AFRL Year 4 Research Proposal Discussion

1. CELDi Center for Engineering Logistics & Distribution AFRL Year 4Research Proposal Discussion University of Arkansas Department of Industrial Engineering

2. Sense and Respond Logistics • The Fundamental Principles of Sense and Respond Logistics • Speed and coordination • Adaptability and flexibility • Networked Organization • Robustness • Dynamic Sychronization • Survivability • Shared Awareness • Precision • Shockproof[1] • [1] Shockproof refers to the system being coherent between levels of scale. Haeckel refers to this as ‘the governance system’ and Ed Smith refers to it at ‘cerebral networks.’ Sense & Respond Military Logistics Network Optimization

3. Sense and Respond Research and Technology Requirements • Types of technology or bodies of thought that we see as potentially useful (pending further research on the requirements and capabilities out there) in this environment for the S&RL • Real-time, Adaptive, Sense and Respond Organizations • Complex Adaptive Systems • Cybernetic Control Systems • COP/CROP/FIOP • Knowledge/Data Wall • Real-time Metrics • RFID Technologies • Supply Chain Event Management (SCEM) • Business Activity Monitoring (BAM) • Business Intelligence • Synchronization/Self-Synchronization • Support network Optimization • Real Time Location Systems (RTLS) • Business Process Integration (BPI) • Enterprise Application Integration (EAI) • Data Warehousing • Data Mining • Agent-Based Modelling and Simulation • Adaptive and Agile Supply Chains • Sense and Respond Logistics Con-Ops, Spet 2003 Sense & Respond Military Logistics Network Optimization

4. Focused Logistics Sense & Respond Military Logistics Network Optimization

5. Our Approach • Integrated Team Effort • Three Focus Areas • Network Modeling and Analysis • Dr. Mason • Dr. Cassady, Dr. Chimka • Agent Based Modelling and Simulation • Dr. Rossetti • Dr. Cassady, Dr. Buyurgan • Cognitive Decision Support • Dr. Nachtmann • Dr. Nam, Dr. Chimka, Dr. Buyurgan Sense & Respond Military Logistics Network Optimization

6. AFRL Year 4 Proposed Research:Sense & Respond Military Logistics Network Optimization Justin R. Chimka, C. Richard Cassady, Scott J. Mason (POC), Edward A. Pohl October 15, 2004

7. Traditional Military Logistics Network Analysis • Based on paradigm of nodes and arcs • Nodes represent manufacturers, warehouses, depots, and bases at fixed locations • Relationship of each node to each other node in network collectively defines supply chain (SC) echelon structure • Each baseacts independently of other bases, typically using a single depot • Each depot and warehouse acts independently of their counterparts • Arcs connecting the nodes represent transportation links between locations • Network flow occurs when item is transported to an adjacent (often downstream) echelon • Items (entities) are either non-repairable (i.e., consumable) or repairable • Driving force behind entity flow is base-level demand • Often assumed to be static (i.e., not time-dependent) • Deterministic or probabilistic with a known underlying stationary distribution • Item lead times are static • Performance metrics of interest • Demand fill rate • Inventory levels (position) Sense & Respond Military Logistics Network Optimization

8. Sense & Respond Military Logistics Networks • Dynamic and adaptable in structure • In addition to traditional, fixed network nodes • Movingnodes used to represent infantry units, maritime vessels, and so on • Temporary nodes relating to contingencies that are only active/probable for some finite amount of time • Potential nodes associated with opportunities/planned missions • Nodes can belost with some probability due to evolving opportunities/missions • Sensing technologies provide access to real-time node locations and statuses • Total asset visibility creates potential for • Lateral supply between nodes • Increased network connectivity • Reduced item lead times • Network arcs can be permanent or temporal, added or removed, created or lost • Dynamic demand (but less variable) at current bases • Potential bases/demand locations are sources of uncertainty • Need for additional new, more applicable performance metrics • Responsiveness • Vulnerability • Coverage/reach/position • Multi-objective analysis Sense & Respond Military Logistics Network Optimization

9. Proposed Research • Create a framework that can be used to construct mathematical and logical models of sense and respond (S&R) military logistics networks. • Three “scenarios” of research focus corresponding to Dept of Homeland Security threat levels • Yellow Scenario • Majority of network nodes and arcs are known and fixed • Demand requirements are primarily deterministic • Develop optimization models to assist in definition of policies that optimize network “day-to-day” operational performance and maximize network responsiveness. • Orange Scenario • An apparent threat growing/developing at one or more network locations • Nodes have a probability of becoming active within the network • Stochastic nodes can affect transportation network connectivity and item (inventory) placement/staging decisions • Create optimization models to assist in definition of policies that maximize network responsiveness and maximize network robustness in the face of contingencies. • Red Scenario • Network structure and connectivity may be unstable • Acting/reacting to an actual threat or attack on one or more network nodes/locations • Develop optimization models to help determine appropriate means of restoring network connectivity, minimizing future network vulnerability, and maximizing network responsiveness. • For all three scenarios, we will define appropriate performance metrics for S&R military logistics networks that properly characterize the stated objective functions of interest. Sense & Respond Military Logistics Network Optimization

