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Novel Resource Distribution
Decision Tool to Optimize Complex
Industrial Projects
LEAD INVENTOR
Haitao Li, Ph.D.
Associate Professor, Supply Chain Management & Analytics
University of Missouri – St. Louis
Supported by funding from the UM System
Interdisciplinary Intercampus Research and
FastTrack programs
Such industries include:
o Construction
o Pharmaceutical
o Oil & Gas
o Military
o Supply Chain
o Logistics
Across many industries, optimizing resource management,
supply chain design and logistics is essential in minimizing
costs and keeping projects on time.
Project Management Optimization Needs
What is the Problem?
 Cost over-runs are not uncommon because resources are not
optimally allocated to project tasks
 Uncertainty in task duration caused by variations in crew productivity
 Difficulty of scheduling payments to meet complex payment terms
Current tools designed to save time and money by optimizing project
management are lacking – they aren’t dynamic and very few provide
real-time feedback or other useful functionality.
What is the Problem?
Project Management Challenges
 Optimizes the time-cost tradeoff (crashing) decisions with
complex payment structure and discounted cash flows
 Sequential and adaptive solution approach to capture the
variation of project operations efficiency and productivity
 A data-driven approach closing the gap between decision
support and the “big data” made available through IT
advancement
How Does This Technology Solve
the Problem?
(2) External Disturbance
0 1 2 3
(1) Initial Crashing
Decision
(3) Observe External
Disturbance and Update
Problem Parameters
(4) Optimize the Current
Crashing Decision
…
…
Sequential, Adaptive Solution
Framework
Optimization Model
OUTPUTS
• Option chosen (duration & cost) for
each task
• Start time of each task
• Maximum Return on Revenue
(ROR)
• Optimal project makespan
MIXED-INTEGER PROGRAMMING
(MIP) Model
Optimization Model for Maximum ROR
INPUTS
• Total project contract value
• Project makespan due date
• Duration of options for each task
• Cost of options for each activity
• Indirect cost rate
• Discount and interest rate
• Exogenous disturbances
Maximized return on revenue (ROR)
 ROR Increased by 16% in a real-world construction
project case study involving only 8 tasks (details provided
in the handout)
Improved efficiency for project operations
Duration improved from 65 days to 59 days in the noted
case study
Improved quality of service delivery
Value Proposition
 Optimizes the time-cost tradeoff (crashing) decisions
with complex payment structure and discounted
cash flows
 Leverages the availability of data to provide data-
driven project scheduling and resource
distribution solutions
Market Use
Project Management for the U.S. construction
industry represents a significant, growing market
 Estimated at $1.3 billion in 2018
 Expected to reach between $1.62 billion and $2.71 billion by
2024
 Growing at a CAGR of between 7.9% and 8.8% (2019-24)
 Of smaller construction firms:
 1/3 currently use construction software
 1/3 used generic tools such as email/spreadsheets
 28% cite improved workflow efficiency as their top need/priority
Sample Target Industry: Construction
Tool Task
Scheduling
Cash-Flow
Prediction
Payment
Scheduling
Dynamic,
Adaptive
Real-Time
Feedback
MS Project Yes No No No No
Primavera Yes Yes No No No
Buildertrend,
Contractor
Foreman
Yes No No No No
CoConstruct,
Raken, RedTeam,
CMiC
Yes No No No Yes
PASKR Yes No No No No
PlanGrid No No No No No
ProCore, eSub Yes No No No Yes
OUR TOOL Yes Yes Yes Yes Yes
Competition
The IP includes both copyrights and patent
rights.
 Provisional (62/454,511) filed 02/03/17
 PCT/US2018/16369 filed 02/01/18
 US National Stage application filed 08/03/19
Intellectual Property
 A prototype has been developed with:
web-based architecture using ASP technology (eliminates the
need for user to purchase optimization solver);
successful integration of GUI, database and optimization
models; and
controlled user portal and privilege
 Testing with real-world construction company data
completed
Development Stage
 Design and development of user interface for enhanced
user experience
 Automation of workflow for users with different roles in a
project
 Cloud-based architecture to support mobile access
 Seamless access and data exchange with project
management applications (e.g., MS Project)
 Additional testing and de-bugging of the tool
Further Development Needed
 Complex Projects need tools to effectively and efficiently
allocate resources
 Resource allocation decisions have a huge impact on finish
times and ROR
 Our technology provides dynamic optimization decision
support functionalities not offered in currently available
tools
Summary
Thank You!
Contact: Tamara Wilgers
Director, Technology Commercialization
wilgerst@umsl.edu
314-516-6884
Case Study
BRIDGE CONSTRUCTION PROJECT
Activity
number
Activity description Crew ID
1
Shop drawings, abutment, and deck
steel
FAB
2 Shop drawings, foot steel FAB
3 Move in N/A
4 Deliver piles N/A
… …
35 Painting B78
36 Guardrail N/A
37 Clean up A5
38 Inspection N/A
BRIDGE CONSTRUCTION CASE STUDY:
Activities
BRIDGE CONSTRUCTION CASE STUDY:
Project Network
• Labor Hours = Total Mhrs / Daily Output
• Daily Output is assumed to be a non-linear function of crew size to
model the diminishing return of crew size
BRIDGE CONSTRUCTION CASE STUDY:
Activity Info for Updating
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Before Project Start Above Cost & Ahead Schedule Above Cost & Behind Schedule Below Cost & Behind Schedule
All-Normal Optimal Solution
16.0%
16.0%
19.5%
2.8%
BRIDGE CONSTRUCTION CASE STUDY:
Computational Results Summary

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Novel tool to optimize complext industrial projects

  • 1. Novel Resource Distribution Decision Tool to Optimize Complex Industrial Projects LEAD INVENTOR Haitao Li, Ph.D. Associate Professor, Supply Chain Management & Analytics University of Missouri – St. Louis Supported by funding from the UM System Interdisciplinary Intercampus Research and FastTrack programs
  • 2. Such industries include: o Construction o Pharmaceutical o Oil & Gas o Military o Supply Chain o Logistics Across many industries, optimizing resource management, supply chain design and logistics is essential in minimizing costs and keeping projects on time. Project Management Optimization Needs What is the Problem?
