Simulating Technology Improvements for Maintenance Excellence (TIME)

1. Simulating Technology Improvements for Maintenance Excellence (TIME) • Investigator: Dr. Manuel D. Rossetti • Goals & Objectives • To research mechanisms for (1) the evaluation of automatic data collection system’s benefits and costs within a logistics environment, and (2) to provide decision support technologies for the integration of the automatic data collection system with the simulation planning mechanisms. • Planned Approach • Expand upon current work to provide a prototype object-oriented simulation of the flight and maintenance operations at a typical air base • Investigate using object-oriented analysis and design techniques how to model automatic data collection systems within the system • Anticipated Outcomes • Flexible modeling tools for simulating the effect of automated sensor technology on flight, maintenance, and supply operations • New simulation techniques for control and system evaluation

2. Automatic Data Collection Systems • Automatic data collection systems are information systems which utilize computerized data collectors to capture and record data associated with events at or near the spatial occurrence of the event and at or near the actual time of the event. • Automatic data collection systems form the infrastructure on which modern management information systems are built. • Higgins(1989) and Bauer (1994) et. al. discuss in detail the activities of the monitoring function in the context of production activity control systems for shop floor control. A system monitor performs data capture, data analysis, and decision support. An automatic data collection system supports the data capture activities of the monitor.

3. Discrete Event Dynamic Systems • Discrete event dynamic systems consist of sets of interacting objects which compete for services from a variety of resources dynamically at discrete points in time. • Automatic data collection systems support system monitoring • by capturing data on the interactions of the objects as they compete for resources within the DEDS. • By reporting on the current operating state of the object at periodic time intervals

4. Basic Questions • Why do some automatic data collection systems fail to achieve their full potential? Or how can they be better designed to gain the planned benefits? • Improper analysis and consideration of the existing system especially with respect to: • the impact of system events on data collection throughput and response time • the impact of work procedures and processes on data collection • the impact of hardware/software interfacing on the existing computing infrastructure • Inadequate attention to design and integration stages of the project especially with respect to: • lack of non-vendor dependent design and development tools • difficulty testing and prototyping designs without having to resort to physical prototypes

5. Basis Research Questions • 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?

6. Simulation Substitutability • What is simulation substitutability in the context of DEDS and ADC? • The ability to substitute a simulation of the system for the real system for planning and control purposes.

7. Example The state of the manufacturing system is dependent upon the actual data collection devices which interact with the control elements of the system. In order to substitute a simulation model for the actual system the same protocols of the data collection devices must be adhered to within the simulation environment. Current simulation modeling tools, do not explicitly provide primitives for representing the collection of data from the system as it actually occurs.

8. Basic Approach • Develop object-oriented simulation framework and primitives that support the modeling of ADCs within a simulation model of the system • Explore requirements for substitutability • Investigate (in a logistics context) the simulation of example ADCs within the framework.

9. Phase 1 (Fall 2004) • Enhance the JSL • 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, etc. • Provide process oriented modeling capabilities for thread-based interaction. This is especially relevant when modeling the “processing” life of an ADC • Documentation • Project Team: Dr. Rossetti, Stephen Farris, Brad Hobbs, Soncy Thomas

10. Phase 2 (Spring 2005) • During phase 2, the Object Oriented Spare Parts Supply Chain Simulation Framework will be enhanced to model • Maintenance flight line operations, e.g. similar to Commercial Logistics Project, Sortie Generation Project, and Paul Faas thesis • ADC example will be conceptualized and requirements for testing example established • Framework analysis using object-oriented design and analysis techniques (UML, etc) • New modeling constructs developed and incorporated into the framework • Documentation • Project Team: Dr. Rossetti, Stephen Farris, Josh McGee, Brad Hobbs

11. Object Oriented Spare Parts Supply Chain Simulation Framework System Structure Indenture 1 Pump System Operation Indenture 2 Valve Piston Indenture 3 Stem Ring Rod Spare parts To higher echelon Facility MI Hierarchy Depot Echelon 1 Ware House Repair Base Base Echelon 2 ME Hierarchy

12. Detailed Product State Transitions

13. 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

14. Phase 3 (Summer/Fall 2005) • Developing testing plan • Development and implementation of ADC object concepts within Java • Conceptualizing, designing, developing, and documenting Java implementation • Testing of simulation code • Report writing and documentation

15. How to help? • Information about ADC within SMART • Conceptualizing and developing an example application of ADC with appropriate military and maintenance context • Other literature or efforts in this area that you are aware of • Review and model walk throughs.