Complex Project Scheduling Lessons Learned from NASA, Boeing, General Dynamics and Others (2019)

The work presented in this paper describes lessons learned from expert schedulers working on many of the world's most complex scheduling challenges and incorporating these lessons into an intelligent scheduling software framework that utilizes domain specific knowledge and reasoning. This intelligent scheduling software framework, called Aurora, originated in part from many earlier NASA-funded efforts and has been utilized by NASA for some of its most complex scheduling challenges, including the scheduling of the maintenance, repair & overhaul (MRO) of the Space Shuttle during its tenure. All of NASA has access to Aurora. Aurora has also been applied to complex scheduling challenges faced by Boeing, General Dynamics Electric Boat, the US Air Force, Pfizer and others. Lessons learned from one domain / implementation has greatly benefited future implementations. For example, even though much of our early work was with NASA, Stottler Henke continues to work with NASA and leverage lessons from other implementations. For example, and ongoing implementation is a solution called, Aurora-KSC, has been designed, developed and deployed at KSC to automate a large amount of Kennedy Space Center's planning, scheduling, and execution decision-making. This implementation leverages the robust filtering and highlighting capability, developed and improved via many earlier implementations, in addition to the concept of the Hazard Constraint that has evolved from the non-concurrent constraint developed earlier. More specifically this paper will look at the following valuable capabilities that are rare or non-existent in other project management / scheduling tools that have proven invaluable to solving many of the world's most complex scheduling challenges: Ability to capture human scheduler reasoning. That is, when decisions / tradeoffs need to be made, use the expertise of expert schedulers so that the scheduling system reacts as a human expert wants it to; Ability to model human resources with details beyond just an occupation, such as occupation plus a set of specializations and/or certifications; Ability to handle less than perfect data sources, such as having an override for the status of work-in-progress tasks, so schedulers can easily override data from external sources; Provide a convenient interface for visualizing what tasks can be outsourced and providing a one-click option to outsource a task that adjusts the actual model appropriately; Provide an explanation capability that shows the rationale for why every task is scheduled where it is, that is, each task includes the reasons why it is scheduled at its current time; Provide a robust filtering and highlighting capability, so users can visualize the criteria of interest; Provide robust constraint support beyond the traditional FS, SS, FF, SF constraints found in traditional project management tools. The result of working directly with many of the best schedulers has been the development of these powerful capabilities and a solution that produces a schedule that is significantly better than those reached by previous methods.