Aligning Unmanned System Resource Management with

Operational Planning

10 Apr 2014

Mid-Atlantic Symposium on Aerospace, Unmanned Systems and Rotorcraft

Summary

• Acquiring, operating, and sustaining unmanned systems represents a significant commitment of organizational resources.

• The corporate and governmental resourcing environment is a complex web of interrelated decisions across many time spans and levels of detail.

• The challenge unmanned systems managers face is how to make consistent resourcing decisions across such diverse planning activities.

• Hierarchically-integrated modeling and simulation (M&S) techniques can help organizations link their resourcing decisions to operational performance.

2

Overview

• Why relate unmanned system resource management to operational performance?

• Can unmanned system resourcing really be linked to operational performance?– Strategic resourcing

– Tactical resourcing

– Operational resourcing

• Summary and recommendations

3

The ChallengeRelating unmanned system resourcing to operational performance

• Governmental agencies are committed to finding effective and efficient resource mixes.

• This fiscal environment warrants using integrated analytics for determining budgets and weighing budget alternatives.

• Business analytics are a good fit for governmental analytics needs.

• Unmanned systems managers face resource decisions that business analytics can help answer.

• Integrated business analytics can help unmanned systems managers make better resourcing decisions.

4

Decision Timeframe vs. Detail

5

Year

s

5

1-3

<1

Macro MicroLevel of Detail

Strategic

Tactical

Operational

Higher level decisions constrain lower level decisions

Hierarchically-Integrated Modeling & Simulation

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Strategic Math Program

TacticalSpares Model

Operational Simulation

Match analytical tool to timeframe & level of detail

Case Study: Using Unmanned Aircraft Systems to Monitor

Wildfires

A Notional UAS Scenario

• Givens– The MQ-1 Predator UAS is the system being considered– It will be operated from a select number of bases located

to maximize the surveillance coverage of high risk wildland-urban interface areas

– These bases are supported from higher echelon logistics facilities (e.g., depots and manufacturers)

• Illustrative operational and logistical questions– Where should the UAS operating bases be located and

how many UASs should be assigned to each base?– How many spare parts are needed?– Will this logistics network support the desired

operational tempo?8

Strategic: How many UASs are needed and where should they be

located?

* Ericson Davis, Jeremy Eckhause, & Mike Pouy

Notional Strategic Scenario – Optimize UAS Bases

• Model objective:– Maximize surveillance coverage of high-risk,

wildland-urban interface areas

• Model scope:– Alternative MQ-1 operating locations and high risk areas

• Model formulation:– Coverage optimization via mathematical programming

(such as mixed-integer linear programming) techniques

• Initial constraints:– Total cost of airport and UAS operations does not exceed

fixed budget– Candidate airport locations are given

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Integer Program Model Inputs

• Objective: Position UAS units to detect wildfire activity inside target area

• Regions in target area can have different objective value (e.g. woodlands and higher populated areas should be visited more frequently)

• Each airport can have 0 - 8 aircraft assigned• Candidate airports available for MQ-1 operations are

provided• An aircraft may only visit regions within its range• “Swath width,” the size of each aircraft’s field of

vision, is a function of base airport elevation and the aircraft’s operational ceiling

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Integer Program Model Inputs

CombineForest Coverage

With Population Density

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Reward Intensities

High Reward Areas

Low Reward Area

We can assign different weights to population density and forest coverage for the reward intensity calculation

High

Low

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Variable Description

• Output (decision) variable– Candidate airport configuration – zxy – either 0,1

(e.g., z32 = 1 means 2 aircraft at airport 3)• Input variables

– Available budget – B– Region priorities (heat map value at region (i,j)) - Rij– Aircraft range – S– Cost per airport configuration – Cxy

• Airport “startup” cost + number of aircraft operation cost

• Other– Region coverage – Gij

• Indicates the total level of coverage for region (i,j)– Airport coverage – Aijxy

• Indicates the level of coverage for region (i,j) provided by airport x with y UASs

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Model Formulation

]1,0[},1,0{

1Z

,

xy

subject to

ijxy

x yxyxy

y

x yxyijxyij

i jijij

GZ

BZC

x

jiZAG

GRMax Maximize reward from coverage

Airport equipping is binary Grid region coverage ranges 0 to 100%

Operate within budget

Only one level of equipping per airport

Sum coverage for each region across all airports

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Possible UAS Positioning

16

Higher Coverage

Lower Coverage

No Coverage

Moderate Coverage

2

4

2

LegendActivated Airports

Candidate Airports

Possible UAS Positioning

17

Higher Coverage

Lower Coverage

No Coverage

Moderate Coverage

2

4

2

LegendActivated Airports

Candidate Airports

Strategic Decision Highlights

• Mathematical programming is readily adaptable to many coverage-type problems

• “Reward” can incorporate multiple criteria• Graphical output relates intensity of coverage to

criticality of need• For modeling UAS deployments, the more data that

are available on the proposed operations, the more realistic the solution is

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Tactical: How many spare parts are needed?

