Two tasks related to payload activity planning and scheduling were performed. The first task involved making a comparison of space mission activity scheduling problems with production scheduling problems. The second task consisted of a statistical analysis of the output of runs of the Experiment Scheduling Program (ESP). Details of the work which was performed on these two tasks are presented

Bullington, Stanley F.

English

NASA Technical Reports Server

N92-15856
1991
NASA/ASEE SUMMER FACULTY FELLOWSHIP PROGRAM
MARSHALL SPACE FLIGHT CENTER
THE UNIVERSITY OF ALABAMA IN HUNTSVILLE
EVALUATION OF SCHEDULING TECHNIQUES FOR
PAYLOAD ACTIVITY PLANNING
Prepared By:
Academic Rank:
Institution:
NASA/MSFC:
office:
Division:
Branch:
MSFC Colleague:
Contract No.:
Stanley F. Bullington, Ph.D., PE
Assistant Professor
Mississippi State University,
Department of Industrial
Engineering
Mission Operations Laboratory
Mission Analysis
Planning Systems
John P. Jaap
NGT-01-008-021
The University of Alabama
in Huntsville
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Two tasks related to payload activity planning and scheduling
were carried out. The first task involved making a comparison of
space mission activity scheduling problems with production
scheduling problems. The second task consisted of a statistical
analysis of the output of runs of the Experiment Scheduling Program
(ESP). Details of the work which was performed on these two tasks
are presented in the separate sections which follow.
TASK 1
Description. A paper entitled "A Comparison of Space Mission
Activity Scheduling Problems with Production Scheduling Problems"
was written (reference I). This paper will be submitted for
possible publication as a NASA Reference Publication.
Objectives. It was felt that a need existed for a "bridge"
between the literature in space mission activity scheduling and the
literature in production scheduling. Writing a document which
compared the two problems was seen as the best means of providing
this bridge. This document could then serve to introduce the two
problems to those not familiar with one, or both, of them. For
example, those who are familiar with space mission activity
scheduling problems (generally NASA personnel and NASA contractor
personnel) might use the document to gain a better understanding of
the literature on production scheduling problems, and how it
relates to space mission activity scheduling. Similarly, those who
are familiar with production scheduling problems, including
academic and industrial researchers, as well as newly-graduated
engineers, could use such a document as an introduction to space
mission activity scheduling problems.
Another objective of this task was to identify possible areas
for further research in space scheduling problems. This was done
by examining the literature for both types of scheduling problems
to see which approaches had been successful, and by considering the
effects of the important differences in the two types of problems.
Approach. The document produced in Task 1 begins with a brief
discussion of scheduling problems in general. This is followed by
a comparative description of space mission activity scheduling
problems and production scheduling problems. For each of the
problem types, the associated terminology is reviewed. This is
followed, for each case, by a detailed problem description which
covers the problem environment, typical objectives, and typical
constraints. A discussion of the solution approaches which have
been applied to each problem type is included. Finally, some areas
for further research in space scheduling problems are identified.
Results. The comparison of the two scheduling problems
pointed out the similarities between them, especially in terms of
their level of difficulty and the solution approaches which have
been applied to them. However, several important differences in
the problems were identified. Some of these differences are
discussed below.
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In production scheduling problems, machine capacity is often
assumed to be the only constraining resource. In a very few
studies, other resources such as labor or tooling are considered.
In space mission scheduling, on the other hand, many different
types of resources act as important constraints on the system, and
must be considered when solving the scheduling problem. This
consideration of multiple resource types makes the space mission
activity scheduling problem an extremely difficult one.
The objectives of the two scheduling problems are quite
different. In production problems, the objectives of interest
generally include those related to job completion times (e.g.,
flowtime and makespan), those related to due dates (e.g., mean or
maximum tardiness or lateness, number of tardy jobs, etc.), and/or
those related to inventory and utilization costs (e.g., average
number of jobs waiting, machine utilization percentages, etc.). In
space mission activity scheduling, the single major objective is
the maximization of the scientific return from the mission.
Because of the difficulty of measuring this objective directly,
surrogate measures such as the number of activity model
performances scheduled_ the amount of crew time scheduled, the
amount of experiment time scheduled, or a subjectively weighted
schedule grade, are generailylu_ed.
There are a number of important differences in the constraints
on the two types of scheduling problems. In general, space mission
activity scheduling problems tend to have many more constraints
than production scheduling problems. One major difference in the
two problem types is that space missions have a finite duration.
Because of this, only a subset of the activities which could be
scheduled actually will be scheduled. In production problems, on
the other hand, all jobs are generally scheduled eventually. Those
which cannot be completed in one week will be completed in the
following week, for example.
In space scheduling problems, another important type of
constraint is the existence of time windows in which the activities
must be scheduled. These constraints arise because of the position
or attitude of the spacecraft or target objects, the nature of the
scientific experiment or activity being performed, the duty cycles
