Think. Learn. Succeed.

Surface Congestion Reduction Analysis & Modeling (SCRAM) Team:

Karen DavisGreg HaubnerJames Hingst

Bill JudgeChris Zalewski

Marine Highway SystemA Multimodal Short Sea Freight Shipping System

1 April 2010 – Progress Update

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Agenda – 1 April 2010• Introduction• Short project status update• Interim project briefing

• Update to our first build toward our final briefing• Slides will be populated as the project develops

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Project Summary• This project will validate the Multimodal Short Sea

Freight Shipping (MSSFS) concept in the I-95 corridor region• Customer desires a scalable discrete

event simulation of one or two potential marine highways along this corridor, with an accompanying assessment of their viability based on cost, time, and freight volume.

• (Update) After meeting with our client, reliability is the main driver, not necessarily time or cost

• (Update) Advised to think about the benefits to the truck drivers

Warehouse(origin)

Warehouse(destination)

Seaor

Road

OR 680, Spring 2010 4

Technical Approach

Decision Analysis ToolExcel

DataGathering

Marine Terminal

SimulationArena

Analysis

RecommendationsMultimodalSimulation

Arena Deliverables

@

Final Presentation

Final Report

Project Website

OR 680, Spring 2010 5

Where we are at…• Data

• Reviewing additional freight/market data• Data scrubbing and refining

• Tools• Testing the Prototype Decision Analysis Tool• Initializing Data Migration into Decision Analysis Tool• Developing a discrete event simulation

– Simulations are functional but need some fidelity and scalability enhancements– Meet with Dr. Chen for detailed simulation scrub tomorrow

• Briefing• Building towards the final presentation

• Report• Integrating results and findings into our template

• Web site• Continuation of updating the draft website with final deliverables

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Schedule

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Schedule (continued)

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EVM

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Near Term Progress Goals• 1-Week

• Meet with our client for confirmation on proposed simulation• Finalize data scrubbing• Continue work on decision analysis tool and simulation

• 2-Weeks• Add onto status briefing for Dry Run of Final Presentation• Add onto final report shell• Analyze results• Schedule final meeting with client to present our draft dry run

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May 5th Build

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Background• With the recent exponential growth in GDP, a huge burden has

been placed on the domestic transportation system• The following projections are published on the DOT Maritime

Administration (MARAD) website• Expected volume of trucks on Interstate Highway System is expected to

double between now and 2035• According to MARAD Trucks account for 40% of the time Americans

spend in traffic• In 2007, 60% of federal highway funding went towards maintenance, vice

new construction• Road expansion is not always feasible

• One solution – the underutilized marine highways• DOT established a framework to provide federal support to expand the

use of America’s Marine HighwaysLimited Highway Capacity + Road Maintenance Costs + Traffic Congestion = Marine

Highways

OR 680, Spring 2010

Problem Statement• This project will validate the Multimodal Short Sea

Freight Shipping (MSSFS) concept in the I-95 corridor region• The SCRAM Team will help

validate the MSSFS concept through the use of simulation and optimization

• We will select one inland and one marine route that will serve to reduce traffic bottlenecks in and around I-95 and explore options for moving fright traffic to marine highways

Warehouse(origin)

Warehouse(destination)

Seaor

Road

SCRAM = Surface Congestion Reduction Analysis & Modeling

OR 680, Spring 2010

Assumptions / Limitations• Freight transportation data is split evenly in each direction• No induced traffic resulting from any cargo moved off the

highways onto the waterways• Ship terminal/crane reliability is constant• Manufacturers would be willing to pay more in time and/or cost

for reliable deliveries• A Twenty foot Equivalent Unit (TEU) is our standard for

measuring cargo• Trucker “happiness” is measurable

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CSC/AMCMODELING & SIMULATION

MARINE OPERATIONSMULTIMODAL SYSTEMS

RUTGERS/CAITSFREIGHT TRAFFIC ANALYSIS

INTERMODAL SYSTEMS

THE I-95 COALITIONVIRGINIA DOT

VIRGINIA PORT AUTHORITY NEWYORK/NEWJERSEY PORT AUTHORITY

DLR - GERMAN TRANSPORTATION RESEARCH CENTER

SHORT SEA SHIPPING CRS & SI APPLICATIONS

GEOEYEWORLD LEADERS IN

IMAGERIESCRS&SI DATA SYTEMS

GMUCRS&SI TECH APPLICATIONSINFRASTRUCTURE SYSTEMS

FREIGHT ANALYSIS

Stakeholders: GMU Consortium Team

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What makes this a difficult problem• Freight is generally shipped point to point

