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![Page 1: OPTIMIZATION Lecture 24. Optimization Uses sophisticated mathematical modeling techniques for the analysis Multi-step process Provides improved benefit.](https://reader035.fdocuments.us/reader035/viewer/2022062518/56649d1f5503460f949f2b1b/html5/thumbnails/1.jpg)
OPTIMIZATION
Lecture 24
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Optimization
• Uses sophisticated mathematical modeling techniques for the analysis
• Multi-step process
• Provides improved benefit to agencies
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Optimization Analysis Steps
• Determine agency goals
• Establish network-level strategies that achieve the goals
• Select projects that match the selected strategies
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Optimization Considerations
• Other techniques are easier to understand
• Loss of control perceived
• Requires individuals with backgrounds in mathematics, statistics, and operations research
• Consistency in data is more important
• Requires sophisticated computers
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Is Optimization Appropriate?
• Select prioritization if:– Management wants to exercise significant control
over the planning and programming exercises.
• Select optimization if:– Management wants to take a global view and is
willing to put substantial faith in a system.
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Objective Function
• Used to express an agency goal in mathematical terms
• Typical objective functions– Minimize cost– Maximize benefits
• Identify/define constraints
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Markov Transition Probability Matrix
9 7 5 39 0.2 0.4 0.3 0.17 0.2 0.6 0.25 0.1 0.3 0.63 0.1 0.9
Future PC StateCurrent PC State
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Markov Assumptions
• Future condition is independent of past condition
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Other Parameters
• Transition costs must be defined– Life-cycle costs– Present worth analysis typically more common
• Heuristic approaches reach near optimal solutions– ICB Ratio
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Example of a Markov Decision Process
• Assumptions– 100 mile network– Two condition states: good (1) or bad (2)– 80% of the network is in good condition– 20% of the network is in poor condition– Two maintenance activities are considered: Do
Nothing (DoNo) and Overlay (Over)
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Transition Probability Matrix
From Condition
States1 2 1 2
1 0.6 0.4 0.95 0.052 0.01 0.99 0.8 0.2
To Condition States
Do Nothing Overlay
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Network Conditions - Year 1Strategy = Overlay All Bad
From Condition
States1 2
1 80%*0.6 = 48 80%*0.4 = 32
2 20%*0.8 =16 20%*0.2 = 4
Total 64% 36%
To Condition States
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Network Conditions - Year 2Strategy = Overlay All Bad
From Condition
States1 2
1 64%*0.6 = 38.4 64%*0.4 =25.6
2 36%*0.8 =28.8 36%*0.2 = 7.2
Total 67.2% 32.8%
To Condition States
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Network Conditions - Year 3Strategy = Overlay All Bad
From Condition
States1 2
1 67%*0.6= 40.2 67%*0.4= 26.8
2 33%*0.8= 26.4 33%*0.2 = 6.6
Total 66.6% 33.4%
To Condition States
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Example Cost Data
Condition State
Action Initial Cost Annual Maintenance
Cost
Total Cost
1 Do Nothing -$ 2,000$ 2,000$
2 Overlay 10,000$ 100$ 10,100$
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Policy Costs - Year 1For Repair Strategy
Condition State
# of mi
Action Cost ($000)
Total Cost ($000)
1 80 Do Nothing $160 $362
2 20 Overlay $202
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Policy Costs - Year 2For Repair Strategy
Condition State
# of mi
Action Cost ($000)
Total Cost ($000)
1 64 Do Nothing $128 $492
2 36 Overlay $364
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Policy Costs - Year 3For Repair Strategy
Condition State
# of mi
Action Cost ($000)
Total Cost ($000)
1 67 Do Nothing $134 $467
2 33 Overlay $333
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Simulation Objectives
• Identify the policy with the minimum expected cost after the system reaches steady state.
• Establish desired long-term performance standards and minimum budgets to achieve standards or short-term objectives to reach steady state within a specified period at a minimum cost.
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Example Network Performance
Projected Performance
00.10.20.30.40.50.60.70.80.9
0 2 4 6 8 10
Years
Pro
po
rtio
n o
f R
oa
ds
Undesirable Condition Desirable Condition
Steady State Begins
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Example Budget Expenditures
Projected Maintenance Budget
0
10
20
30
40
0 2 3 4 5 6 7 8
Years
An
nu
al C
os
ts
(mill
ion
s o
f d
olla
rs)
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Markov Approach
• Advantages
• Disadvantages
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Mathematical Programming Methods
• Linear programming
• Non-linear programming
• Integer programming
• Dynamic programming
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Linear Programming
Objective Functions
Constraints
FeasibleSolutions
Variable Number 2
Variable Number 1
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Non-linear Programming
Constraints
Objective Functions
FeasibleSolutions
Variable Number 2
Variable Number 1
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Integer Programming
Projects Do Nothing Seal Overlay1 0 1 02 1 0 03 0 0 14 0 1 0
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Dynamic Programming
63
A
B
C2
Decision Flow
Solution Flow
End
4
55
3
2
6
Begin
(Costs)
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Selecting the Appropriate Programming Method
• Function of:– Type of variables in analysis– Form of objective function– Sequential nature of decisions
• Typical approaches:– Linear programming most common– Dynamic programming second most common
approach– Non-linear third most common approach– No agency is using integer programming
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Markov Implementation Steps
• Define road categories
• Develop condition states
• Identify treatment alternatives
• Estimate transition probabilities for categories and alternatives
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Markov Implementation Steps (cont.)
• Estimate costs of alternatives
• Calibrate model
• Generate scenarios
• Document models
• Update models
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Case Study - Kansas DOT
• System Components– Network optimization system (NOS)– Project optimization system (POS) (was not fully
operational in 1995)– Pavement management information system
(PMIS)
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Overview of KDOT Data Collection Activities
• Collect pavement distress information
• Monitor rutting
• Collect roughness data
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KDOT M&R Programs
• Major Modification Program
• Substantial Maintenance Program
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KDOT Databases
• CANSYS
• PMIS
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KDOT NOS Analysis
• 216 possible condition states
• Primary influence variables:– Indices to appearance of distress– Rate of change in distress
• Rehabilitation actions based on one of 27 distress states
• Linear programming used to develop programs to maintain acceptable conditions for lowest possible cost
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KDOT POS Analysis
• Projects from NOS are investigated in more detail using POS
• Identify initial designs to maximize user benefits
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Issue paper PMS Steering Committee Pavement Management Task Force Consultant
KDOT System Development
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Summary
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• Understand philosophy of optimization
• Identify concepts involved in optimization analysis
• Identify types of models used in optimization analysis
Instructional Objectives