10. Potential Solution Methodologies • Deterministic and stochasticoptimization models will be formulated • Model tractability directly affects solution methodologies selection • Deterministic models will contain • Discrete variables • Linear and non-linear constraints and/or objective functions • Can increase tractability through linearization • Results in mixed-integer linear programs that can be analyzed by branch-and-bound techniques • Less tractable, non-linear formulations analyzed using combination of decomposition-based approaches, greedy heuristics, and metaheuristic approaches such as genetic algorithms and Tabu search • New heuristics may be developed • Stochastic model analysis will utilize (to the extent possible) • Stationary stochastic processes • Assume independence between system elements • We will utilize the simulation test-bed created in other aspects of this effort to evaluate the quality of our solution approaches under realistic conditions • Use simulation-based optimization heuristics as an alternative to mathematical modeling • Use simulation test-bed as an experimental tool to create approximate mathematical models of some aspects of network performance Sense & Respond Military Logistics Network Optimization

11. Simulation FrameworkforSense and Respond Military Logistics Sense & Respond Military Logistics Network Optimization

12. Manufacturing +Logistics +Transportation = Supply Chain Network For commercial and military organizations, the logistics network consists of a set of interconnected facilities (factories, warehouses, distribution centers, retail outlets, etc.) that exchange material and information in order to provide material, products, or services to end-users across geographically dispersed areas. Sense & Respond Military Logistics Network Optimization

13. Goal/Objectives • To develop an object-oriented, agent-based, discrete-event military logistics and supply chain simulation model architecture • To evaluate sense and respond based, adaptable, demand and supply networks • To evaluate new metrics for optimizing and controlling these networks • To understand the new decisions required in such networks, and • To model and test the human cognitive interactions within these networks. Sense & Respond Military Logistics Network Optimization

14. Supply Chain Simulation Framework • We have developed a prototype object-oriented software architecture and framework using the Unified Modeling Language (UML) and developed within the Java programming language to facilitate the development of supply chain simulations. • Current implementation: • Built on the Java Simulation Library (JSL) • Models the Relationship network between a set of elements (facilities and end-customers) which have functions associated with the creation or consumption of the products/services • Includes basic inventory policies, flexible demand generation, multiple products, customers can be served from multiple suppliers at different tiers in the supply chain • Verified and validated on small simulation example Sense & Respond Military Logistics Network Optimization

15. Java Simulation Framework • The Java Simulation Framework: • Rossetti, M. D., Aylor, B., Jacoby, R., Prorock, A., White, A. (2000) “Simfone: An object-oriented simulation framework”, The Proceedings of the 2000 Winter Simulation Conference, ed. J. Joines, R. Barton, P. Fishwick, and K. Kang, ACM/SIGSIM, ASA, IEEE/CS, IEEE/SMCS, IIE, INFORMS/CS, NIST and SCS, pp. 1855-1864 • Contains Java classes that are used in the development of simulations including modeling and experimentation • Model Element, Model, RandomVariable, Statistic, Control Variable, ResponseVariable, Event, Queue, Resource, Process, Scheduler, etc. Sense & Respond Military Logistics Network Optimization

16. SCSF Relationship Network • Composite pattern represents aggregation of elements into regions, e.g. demand region • Facilities perform major processing within supply chain Sense & Respond Military Logistics Network Optimization

17. Node Hierarchy • Contains a set of nodes and relationships • A relationship represents a conceptual connection between two nodes • Object aggregations are used in this model to achieve the containment of nodes and relationships • Each relationship has a supplier to supply product to a customer Sense & Respond Military Logistics Network Optimization

18. Inventory Modeling • Facility • Has a Warehouses • Has many Inventories Inventory Associates with InventoryPolicy Sense & Respond Military Logistics Network Optimization

19. Persistent Storage Mechanism for Supply Chain Configurations • Database • Makes a Persistent Network • Makes a Non-Persistent Network • Creates a Supply Chain Network Based on Non-Persistent Network • Uses industry standards • Java Data Objects • Works with any SQL compliant database Sense & Respond Military Logistics Network Optimization

20. Hooked into JSL • Uses a class named SupplyChainElement as the connection between JSL and Supply Chain Framework • All Non-Persistent classes in Supply Chain Framework inherit from SupplyChainElement Application application = new Application (“CELDi", "Single Echelon"); Model model = application.createModel(); model.turnOnDefaultReplicationReport(); model.turnOnDefaultSummaryReport(); model.setLengthOfWarmUp(0); model.setLengthOfReplication(3600); model.setNumberOfReplications(30); SCNModel scnModel = new SCNModel(); model.addModelElement(scnModel); model.startSimulation(); Sense & Respond Military Logistics Network Optimization

