Advanced timeline systems

Abstract:  The Mission Planning Division of the Mission Operations Laboratory at NASA's Marshall Space Flight Center is responsible for scheduling experiment activities for space missions controlled at MSFC. In order to draw statistically relevant conclusions, all experiments must be scheduled at least once and may have repeated performances during the mission. An experiment consists of a series of steps which, when performed, provide results pertinent to the experiment's functional objective. Since these experiments require a set of resources such as crew and power, the task of creating a timeline of experiment activities for the mission is one of resource constrained scheduling. For each experiment, a computer model with detailed information of the steps involved in running the experiment, including crew requirements, processing times, and resource requirements is created. These models are then loaded into the Experiment Scheduling Program (ESP) which attempts to create a schedule which satisfies all resource constraints. ESP uses a depth-first search technique to place each experiment into a time interval, and a scoring function to evaluate the schedule. The mission planners generate several schedules and choose one with a high value of the scoring function to send through the approval process. The process of approving a mission timeline can take several months. Each timeline must meet the requirements of the scientists, the crew, and various engineering departments as well as enforce all resource restrictions. No single objective is considered in creating a timeline. The experiment scheduling problem is: given a set of experiments, place each experiment along the mission timeline so that all resource requirements and temporal constraints are met and the timeline is acceptable to all who must approve it. Much work has been done on multicriteria decision making (MCDM). When there are two criteria, schedules which perform well with respect to one criterion will often perform poorly with respect to the other. One schedule dominates another if it performs strictly better on one criterion, and no worse on the other. Clearly, dominated schedules are undesireable. A nondominated schedule can be generated by some sort of optimization problem. Generally there are two approaches: the first is a hierarchical approach while the second requires optimizing a weighting or scoring function.