  • 3.  Cost over-runs are not uncommon because resources are not optimally allocated to project tasks  Uncertainty in task duration caused by variations in crew productivity  Difficulty of scheduling payments to meet complex payment terms Current tools designed to save time and money by optimizing project management are lacking – they aren’t dynamic and very few provide real-time feedback or other useful functionality. What is the Problem? Project Management Challenges
  • 4.  Optimizes the time-cost tradeoff (crashing) decisions with complex payment structure and discounted cash flows  Sequential and adaptive solution approach to capture the variation of project operations efficiency and productivity  A data-driven approach closing the gap between decision support and the “big data” made available through IT advancement How Does This Technology Solve the Problem?
  • 5. (2) External Disturbance 0 1 2 3 (1) Initial Crashing Decision (3) Observe External Disturbance and Update Problem Parameters (4) Optimize the Current Crashing Decision … … Sequential, Adaptive Solution Framework
  • 6. Optimization Model OUTPUTS • Option chosen (duration & cost) for each task • Start time of each task • Maximum Return on Revenue (ROR) • Optimal project makespan MIXED-INTEGER PROGRAMMING (MIP) Model Optimization Model for Maximum ROR INPUTS • Total project contract value • Project makespan due date • Duration of options for each task • Cost of options for each activity • Indirect cost rate • Discount and interest rate • Exogenous disturbances
  • 7. Maximized return on revenue (ROR)  ROR Increased by 16% in a real-world construction project case study involving only 8 tasks (details provided in the handout) Improved efficiency for project operations Duration improved from 65 days to 59 days in the noted case study Improved quality of service delivery Value Proposition
  • 8.  Optimizes the time-cost tradeoff (crashing) decisions with complex payment structure and discounted cash flows  Leverages the availability of data to provide data- driven project scheduling and resource distribution solutions Market Use
  • 9. Project Management for the U.S. construction industry represents a significant, growing market  Estimated at $1.3 billion in 2018  Expected to reach between $1.62 billion and $2.71 billion by 2024  Growing at a CAGR of between 7.9% and 8.8% (2019-24)  Of smaller construction firms:  1/3 currently use construction software  1/3 used generic tools such as email/spreadsheets  28% cite improved workflow efficiency as their top need/priority Sample Target Industry: Construction
  • 10. Tool Task Scheduling Cash-Flow Prediction Payment Scheduling Dynamic, Adaptive Real-Time Feedback MS Project Yes No No No No Primavera Yes Yes No No No Buildertrend, Contractor Foreman Yes No No No No CoConstruct, Raken, RedTeam, CMiC Yes No No No Yes PASKR Yes No No No No PlanGrid No No No No No ProCore, eSub Yes No No No Yes OUR TOOL Yes Yes Yes Yes Yes Competition
  • 11. The IP includes both copyrights and patent rights.  Provisional (62/454,511) filed 02/03/17  PCT/US2018/16369 filed 02/01/18  US National Stage application filed 08/03/19 Intellectual Property
  • 12.  A prototype has been developed with: web-based architecture using ASP technology (eliminates the need for user to purchase optimization solver); successful integration of GUI, database and optimization models; and controlled user portal and privilege  Testing with real-world construction company data completed Development Stage
  • 13.  Design and development of user interface for enhanced user experience  Automation of workflow for users with different roles in a project  Cloud-based architecture to support mobile access  Seamless access and data exchange with project management applications (e.g., MS Project)  Additional testing and de-bugging of the tool Further Development Needed
  • 14.  Complex Projects need tools to effectively and efficiently allocate resources  Resource allocation decisions have a huge impact on finish times and ROR  Our technology provides dynamic optimization decision support functionalities not offered in currently available tools Summary
  • 15. Thank You! Contact: Tamara Wilgers Director, Technology Commercialization wilgerst@umsl.edu 314-516-6884
  • 17. Activity number Activity description Crew ID 1 Shop drawings, abutment, and deck steel FAB 2 Shop drawings, foot steel FAB 3 Move in N/A 4 Deliver piles N/A … … 35 Painting B78 36 Guardrail N/A 37 Clean up A5 38 Inspection N/A BRIDGE CONSTRUCTION CASE STUDY: Activities
  • 18. BRIDGE CONSTRUCTION CASE STUDY: Project Network
  • 19. • Labor Hours = Total Mhrs / Daily Output • Daily Output is assumed to be a non-linear function of crew size to model the diminishing return of crew size BRIDGE CONSTRUCTION CASE STUDY: Activity Info for Updating
  • 20. 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 Before Project Start Above Cost & Ahead Schedule Above Cost & Behind Schedule Below Cost & Behind Schedule All-Normal Optimal Solution 16.0% 16.0% 19.5% 2.8% BRIDGE CONSTRUCTION CASE STUDY: Computational Results Summary