Lessons learned

Abstract

The Jet Propulsion Laboratory's (JPL) Resource Allocation Process incorporated the decision making software system RALPH into the planning process four years ago. The current principal task of the Resource Allocation Process includes the planning and apportionment of JPL's Ground Data System composed of the Deep Space Network and Mission Control and Computing Center facilities. The addition of the data-driven, rule-based planning system, RALPH, has expanded the planning horizon from eight weeks to ten years and has resulted in significant labor savings. Use of the system has also resulted in important improvements in science return through enhanced resource utilization. In addition, RALPH has been instrumental in supporting rapM turn around for an increased volume of special "what if" studies. This paper briefly reviews the status of RALPH and focuses on important lessons learned from the creation of an highly functional design team, through an evolutionary design and implementation period in which we selected, prototyped and ultimately abandoned an 'AI' shell, and through the fundamental changes to the very process that spawned the tool kit. Principal topics include proper integration of software tools within the planning environment, transition from prototype to delivered software, changes in the planning methodology as a result of evolving software capabilities and creation of the ability to and generic requirements to allow planning flexibility. are of techniques of an of in the of to permit changes. Finally, we a discussion of a design which provides the ability to easily alter and structures to provide a problem-independent system applicable to a wide range of scheduling