Conference Paper

NREL/CP-7A2-47844

June 2010

Geospatial Analysis and 

Optimization of Fleet Logistics to 

Exploit Alternative Fuels and 

Advanced Transportation 

Technologies

Preprint

W. Sparks and M. Singer

National Renewable Energy Laboratory

To be presented at TechConnect World Conference & Expo 2010 

Anaheim, California 

June 21–25, 2010
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Geospatial Analysis and Optimization of Fleet Logistics to Exploit Alternative Fuels 

and Advanced Transportation Technologies

W. Sparks* and M. Singer**

*

1617 Cole Blvd., Golden, CO, USA, witt.sparks@nrel.gov 

National Renewable Energy Laboratory 

**

1617 Cole Blvd., Golden, CO, USA, mark.singer@nrel.gov 

National Renewable Energy Laboratory 

ABSTRACT

Today's fleet managers are faced with new challenges 

posed by government mandates and private-sector goals 

that include reducing petroleum use, reducing greenhouse 

gas (GHG) emissions, and—for federal and other 

government fleets—complying with alternative-fuel 

mandates. Choosing the right technologies to reach these 

goals is a complex process that requires an understanding of 

the technological landscape in addition to the makeup and 

operational requirements of each fleet. Funded by the U.S. 

Department of Energy's Federal Energy Management 

Program (FEMP) and Vehicle Technologies Program, the 

National Renewable Energy Laboratory (NREL) is 

developing geographical information system (GIS) tools to 

evaluate alternative fuel availability in relation to garage 

locations and to perform automated fleet-wide optimization 

to determine where to deploy alternative fuel and advanced 

technology vehicles and fueling infrastructure.

Keywords: fleet, alternative fuel, GIS, optimization, 

transportation

1 IMPETUS

The United States Energy Policy Act of 2005 and 

resulting regulations require federal fleets to fuel their 

alternative fuel vehicles with alternative fuel if suitable fuel 

is available within five miles of the vehicle's garage 

location. [1]. This requirement led to the development of a 

geospatial analysis capability to determine, using the 

Department of Energy's database of alternative fueling 

stations [2], which federal vehicles are subject to the 

requirement. 

In addition, Executive Order 13514 directs federal 

agencies to prepare a mult-year plan for reducing carbon 

emissions. This goal is another factor motivating fleets to 

maximize the use of alternative fuels and advanced vehicle 

technologies.

Both federal and private fleets are pursuing goals that 

can be addressed using alternative fueling and advanced 

vehicle technologies. These goals include reducing 

petroleum consumption to reduce costs or promote U.S. 

energy security and reducing fleet greenhouse gas 

emissions. 

Unlike gasoline and diesel fuel, alternative fuels such as 

biodiesel, ethanol, natural gas, and electricity are not 

available universally. Before investing in alternative fuel 

vehicles, a fleet must establish that either a source for an 

alternative fuel exists or that sufficient fuel will be used to 

justify the installation of fueling infrastructure. This 

analysis is inherently spatial, and the easiest way for a fleet 

manager to understand the situation is to examine the data 

on a map.

2 GIS ANALYSIS

Fleet Atlas is a Web-based application developed by 

NREL that helps users visualize their garage locations and 

nearby alternative fueling stations on a map. The 

application is based on a largely open-source software 

stack. Data are housed in an Oracle database that includes 

Oracle Locator [3], a subset of Oracle Spatial. MapServer

[4], Tilecache [5], and Feature Server [6] are used to serve 

map data to the client Web application. The client is written 

using ExtJS [7] and OpenLayers [8]. The application 

supports basic features found in GIS, including pan, zoom, 

find location, query, and custom styling of data layers.

After fleet location data have been imported into the 

database, the application automatically generates data 

layers showing garage locations as well as those alternative 

fueling stations located within five miles of a garage 

location. The alternative fueling station layers are separated 

by fuel type, which includes E85 ethanol, biodiesel, 

compressed natural gas, liquefied natural gas, liquefied 

propane gas, hydrogen, and electricity. This information 

can be used to answer questions such as:

• Which garages have access to which alternative 

fuel? This answer can be used to identify locations 

where alternative fuel vehicles should be added to 

the fleet and which fuels those vehicles should 

utilize.