* Rob Kline & Dave Peterson

Notional Tactical Scenario – Spares Optimization

Page 20

• Model objective:– Use inventory techniques to evaluate spares investment

requirements for the logistics network • Model scope:

– The UAS is modeled as a composite of its major subsystems (sensors, electronics, structural, mechanical, etc.)

• Model formulation:– Readiness-based sparing techniques optimize spare parts

stockage– Realistic portrayal of logistics network and UAS complexity

• Constraints:– Defined logistics network, UAS configuration, deployment

and operational flying program(s)– Maintenance capabilities, transportation and procurement

lead times, budget, etc.20

Budget Estimates

Spares List

Supply Chain

Item Data

Operations TempoDollars $

100

50

Availability

Availability CurveINPUT OUTPUTPROCESS

Demand 

Backorders

Spares

Availability Cost CurveBase 1

Depot Base 2

Base 3

ASM is a registered trademark of the Logistics Management Institute

Overview of a Readiness-Based Sparing Model

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The Baseline Availability-to-Cost CurveThree operating/support bases (2, 4, 2 aircraft)

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Tactical Model Highlights

• Readiness-based sparing balances inventory investment with taskings

• Optimally allocates available inventory investment across locations

• Highly adaptable to operational and logistical realities:– Operational tempo, airframes, system components, etc.

– Maintenance and supply capabilities/policies and times

• Enables a wide variety of what-if excursions, around capabilities and/or budget constraints

• Links strategic with operational modeling

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Operational: Will this logistics network support

the desired operations?

* Vitali Volovoi & Dave Peterson

Notional Operational Scenario – Simulate UAS Ops

• Model objective:– Evaluate the network’s robustness to day-to-day

operational variability• Model scope:

– Model key resources, such as: aircraft, facilities, manpower and equipment within the context of the preceding strategic and tactical decisions

• Model formulation:– Simulation allows modeling complexity, interdependencies

and variability inherent to UAS operations and support• Model details:

– Multiple bases launching multiple simultaneous sorties– Actual daily aircraft operations can be highly uncertain– A variety of other operational and logistical factors may be

represented25

Linking Tactical to Operational Decisions

Simulation collects support data from sparing at key points

Sparing Viewpoint

Sat Link (1/1)

GCS(1/2)

UAV(1/4)

D F HE I JG

Preflight

Post FlightRepair

Mission Area

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Mission Coverage = 91.1%

Are the Planned Flying Hours Achievable?Central Orbit Area

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Operational Model Highlights

• Places (objects) are states and tokens (aircraft and GCS) are tokens moving through them• In typical reliability block diagram simulations, objects are

the aircraft so doubling aircraft doubles objects• In APN*, scaling up the number of aircraft does not

increase the number of states (model’s visual complexity)

• APN allows for graphically modeling interactions between objects more easily

• APN provides an animation of the problem. Since objects are limited, the visualization is more clearly focused.• Helps management to understand the problem• Testing (a difficult part of simulations) improves

28* Abridged Petri Nets by Dr. Vitali Volovoi, 2014

Final Thoughts …

• The unmanned systems community is very skilled at designing and fielding a variety of high technology systems for many different regimes …

• However, historically, the operations and support (O&S) costs to sustain high technology systems over their useful life can be as high as “60 percent–80 percent of the life cycle costs of a weapon system.”*

• Are life cycle O&S costs the next big challenge facing the unmanned systems community?

• Hierarchically-integrated M&S, using a variety of analytical techniques, will be essential for unmanned system managers as they make resourcing decisions.

29* Taylor, Mike and Joseph Murphy, “OK, We Bought This Thing, but Can We Afford to Operate and Sustain It?,” Defense AT&L, Vol XLI, No. 2, March-April 2012, p. 18.

For More Information

Dave PetersonLMI(703) 917-7260dapeterson@lmi.org

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See also: “Exploring How Hierarchical Modeling and Simulation Can Improve Organizational Resourcing Decisions,” Proceedings of the 2013 Winter Simulation Conference, E. R. Davis, J. M. Eckhause, D. K. Peterson, M. R. Pouy, S. M. Sigalas-Markham, and V. Volovoi. December 2013.

Backup Slides

Is a Desired Availability Target Sufficient?

Perhaps faster turn rates can alleviate the shortfall?

32

Stockage based on an 80% aircraft availability

target

Can Multiple Sorties per Day Remedy the Shortfall?

33

Higher max average sortie rates and cannibalization can help moderate the

sortie shortfall.

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