applying stochastic linear scheduling method to pipeline construction fitria h. rachmat bechtel...
TRANSCRIPT
Applying Stochastic Linear Scheduling Method to Pipeline Construction
Fitria H. RachmatBechtel Corporation, Texas, U.S.
Lingguang Song & Sang-Hoon (Shawn) Lee University of Houston, Texas, U.S.
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
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– 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
Location
Calendar
Floor 2 - 1
July 2
Formwork Rebar
Space Buffer
Time Buffer
July 1
Electrical
Interruption
Floor 2 - 2
A Demo of LSM
Section 1B
Section 2B
Traditional Bar Chart Schedule
Pour Section Layout
Schedule Delay - Elimination
1B
Floor 2
2B
Pour section layout
LSM Chart
Formwork
Concreting
Rebar
Electrical
Research Problem & ObjectivesCurrent Look-ahead Scheduling Practice
Historical dataPersonal experience
Deterministic schedule (CPM or LSM)
Proposed Look-ahead Scheduling Method
Collect actual project data
Collect actual project data
Stochastic LSM simulation
Stochastic LSM simulation
• Use real project data
• Include uncertainty
• Accurate schedules
Automated input modeling
Automated input modeling
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
Date
TaskStation
FootageProductivity
(ft/d)From To
9/15Stringing
5484+00
5636+00 15,000 15,000
9/16Stringing
5636+00
5705+83 6,983 6,983
9/17Stringing
5705+83
5806+00 10,017 10,017
9/18Stringing
5806+00
5972+00 16,600 16,600
9/19Stringing
5972+00
6140+00 16,800 16,800
Sample Actual Productivity Data
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 & OutputsComparison 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.
19
Thank You & Questions