• Which garages do not have access to alternative 

fuel? This answer identifies where not to place 

alternative fuel vehicles and where to consider 

1
utilizing other technologies, such as hybrid 

vehicles, or installing infrastructure.

If users agree to share their data with other fleet users, 

the tool can be used to identify opportunities for 

collaboration. For example, if several fleets have garage 

locations near a single point, it might be possible to justify 

the installation of fueling infrastructure based on the 

aggregate number of vehicles among all fleets, but an 

individual fleet might not have enough vehicles to justify 

the investment.

The tool also provides data layers depicting flex-fuel, 

diesel, and hybrid-electric vehicle densities in the overall 

U.S. vehicle fleet. When fleet managers are deciding 

whether to install fueling infrastructure, these data indicate 

the number of potential customers outside their fleet.

Figure 1: Fleet Atlas showing an example fleet with nearby 

E85 fueling stations

2.1 Data Quality

The biggest hurdle to overcome is the quality of the 

input data. For large fleets, it is sometimes difficult to 

gather all the data for the garage locations with accuracy—

particularly to the address level—and store them in a single 

data repository. Federal fleets, for example, often have 

garage locations to the ZIP-code level. Performing a GIS 

analysis with ZIP-code-level accuracy implicitly assumes 

the garage is located at the geographic center of the ZIP 

code. While this can provide a reasonable approximation, 

there will be cases in which this assumption introduces 

errors.

2.2 Future Enhancements

Potential enhancements to the tool include:

• Customizing the search radius for nearby stations. 

This will allow the user to perform custom analysis 

using a driving radius from the garage location to 

the fueling station location appropriate to that fleet's 

situation.

• Filtering garage locations to show only those 

garages where alternative fuel is not available 

nearby. This will facilitate an analysis of where to 

place advanced technology vehicles that rely on a 

traditional fuel (such as hybrids), and where to 

consider adding fueling infrastructure.

• Downloading tabular reports. This feature is 

necessary to fulfill certain government reporting 

requirements and provides the ability to export data 

to perform further analysis.

3 OPTIMIZATION

Federal agency fleets have aggressive targets for 

reducing petroleum consumption and greenhouse gas 

emissions. NREL, funded by FEMP, is working with 

federal fleet managers to develop an optimization 

application as a decision-support tool that will allow 

managers to compile fleet descriptive data, apply mandated 

and situational constraints, and solve for optimal solutions. 

Fleet managers will be able to test multiple strategies for 

both positive and negative ramifications, which will allow 

for informed decision-making as they strive to meet their

energy goals.

3.1 Software 

The optimization tool is being developed in the FICO 

Xpress Optimization Suite [9] that runs the Mosel 

optimization coding language. It is primarily an integeroptimization program constructed of a system of linear 

equations defining all fleet constraints in terms of the 

decision variables to be optimized. The optimizer returns 

the best-possible value for the decision variables given the 

constraints. The software is valuable in solving over large 

samples. In this case, the model handles thousands of 

vehicle combinations spread over thousands of possible 

locations.

3.2 Primary Challenge 

The optimization tool will primarily benefit fleet 

managers during the fleet-acquisition process when new 

vehicles are sited across diverse locations. At this time, 

managers deal with a myriad of challenges or constraints.

New vehicles must meet the needs of the assigned 

mission. Typically, an acquired vehicle is replacing another 

from the fleet inventory. Currently, the new vehicle must be 

of a similar type as the out-going vehicle to ensure meeting 

mission requirements. Provided that vehicle use data were 

available, the optimization tool could be enhanced to 

identify opportunities for fuel efficiency gains through 

right-sizing, the substitution of a smaller vehicle that still 

meets operational requirements.

2
Fleets are subject to a large number of statutory 

mandates and executive order requirements. For example:

• A portion of newly acquired vehicles must be 

alternative fuel vehicles (AFVs).

• These AFVs must be fueled by alternative fuel, or if 

none is available, the fleet must apply for a waiver 

to fuel the vehicle with conventional petroleum 

fuel.

• Fleets must increase the use of alternative fuel and 

decrease the use of petroleum.

In addition to the constraints of meeting mission needs 

and mandates, fleet managers operate with budgetary 

constraints. 