21. Object Oriented Spare Parts Supply Chain Simulation Framework • Object-Oriented analysis and identification of the fundamental elements of a spare parts supply chain network. • Provide a standardized, well documented, agent-based, generic simulation framework for a multi-indenture, multi- echelon (MIME) spare parts supply chain network. • Enhanced and built upon the SCSF Sense & Respond Military Logistics Network Optimization

22. System Structure Indenture 1 Pump System Operation Indenture 2 Valve Piston Indenture 3 Ring Rod Spare parts To higher echelon Facility MI Hierarchy Depot Echelon 1 Ware House Repair Base Base Echelon 2 ME Hierarchy Represents the Weapon System Complex multi-indentured, multi-echelon hierarchies Sense & Respond Military Logistics Network Optimization

23. Detailed State Modeling Sense & Respond Military Logistics Network Optimization

24. Detailed Base/Depot Repair Modeling • Order Receiving Agent • receives order for a facility • Order Sending Agent • creates and sends order for a facility • Shipment Receiving Agent • receives shipment for a facility • Shipment Sending Agent • creates and sends shipment for a facility • End Item Scheduling Agent • Schedules operational cycle for end items Sense & Respond Military Logistics Network Optimization

25. Leverage Year 3 TIME Project • Develop object-oriented simulation framework and primitives that support the modeling of Automatic Data Collection Systems (ADCS) within a simulation model of the system • Explore requirements for substitutability • The ability to substitute a simulation of the system for the real system for planning and control purposes. • Investigate (in a logistics context) the simulation of example ADCS within the framework. • How to properly integrate the automatic data collection system into the operations of an existing DEDS? • How can we collect the data that is actually needed for planning and control? What should be the number of data acquisition points and where should they be located to support both planning and control? • How can the tradeoff between the design, cost, and operation of the automatic data collection system be analyzed with respect to the information actually gained and the impact on the DEDS? • How can we easily and reliably extract information from the collected data? Sense & Respond Military Logistics Network Optimization

26. Topics of Investigation • Integrating current efforts (SCSF, Spare parts SCSF, TIME, JSL) • Software analysis, design, development, testing • Collecting and reporting metrics • Integrating with current agent-based modeling paradigms, e.g. COUGAAR • Develop agents for optimizing within new sense and respond doctrine • Develop agents to represent human cognitive processing • Enhancing usability of programming framework • Use by optimization algorithms for testing • Displaying simulation information to the human user • Database representation of problem instances Sense & Respond Military Logistics Network Optimization

27. End Result • AFRL and CELDi will have a simulation based test-bed for evaluating algorithms, metrics, and decision making within the context of the new sense and respond doctrine. Sense & Respond Military Logistics Network Optimization

28. Cognitive Decision Support Task • Objectives • Work toward information superiority through enhanced cognitive decision support • Integration of course of action analysis, knowledge management and mining, and pattern recognition and learning into the network • Improve the logistician’s ability to convert data and information into relevant knowledge and understanding • Sub-tasks • Decision model development • Artificial intelligence agent • Human computer interaction Sense & Respond Military Logistics Network Optimization

29. Decision Model Development Sub-task • Objective • To understand and model human decision processes within a S&RL network • Capabilities • Understand how preferences and information are used by people to choose actions • Knowledge of how past experience should be used to anticipate the future • Ability to determine when a satisfactory alternative has been found • Benefits • Understanding of human decision processes should improve the programming of prediction models within a S&RL network • Effective modeling of human interaction with network components Sense & Respond Military Logistics Network Optimization

30. Artificial Intelligence Agent Sub-task • Objectives • To sustain a knowledge-driven specialized decision support system • To assist in creating knowledge for network decision models • Capabilities • Intrinsic ability to handle the complexity of the human decision process • Analyze voluminous data from the other system components • Extract knowledge from the data • Intelligently realize and distinguish the existing status • Benefits • Knowledge of symptoms and indicators related to decision making process and the environment • Understanding of the relationships within the process and environment Sense & Respond Military Logistics Network Optimization

31. Human Computer Interaction Sub-task • Objectives • To systematically examine the appropriate form of interactions between the human user and agents • To design intelligent user interfaces • Capabilities • Understand and improve agent-assisted collaboration including • how the human user instructs and controls the agent(s) • the nature of the feedback from the agent to the user • the manner by which the agent provides the user with information • Benefits • Fundamental impact on agent-assisted collaboration efforts • Design principles and guidelines can apply to other areas of human-agent collaboration Sense & Respond Military Logistics Network Optimization