– Origin direct to destination– Some areas produce more, others accept more– Shippers pick their own (optimal?) routes

• Result is granular, fragmented shipping– Not a bad quality but difficult to understand

Ref: http://www.marad.dot.gov/ships_shipping_landing_page/mhi_home/mhp_map/mhp_map.htm

OR 680, Spring 2010

SCRAM Team

• Karen Davis• OR, 8 years of experience

• Greg Haubner• OR, 5 years of experience

• James Hingst• OR/Military Analysis, 17 years of experience

• Bill Judge• OR, 3 years of experience

• Chris Zalewski• OR/Decision Analysis, 17 years of experience

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Objective and Scope• Build a scalable model• Determine the feasibility of transferring land-based

freight to marine highways based on:– Cost comparison of marine and surface routes– Reliability comparison of end-to-end transportation time

(including variability) using marine and surface routes

• Estimate any environmental benefits

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Metrics

• Comparison of modes: short sea vs. land-based• Time (and variability)• Throughput volume• Transportation Costs

• Outcome• Assess total benefit of the alternate transportation system compared to

highway freight transportation for selected routes• Direct

• Improving reliability and lowering freight ??• Indirect

• Safety• Congestion mitigation• Emission reduction• Savings in highway maintenance costs• Truck driver “happiness”

OR 680, Spring 2010 19

Technical Approach

Decision Analysis ToolExcel

DataGathering

Marine Terminal

SimulationArena

Analysis

RecommendationsMultimodalSimulation

Arena Deliverables

@

Final Presentation

Final Report

Project Website

OR 680, Spring 2010

Model – Decision Analysis Tool

• The Decision Analysis Tool will help test the viability of port-port pairs along the I-95 corridor

• The initial baseline scenarios will compare routes between Richmond, VA and Norfolk, VA

• After analysis of baseline scenario and updates made to the tool as needed, other origin/destination scenarios will be selected to test the scalability of the tool

• Current Status– Tool has been reworked to accept actual port costs– Freight data will be integrated within a week

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Model – Decision Analysis Tool

• Assumptions:• Speed, land and sea, degraded by congestion• Delays associated with transshipment, waiting for a ship relative to ship size• Fuel consumptions and cost• Market share relative to port proximity• Harbor maintenance tax?

• Data• County to Count freight data• Land and Sea distances• Congestion index

• Outputs• Traffic diverted from highways (congestion relief)• End to end time• Total costs• Fuel consumption• Ships necessary for certain frequency of service (integer constraints) 21

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Model – Decision Analysis ToolAssumptions

Speed(miles per hour)land 55sea 25Transshipment delay hours per debarkland 0sea 10Transshipment cost ($/TEU)RORO 8LOLO 10Mileage(TEU*Miles / gallon)land 12ship sizes and types TEU Capacity Transshipment delay time(Hrs) Mileage(TEU*Miles/Gallon)LOLO TEU/hr loaded 20small 100 5 10medium 300 15 14large 1000 200 18RORO TEU/hr loaded 40small 75 1.875 8medium 200 106.6666667 12large 500 4.6875 16

Market share assumption relative to distance from endpoints A-B

25 0.650 0.5

100 0.4150 0.3200 0.2250 0.1

Diesel ($/gallon) 3HMT ?

DataPair Specific infoA-B Distance(Statute Miles)Land 400Sea 350Time(hours)land 7.272727273sea 14Congestion index 0.6

Monthly shipping non cumulative volume in TEU within total distane of XX from proposed endpoints A-B B-A

25 100 5050 200 125

100 300 200150 500 250200 600 400250 800 500

CalculationsAvailable Volume A-B B-A

25 630 377.5

Monthly ShiploadsLOLOsmall 7medium 3large 1ROROsmall 9medium 4large 2

Timeland 7.3seaAverage Drayage(miles) 90.47619048 2.7steaming time 14.0Delays for various ship sizes and types Transshipment delay time(Hrs) waiting for full ship delayLOLOsmall 2.5 51.4 53.9medium 7.5 120.0 127.5large 100.0 360.0 460.0ROROsmall 0.9 40.0 40.9medium 53.3 90.0 143.3large 2.3 180.0 182.3

total time

Costland per TEU 100seaAverage Drayage(miles) 106.7599491 26.69steamingDelays for various ship sizes and types steaming costs loading costsLOLOsmall 105 105medium 75 75large 58.33333333 58.3333ROROsmall 131.25medium 87.5large 65.625