3.3 Fleet Goals

Fleet managers approach the vehicle-acquisition process 

with different strategies for reaching targets related to 

reducing petroleum consumption and GHG emissions. One 

option is to increase the use of AFVs, but there are 

challenges to the strategy. Mission needs or fuel availability 

might seem to prevent the possibility of aggressive 

alternative fuel use. Aside from alternative fuel goals, 

conflicting management priorities and a limited budget

complicate decision-making. For instance, fleets might 

have a need to invest in vehicle maintenance capabilities, 

fueling infrastructure, or special-use vehicles, such as fire 

trucks or large busses that fall outside the typical 

acquisition cycle.

Given these site-specific needs, some AFV-related 

strategies a fleet manager might wish to investigate include:

• Maximizing the acquisition of AFVs

• Maximizing access of AFVs to alternative fuel

• Minimizing cost while complying with all 

requirements

• Optimally locating new alternative fuel pumps.

3.4 Optimization Example

The situation of a typical example federal fleet in 2005 

illustrates the capabilities of the optimization tool:

• Roughly 2,200 vehicles were due for replacement. 

• 59 different vehicle types were being replaced. 

• 41 different AFVs were available as replacements 

for each out-going vehicle. 

• Vehicles were garaged in roughly 900 different 

locations.

• The fleet had a $1.2M incremental budget to 

purchase AFVs.

• The Energy Policy Act of 1992 (EPAct) requires 

that 75% of vehicles acquired in Metropolitan 

Statistical Areas (MSAs) be AFVs. Fleets receive 

EPAct credits for the AFVs they acquire, and the 

fleet required over 1,200 credits.

• As an internal challenge, the fleet was in need of 

funding for fire trucks at $400,000 per truck.

Table 1 summarizes five strategies developed in prior 

fleet modeling efforts [10] that could have been provided to 

the fleet manager to support decision-making. In addition to 

the high-level summary output, the fleet manager would 

receive the detailed listing of specific vehicle replacements.

3.5 Strategy 1: Actual Results

The first scenario depicts the actual fleet acquisitions 

that were decided without the aid of an optimization tool.

Historically, the AFV-acquisition requirement has driven 

agencies to acquire as many AFVs as possible regardless of 

the availability of alternative fuel. This example is no 

exception. The fleet spent the entire $1.2M budget while 

acquiring many more AFVs than mandated, and only 10.6% 

of the vehicles were sited to allow for access to alternative 

fuel.

3.6 Strategy 2: Max EPAct

The goal of the "Max EPAct" scenario is to maximize 

the number of AFV EPAct credits acquired. As can be seen, 

the actual fleet acquisitions came very close to the 

optimized maximum. Nearly double the number of 

mandated AFV credits would be acquired, and the budget 

would be fully exhausted with a marginal increase in the 

number of AFVs with access to alternative fuel.

Strategy Cost $

EPAct Credits

Acquired EPAct %

# of Alternative 

Fuel Pumps Built

% Acquired 

AFVs with

Access to AF

1. Actual Results $1.2M 2,161 >100% 0 10.6%

2. Max EPAct $1.2M 2,182 134% 0 13.1%

3. Min Cost $281K 1,239 75% 0 12.5%

4. Max AF Use $409K 1,232 75% 0 23.1%

5. Max AF Use, 

Infrastructure Allowed $822K 1,231 75% 9 41.3%

Table 1: Example fleet optimization output

3
3.7 Strategy 3: Min Cost

"Min Cost" represents a scenario where the fleet would 

attempt to meet the minimum 75% AFV-acquisition 

requirement while minimizing the budget impact. The 75% 

mandate would indeed be met, but less than a quarter of the 

budget would be spent to meet this goal. 

3.8 Strategy 4: Max AF Use

The "Max AF Use" scenario has a goal of meeting the 

75% requirement, minimizing cost, and maximizing the 

number of AFVs with access to alternative fuel by 

optimizing the placement of newly acquired AFVs in 

locations with access to alternative fuel. The results show 

the fleet could have spent a small fraction of the budget, 

met the AFV-acquisition mandate, and more than doubled 

the percentage of acquired AFVs with access to alternative 

fuel.

