decision support for search assignment production rate

Decision Support for Search AssignmentProduction Rate based on System Dynamics andGIS

Kenneth Gulbrandsøy

Department of Engineering Cybernetics

27. March 2008

www.ntnu.no K. Gulbrandsøy, Search Decision Support

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Outline

— Supporting decisions— The search and rescue domain— The DISCO-SAR project— A dynamic model of search planning and allocation— Impact factor and further work


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Supporting Decisions

— Support vs. automatic control— Social systems and human in the Loop— Articulation of work, work processes— Training vs. Decision Support Systems (DSS)— DSS workload, a function of usability— Work process dependencies may limit DSS applicability


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Modeling Social Systems

— Models have two principal parts• Physical and organizational structure• Decision rules that agents follow to govern the system


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Modeling Decisions

— Decisions are the outcome of the decision process— Decision rules represents agent behavior— Modeling decisions imply rationality assumptions— Decision making in general can not be assumed to optimal— Decisions are based on the perceived system state, not the

actual.


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Decision Rule FormulationFundamentals [Sterman, 2000]

— The inputs to all decision rules must be restricted toinformation actually available to the real decision makers(The Baker Criterion)

— Decision rules should confirm to managerial practice— Desired and actual conditions should be distinguished— Physical constraints to the realization of desired outcomes

must be represented— Decision rules should be robust under extreme conditions— Equilibrium should not be assumed. Equilibrium and stability

may (or may not) emerge from the interaction of the elementsof the system


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Model impact is proportional to useracceptance

— Decision makers (users) do not want ”black boxes”— User acceptance increase with user involvement in the

modeling process— A useful model fulfils the users needs— The modeling process and the finished model is equally

important (second order learning)


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The Search and Rescue Domain

— Hierarchial organization: national, local and field command— From-Problem-to-Solution oriented— Strict time constraints, potentially fatal outcome if not met— Volatile situations, limited resources— Ad-hoc personnel combinations


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The Search problem— A geographical problem

• Where is the missing person?— A tactical problem

• How do we maximize the probability of detection?— A organization problem

• How do we organize the search effort?— A logistical problem

• Which resources are needed and where do we use them?— A technological problem

• How can information technology increase the efficiency of theSearch and Rescue effort?


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The command post

K. Gulbrandsøy, Search Decision Support

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Search Leader (Role)


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Search Planner (Role)


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Logistics Leader (Role)


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Shared situational awareness


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The Search Work Process


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Critical Search Work Processes

— Search management (search leader)— Search assignment planning (search planner)— Search assignment allocation (logistics)


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Main Objectives

— Minimize the risk to personnel— Maximize the probability of detection


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Main Decision Cues

— Positive outcome time constraint (criticality)— Search assignment accuracy and efficiency— Resource utilization— Search assignment demand— Search planning rate— Search assignment allocation rate


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Estimating Search Assignment Demand

— Process tipping point— Manual estimate is based on perceived situational awareness— Perceived situational awareness is based on incomplete

information— Estimate accuracy is closely related to cognitive load— Reduced estimate accuracy usually result in reduced resource

utilization— Automatic estimation is assumed to eliminate this dependency


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Estimated Time Enroute (ETE)

— Is used to calculate search assignment demand— ETE is a function of many factors: distance to travel, slope to

climb or descend, terrain under foot, activity, light and weatherconditions, fitness and fatigue, load to carry.


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ETE Model— The ETE model is based on an extension of Naismith’s Rule

[Naismith, 1892] developed by [Scarf, 2007]

c =∑

R

γ · (δ · (x + α · y))β. (1)

— where γ, δ, α and β are the limiting speed, penalty index,Naismiths coefficient and fatigue factor

— Parameters are estimated using nonlinear regression— Regression data is gathered from GPS tracks— Further analysis is required to validate the model


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A subsystem diagram of the searchmanagement system


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A dynamic model of search planning andallocation

— A hybrid system on mixed logical dynamical (MDL) form

xi+1 = Axi + B1ui + B2δi + +B3zi . (2)

— where xi are states: assignments, demand, available units, etc.— where ui are input (decisions): production, allocation, etc.— where δi are auxiliary boolean variables— where zi are auxiliary real variables— Analysis is pending— Implementation in DSS is pending


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PowerSim implementation


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Production and progress


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Unit states


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Per unit production decision supportGUI


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Total production decision support GUI


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Further work

— Finalize the implementation of model in DSS (DISCO-SAR)— Validate and test using table-top exercises (laboratory work)


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Methodology Impact

— Search and Rescue planning and execution problem— Urban and rural fire-fighting planning and execution problem— Production control and error handling problem— Offshore platform crisis management (first line preparedness)

problem— Managing patient flow from accident to hospital bed


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Thank You!

— Any Questions?


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BibliographyNaismith, W. W.Excursions: Cruach Ardran, Stobinian, and Ben More.Scottish Mountaineering Club Journal, II(3): 136.

Scarf, P.Route choice in mountain navigation, Naismith’s rule, and theequivalence of distance and climb.Journal of Sport Sciences, 25(2): 719–726.

Sterman, J. D.Business Dynamics - Systems Thinking and Modeling for aComplex World.McGraw-Hill Higher Education.


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