CHANDLER VAUGHAN THE INVENTION OF THE STEEL PLOW Deeres second manufactured plow.
COMPUTING A BETTER WAY TO PLOW IN NORTHEASTERN MINNESOTA
description
Transcript of COMPUTING A BETTER WAY TO PLOW IN NORTHEASTERN MINNESOTA
MDSS Stakeholder MeetingJune, 18 2003
1
COMPUTING A BETTER WAY TO PLOW IN NORTHEASTERN
MINNESOTA
Kwasi D. Amoah, Graduate Student, MSEM
University of Minnesota DuluthDepartment of Mechanical & Industrial Engineering
Northland Advanced Transportation Systems Research Laboratory
MDSS Stakeholder MeetingJune, 18 2003
2
Background
• Snowplow Operations & Resource Management (SORM)
• Funded through the Minnesota Department of Transportation (Mn/DOT) and the Center for Transportation Studies (CTS), University of Minnesota
• Focus on Winter Maintenance issues in District 1 of Mn/DOT
MDSS Stakeholder MeetingJune, 18 2003
3
Concerns
• Efficient and effective utilization of resources• Consistent service levels for roadways• Previous attempts in implementing route
optimization software by Mn/DOT failed • Federal Highway Administration’s development
of multi-state decision support system for winter road maintenance
MDSS Stakeholder MeetingJune, 18 2003
4
Goal• Develop a Management Planning
Tool (Decision Support System) using Discrete Event Simulation Modeling to assist in– Operations Improvement– Resource Utilization– “What-if” Scenarios
MDSS Stakeholder MeetingJune, 18 2003
5
Why Simulation?
• Complexity of operations cannot be modeled by analytical methods
• Provides a mechanism for developing and analyzing different scenarios
• Serves as a general management tool that can be modified to meet future needs
MDSS Stakeholder MeetingJune, 18 2003
6
Our Approach
• Work with Virginia, MN
• Select 5 routes on Highway 53 corridor
• Work with Area Superintendent, Supervisors and Drivers to understand operations
Virginia
Clean up
Post Plowing Operations•Dump Extra Sand and Salt•Refuel•Wash•Complete ODVI report
End of Storm?
Storm Arrives
Bare Lane?
Drivers arrive atMn/DOT in Virginia
Drivers completeOperators Daily Vehicle
Inspection Report
Plowing
Load Plowswith Sand & Salt
Fill Brine
No
End of Shift?Yes
No
No
Yes
Yes
Clean up completed?No Yes
Flow Process
Concept Map
Plow Speed
Traffic density
Visibility
SnowMoisture Content
Snow Depth
Time of Day
Pavement Temperatur
e
Air Temperature
Snow Accumulation Rate
Sun
Humidity
Wind Speed & Direction
Concept Map - Simplified
Pavement Temperature
Material Application
Plow Speed
Traffic density
Visibility
SnowMoisture Content
Snow Depth
Snow Accumulation Rate
Input Data Collection & Modeling
Data Collection• “Expert Opinion”
– Plow Speeds
• RWIS– Accumulation Rates– Moisture Content– Pavement
Temperature
• Virginia - Mn/DOT Guidelines– Material Application
Rates
Probability Density Functions
• Plow Speed– Triangular Distribution– Beta Distribution
• Accumulation Rates, Moisture Content and Pavement Temps– Fit distribution using RWIS
data
• Material Application Rates– Fit distribution using
historical application data from Mn/DOT
MDSS Stakeholder MeetingJune, 18 2003
11
Model Layout
MDSS Stakeholder MeetingJune, 18 2003
12
Start Simulation
MDSS Stakeholder MeetingJune, 18 2003
13
Model Design & Data Flow
Route Characteristics
Storm Characteristics
MS Excel VBA ProModel
Road Conditions
Output Data
Model Parameters Simulation ModelActiveXCOM
MDSS Stakeholder MeetingJune, 18 2003
14
Output
Summary Report: Route 201
Rural Commuter; Lane Miles = 81.92 Number of Replications = 5
Name Units Minimum Value Maximum Value Avg Value
Storm Start Time Minutes 0 0 0
Storm End Time Minutes 0 300 300.000
Time To Bare Lane Minutes 0 453.22 453.220
Time To Bare Pavement Minutes 0 284.29 284.290
Material Applied lbs 0 98304 98304.000
Material Cost $ 0 324.4 324.400
Labor Cost $ 0 321.13 321.130
MDSS Stakeholder MeetingJune, 18 2003
15
Conclusions
• The model adequately captures the plowing operation
• Initial Validation suggests that the model might be inadequate in predicting times to bare lane
• Validation will be an ongoing effort as more “accurate” data on the model parameters are obtained and as the scope of the model expands
• Verify Processing Logics & “Rules of Practice”• Model has capability of conducting “what-if”
scenarios to assist with operations improvement and resource management
• Continue to improve user interface
MDSS Stakeholder MeetingJune, 18 2003
16
Acknowledgements
Center for Transportation StudiesNorthland Advanced Transportation
Systems Research LaboratoryMinnesota Department of
TransportationUniversity of Minnesota DuluthFederal Highway Administration