of the crew members, etc. In most production scheduling problems,
the analogous types of constraints are either unimportant or are
ignored for convenience.
Some of the other complicating constraint types in the space
mission activity scheduling problem include the existence of
complex resource availability profiles for the multiple resource
types, the possibility of concurrent activities, the existence of
variable step durations, the requiremen t that there be a certain
minimum time delay between adjacent steps in an activity model or
between different performances of the same model, the requirement
that certain resources remain unavailable for use during such time
delays, the requirement that certain steps in an activity be
performed by the same crew member(s), and the possible existence of V
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valternate scenarios for an activity. Some of these constraints
have analogies in the production scheduling environment, but these
are usually not considered in such problems.
The document produced in Task 1 provides a review of the
solution approaches which have been applied to each of the types of
scheduling problems. Much of the general scheduling literature
deals with theoretical issues and very small scheduling problems
(e.g., single-machine scheduling). Optimization approaches have
been applied in both problem environments, but have been of limited
practical use because of their computational burden. Because of
this, heuristic approaches have generally been used to solve
realistic problems of both types. Various heuristic approaches are
reviewed in the paper, with special emphasis on ESP (see reference
2) and other robust payload activity scheduling programs. Finally,
artificial intelligence approaches to scheduling are discussed.
The last result of the Task 1 document is the identification
of areas where further research is needed. In the short term, the
most promising area appears to be the investigation of methods for
improving heuristic approaches such as ESP. Three specific areas
of possible improvement were identified. First, the decomposition
of the scheduling problem by defining "artificial" time windows for
activities should be examined. In ESP, such a decomposition can be
easily accomplished by using the macro windows feature of the
program. Currently, macro windows are used merely to define the
mission duration. However, they could be used to help control the
placement of activities onto the mission timeline, and to
artificially break a large scheduling problem into several smaller
problems in order to reduce the time required to obtain a solution.
Research is needed to determine effective methods for defining the
macro windows, and to determine the effects of their use on
solution time and the quality of the schedule obtained.
In ESP, activities are placed onto the mission timeline one at
a time. The order in which activities are selected for placement
has a significant impact on the final schedule. Several selection
methods are currently available, but more robust methods are
needed. Finally, the use of rescheduling in heuristics such as ESP
should be examined. In some heuristics, including ESP, an activity
which has been placed on the timeline will not be rescheduled.
Other heuristics do reschedule activities. Rescheduling promises
improved schedules, but this improvement is at the expense of
increased computational requirements. An analysis of the relative
costs and benefits of rescheduling, as well as the definition of
appropriate rescheduling methods, needs to be performed.
The relatively new field of artificial intelligence holds
promise for scheduling applications, particularly in the long term.
Further research in this area is needed.
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TASK 2
Description. A statistical analysis of the output from ESP
runs for two space station data cases was performed. Using the
random activity selection rule, 60 runs were made for a relatively
difficult data case, and 95 runs were made for a relatively easy
data case.
Objectives. It was hoped that Task 2 would provide knowledge
on the number of ESP runs required to reach a specified level of
confidence in the results. A closely related objective was to
suggest how analysts could, after obtaining the results from
several runs, make their own determination as to the need for
making additional runs.
Approach. For the difficult data case, the 60 runs were
randomly divided into groups of size i, 2, 3, 5, and i0. The best
run of each group for each of four performance measures (schedule
grade, number of performances scheduled, crew time scheduled, and
experiment time scheduled) was then identified. Within each group
size/performance measure combination, statistical analyses were
performed on the best runs of each group to find the mean value,
minimum and maximum value, upper and lower tolerance limits (see
reference 3), and a crude estimate (mean value plus three standard
deviations) of the optimal value. The same approach was used for
the easier data case, with the 95 runs being randomly divided into
groups of size i, 3, 5, and i0.
Results. As expected, the data for the two cases indicates
that the number of runs needed to reach a given confidence level is
highly dependent upon the amount of variation within the data.
After making several runs (e.g., five), it is recommended that the
mission planner carry out an analysis like the one performed here.
The closeness of the best solution obtained to the upper tolerance
limit and estimated optimal value should then indicate whether
additional runs should be made.
REFERENCES
i. Bullington, S.F., and Jaap, J.P., "A Comparison of Space Mission
Activity Scheduling Problems with Production Scheduling
Problems," submitted for publication to NASA, 1991.
2. Jaap, J.P., and Davis, E.K., "Experiment Scheduling Program
User's Manual," NASA Marshall Space Flight Center, 1989.
3. Miller, I.R., Freund, J.E., and Johnson, R., Probability and
Statistics for Enqineers, 4th Ed., Prentice-Hall, Englewood
Cliffs, New Jersey, 1990.
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Evaluation of scheduling techniques for payload activity planning

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