#DIV/0!total time 265.023

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Model – Discrete Event Simulations• Two discrete event simulations will be built in Arena to measure

throughput times, costs, and resource usage• Part I

• Derive an estimate of cargo loading times at marine ports

• Part II• Validate the results of the optimization• Capture key metrics (costs & times)

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Model – Marine Terminal Simulation• The simulation of the dock operations is important since trans-

loading containers to shipping vessels poses significant time and cost penalties

• The proposed trade-off is that while slower, the marine highway can move substantially more cargo at once, and more importantly,relieve congestion on surface routes

• The simulation will allow us to estimate how the times of surface and marine routes compare, so we can then propose recommendations to enhance the appeal of marine routes

• Currently, simulation is functional, but still in the process of adding appropriate level of detail

• Once complete, the simulation of the dock operations will be joined to shipping and truck arrival simulations

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Model – Marine Terminal SimulationReal World Process

Port of RichmondImage Ref: http://maps.google.com

Trucks arrive at the portContainers are offloaded and held

until a ship arrivesBoat arrives at the docksContainers are moved from

storage and loaded via cranes onto the ship

Ship departs

This process is similar for theoffloading of ships

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Model – Marine Terminal SimulationReal World

Port of RichmondImage Ref: http://maps.bing.com

Simulation: Container Arrivals

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Model – Marine Terminal SimulationReal World Simulation: Ship Loading

Port of RichmondImage Ref: http://maps.bing.com

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Model – Multimodal Simulation• The Route Model will help validate the Multimodal

Short Sea Freight Shipping (MSSFS) concept along the I-95 and I-64 corridors

• Simulate over a longer period of time (months) due to slow loading times for the ships

• Compare the key metrics between different destinations and freight transportation methods• Truck, RoRo, Small/Medium/Large container ships• Time to transport, # TEUs moved, Costs, Fuel used

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Model – Multimodal SimulationReal World Simulation

Richmond / Baltimore / Port of VirginiaImage Ref: http://maps.google.com

RichmondRichmond

BaltimoreBaltimore

Port of VirginiaPort of Virginia

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Reliability Metric• Reliability defined as the

ability to get a product from Point A to Point B in a known amount of time

• Shown are time of arrival distributions

• Further you move a TEU from the terminal, the less reliable (green, blue, red)

• For Truck movement, “+” mean times are for a destination further from the terminal, “-” are closer (more or less distance to drive)

Distance fromTerminal (miles)

Orig

in –

Por

t of V

irgin

ia

Destination – Port of Richmond

± 5 ± 10

± 5

± 10

0

0

Truck

µ=4µ+5=4.5µ−5=3.5

µ+10=5.5µ−10=2.5

OR 680, Spring 2010 31

Metrics, Analysis, Recommendations

RoRo SmallContainer

MediumContainer

LargeContainerTruck

PoV PoR PoB PoV PoR PoB PoV PoR PoB PoV PoR PoBPoVPoRPoB

Destination Destination DestinationDestination

Orig

in

PoV: Port of Virginia // PoR: Port of Richmond // PoB: Port of Baltimore

• Want to validate multi-modal short sea shipping viability, routes, direct and indirect benefits

• If needed, recommend policy (incentives) changes that may help the viability

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Analysis and Sensitivity Analysis• From the Decision Analysis Tool we found…

• From the Marine Terminal Simulation we found…

• From the Multimodal Simulation we found…

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Findings – Roadblocks• Costs

• At this time, dock usage costs seem prohibitively high for short-sea shipping

• Shipbuilding• The U.S. currently doesn’t build many transport ships,

except for military• Would have to purchase ships from other countries

• Truck• Domestic based trucking is not standardized (i.e., various

sizes)

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Recommendations• Based on our analysis, we recommend

• TBD…• TBD…• TBD…

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Future Work• Expand the short-sea shipping problem to regions beyond

the Virginia/I-95 region.• Develop and implement the data architecture required to

“feed” the data analysis tool• Other TBD…

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Acknowledgements• Dr. Thirumalai• Dr. Chen• Dr. Laskey• Family & Friends

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For More Information Visit• http://seor.gmu.edu/projects/grad-proj.html

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Questions / Comments

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Thank you!

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Backup

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Work Breakdown Structure