3.9 Strategy 5: Max AF Use, Infrastructure 

Allowed

The "Max AF Use, Infrastructure Allowed" scenario is 

similar to the previous but includes the option of the fleet 

funding the construction of new alternative fuel 

infrastructure located where the maximum number of AFVs 

would have access to the fuel. The results show that the 

agency could have met the AFV-acquisition mandate, 

constructed nine alternative fuel pumps, and quadrupled the 

percentage of acquired AFVs with access to alternative 

fuel. Even under this aggressive approach, the organization 

would have sufficient funds remaining to purchase a new 

fire truck. 

While the gains for these scenarios appear significant 

for a single year, the real benefits come year after year as 

fleets apply similar analysis and continue to optimize the 

use of funds and maximize alternative fuel use.

3.10 Ongoing Development

The initial benefit of the described model is that it 

allows a fleet manager to see the cross-cutting impacts of 

the various strategies. Further development of the 

optimization tool will include analyses of the following:

• How can a fleet efficiently transfer AFVs within the

fleet to locations where alternative fuel is available?

• How can a fleet downsize by optimally selecting the 

appropriate vehicles to remove?

• How can a fleet best meet the overarching goals of 

reducing petroleum consumption and greenhouse 

gas emissions?

With the basic structure of the model in place, the 

optimization tool is highly flexible and able to handle new 

constraints or goals. New vehicle and fueling technologies 

are expected to continue to be developed. These 

technologies include plug-in hybrid electric vehicles, 

electric vehicles, and new fuel alternatives. The 

optimization tool will be able to support fleet managers 

acquiring these new technologies as they deploy them in 

their fleets.

Both the optimization tool and the GIS analysis tool are 

currently under development at NREL. The tools are being 

used to provide analytical support within FEMP with the 

intention of eventual public release.

4 CONCLUSION

The use of GIS and optimization tools and techniques 

can help fleet managers meet the petroleum and greenhouse 

gas emissions reduction goals that are increasingly part of 

efforts to move toward sustainable communities, while 

staying within budget and ensuring that the vehicle fleet 

continues to meet mission requirements.

REFERENCES

[1] United States Department of Energy, “Draft 

Guidance: Documentation Requirements for Waiver 

Requests under EPACT 2005 Section 701,” Federal 

Energy Management Program, September 2009. 

http://www1.eere.energy.gov/femp/pdfs/701_guida

nce.pdf 

[2] United States Department of Energy, "Alternative 

Fueling Stations," Alternative Fuels and Advanced 

Vehicles Data Center, 23 March 2010. 

www.afdc.energy.gov/afdc/fuels/stations.html

[3] Oracle Corporation, "Oracle Spatial and Locator: 

Location features for Oracle Database 11g," Oracle 

Technology Network, 23 March 2010. 

www.oracle.com/technology/products/spatial/index.

html 

[4] MapServer Project Steering Committee, 

"MapServer," 23 March 2010. www.mapserver.org

[5] MetaCarta Labs, "TileCache," Web Map Tile 

Caching, 23 March 2010. www.tilecache.org

[6] FeatureServer, "RESTful Geographic Feature 

Storage," 23 March 2010. www.featureserver.org

[7] ExtJS, "JavaScript Framework and RIA Platform," 

23 March 2010. www.extjs.com

[8] Open Source Geospatial Foundation, "OpenLayers: 

Free Maps for the Web," 23 March 2010. 

www.openlayers.org

[9] Fair Isaac Corporation, "FICO Xpress Optimization 

Suite," 29 March 2010. www.fico.com/en/Products 

[10]Helwig, Michael, “The Energy Policy Act of 1992 

and Executive Order 13149: Proposed Compliance 

Strategies and Process Improvements for Federal 

Agencies,” Ph.D. dissertation, George Washington 

University, 2006.

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Geospatial Analysis and Optimization of Fleet Logistics to Exploit 

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14. ABSTRACT (Maximum 200 Words)

This paper describes how the National Renewable Energy Laboratory (NREL) is developing geographical information 

system (GIS) tools to evaluate alternative fuel availability in relation to garage locations and to perform automated 

fleet-wide optimization to determine where to deploy alternative fuel and advanced technology vehicles and fueling 

infrastructure.

15. SUBJECT TERMS

geographical information system; GIS; alternative fuel; alternative fuel vehicle; AFV; garage locations; deployment; 

advanced technology vehicles; fueling infrastructure

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