (LSM)
• Research Problem Objectives
• Stochastic LSM (SLSM)
• Case Study
• Pipeline Construction
• Data Collection
• Automated Input Modeling
• SLSM Modeling
• Outputs
• Conclusions

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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

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Pipeline Construction Assembly Line
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Linear Scheduling Method (LSM)

• LSM
• Designed for linear construction
• 2D time-space graph
• Production line repetitive task
• Line slope productivity

• Benefits
• Easily model repetitive tasks
• Both time space data
• Visualize time/space buffers
• Visualize work continuity

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A Demo of LSM
Section 2B
Section 1B
Pour Section Layout
Traditional Bar Chart Schedule
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Schedule Delay - Elimination
Floor 2
Formwork
Rebar
2B
Electrical
Concreting
1B
LSM Chart
Pour section layout
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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
• Include uncertainty
• Accurate schedules

Collect actual project data
Stochastic LSM simulation
Automated input modeling
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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

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A Case Study

• Case Study
• Construction of 130 miles of 30 pipeline
• Procedure
• Data collection
• Automated input modeling
• Simulation models
• Output schedules

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Data Collection
Sample Actual Productivity Data
Date Task Station Station Footage Productivity (ft/d)
Date Task From To Footage Productivity (ft/d)
9/15 Stringing 548400 563600 15,000 15,000
9/16 Stringing 563600 570583 6,983 6,983
9/17 Stringing 570583 580600 10,017 10,017
9/18 Stringing 580600 597200 16,600 16,600
9/19 Stringing 597200 614000 16,800 16,800
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Automated Input Modeling

• Input modeling
• Determine the underlying statistical
  distributions of an activitys productivity rate

Automated using BestFit
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Automated Input Modeling
Parameters for Fitted Distribution
Actual Productivity Data
Fitted distribution
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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
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SLSM Modeling

• Establish a Project element
• Determine work scope
• Add Task elements
• Productivity rate
• Time space buffer
• Start time

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Experiment Outputs
Comparison of baseline schedule simulated
look-ahead schedule
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Experiment Outputs
Uncertainty analysis of project total duration
Individual activity performance range
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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.

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Thank You Questions