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Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
R. Albrecht, R. N. Hook and H. A. Bushouse, e
Ö Copyright 1998 Astronomical Society of the Pacific. All rights reserved.
ds.
Achieving Stable Observing Schedules in an Unstable
World
M. Giuliano
Space Telescope Science Institute, 3700 San Martin Drive Baltimore,
MD 21218 USA
Abstract. Operations of the Hubble Space Telescope (HST) require the
creation of stable and e#cient observation schedules in an environment
where inputs to the plan can change daily. A process and architecture
is presented which supports the creation of e#cient and stable plans by
dividing scheduling into long term and short term components.
1. Introduction
Operations of the Hubble Space Telescope (HST) require the creation and pub­
lication of stable and e#cient observation schedules in an environment where
inputs to the plan can change daily. E#cient schedules are required to ensure a
high scientific return from a costly instrument. Stable schedules are required so
that PIs can plan for coordinated observations and data analysis. Several fac­
tors complicate creating e#cient and stable plans. HST proposals are solicited
and executed in multi­year cycles. Nominally, all the accepted proposals in a
cycle are submitted in a working form at the beginning of the cycle. However, in
practice, most proposals are reworked based on other observations, or updated
knowledge of HST capabilities. Another source of instability is due to changes in
HST operations. As HST is used the capabilities of some components degrade
(e.g., the solar panel rotation engines), and some components perform better
than expected (e.g., decreases in acquisition times). Changes in component
performance lead to di#erent operation scenarios and di#erent constraints on
observations. A plan created with one set of constraints may no longer be valid
as operations scenarios are adjusted based on an updated knowledge of HST
capabilities. A final source of plan instability is that the precise HST ephemeris
is not known more than a few months in advance. As a result highly constrained
observations cannot be scheduled with accuracy until the precise ephemeris is
known. Given these factors, it is not possible to create a single static schedule
of observations. Instead, scheduling is considered as an ongoing process which
creates and refines schedules as required.
A process and software architecture is presented which achieves stable and
e#cient observation schedules by dividing the task into long term and short term
components. The process and architecture have helped HST obtain sustained
e#ciencies of over 50 percent when pre­launch estimates indicated a maximum
of 35 percent e#ciency. The remainder of the paper is divided as follows. Sec­
tion 2 presents the architecture. Section 3 discusses observation planning as a
process and discusses more details on the long range planner as implemented by
271

272 Giuliano
OB1
OB2
OB3
OB4
OB5
OB6
WK1 WK2 WK3 WK4 WK5 WK6 WK7 WK8
Figure 1. Plan windows for observations 1­6 in weeks 1­8. Week 4 windows are highlighted.
the SPIKE software system (Johnston & Miller 1994). Section 4 evaluates the
approach by comparing the system to other approaches.
2. An Architecture for Creating E#cient and Stable Schedules
E#cient and stable observation schedules are created by dividing the scheduling
process into long range planning and short term scheduling. The long range
planner creates approximate schedules for observations and handles global opti­
mization criteria, and stability issues. The short term scheduler produces precise
one week schedules using the approximate schedule produced by the long range
planner as input.
The long range planner creates 4­8 week plan windows for observations.
A plan window is a subset of an observation's constraint windows, and repre­
sents a best e#ort commitment by the observatory to schedule in the window.
Plan windows for di#erent observations can be overlapping. In addition the
windows for a single observation can be non­contiguous. Figure 1 shows sample
plan windows for six observations. Constraint windows are long term limita­
tion as to when an observation can be executed due to physical and scientific
constraints. Constraint windows include sun avoidance, moon avoidance, user
window constraints, phase constraints, roll constraints, guide star constraints,
and time linkages between observations. The long range planner picks plan win­
dows based on balancing resources (e.g., observing time, observations which can
hide the SAA), and optimizing observation criteria (e.g., maximizing orbital visi­
bility, maximizing CVZ opportunities, maximizing plan window size, minimizing
stray light, minimizing o#­nominal roll).
The short term scheduler builds e#cient week long observation schedules
by selecting observations which have plan windows open within the week. The
short term scheduler is responsible for keeping track of which observations have
already been scheduled in past weeks.
Figure 1 shows a schedule with 6 observations with windows in weeks 1­
8. The figure shows that observations 1,2,3, and 5 are potential candidates for
scheduling in week 4.

Achieving Stable Observing Schedules in an Unstable World 273
Observation Data
Changing on Demand by PIs
Long Range Planning
Executed Nightly
Short Term Scheduling
Executed Weekly (T­3 weeks)
Plan windows
Execution Times
HST
Figure 2: Process data flow
3. Scheduling as a Process
The scheduling process, as illustrated in Figure 2, is an ongoing e#ort. The long
range planner is executed nightly to incorporate the changes made to observing
programs during the day. Each week a short term schedule is created which is
executed on HST 3 weeks later. Although the long range planner is executed
nightly, it must ensure that the resulting plan is stable.
Stability is achieved by considering the long range planner as a function
which maps a set of input observing programs, search criteria, and a previous
long range plan into a new long range plan. The long range planner partitions
observations into those which have windows assigned in the input LRP and
those which do not have input windows. In general, the scheduler will assign
new windows to those observations which are not scheduled in the input plan
and will pass through windows for observations which are scheduled in the input
plan. The system assigns windows for observations, which are not scheduled in
the input plan, in two steps. First, the system uses user defined criteria to
greedily find the best window for each observation. Second, a stochastic search
is used to adjust the resource levels.
There are two cases where the plan window for an observation which was
planned in the input LRP is changed. First, the system has a special case
for linked observations (e.g., OB1 after OB2 by 10­20 days). When the first
observation of a link set is executed the system will automatically adjust the
plan windows for subsequent observations in the link set based on the actual
time the first observation scheduled. A second case occurs when the constraint
windows for an observation change. The system measures the percentage of the
plan window in the observations current constraint window. If the percentage
is below a threshold then, based on a user flag, either the observation is re­
planned from scratch or no plan window is written and a status flag is set in
the output plan. If the percentage is above a threshold (but some of the plan
window is no longer in the constraint window) then the plan window is reduced
so that it overlaps with the plan window. The idea is that a minor change in
an observations constraint window due to PI adjustments or changes to HST
operations will not disrupt the long range plan.
The long range planner must deal with several anomalous cases. First, it
may be possible that loading an observation into the system causes a crash or an
error. A second anomaly occurs when the input products for an observation are
not ready. In either case the problem is caught by the system and any existing
plan windows are written to the output plan. A status field is marked so that

274 Giuliano
the anomaly can be investigated. This approach ensures that the LRP process
is stable. Problem observations do not drop out of the LRP and an error in one
observation does not halt the entire process.
4. Evaluation and Comparison with other Approaches
Using plan windows to communicate between long range planning and short
term scheduling has many advantages which are highlighted by contrasting this
approach with others. Alternatives are: 1) Short term schedule on demand one
week at a time. Do not long range plan; 2) Short term schedule the whole cycle
in advance. Do not long range plan; 3) Long range plan to non overlapping bins.
Short term schedule using the bin schedule as input. Short term scheduling on
demand does not meet the requirement that PIs be informed in advance of the
approximate time an observation will be executed. In addition, the approach
may run into resource problems as greedy algorithms can schedule all the easy
observations first. Short term scheduling the whole cycle in advance does, in
principle, tell PIs when observations will be scheduled. However, the schedule
would be very brittle. Changing a single observation would require the whole
cycle to be rescheduled. The net result would be an unstable plan and lots of
rescheduling work for operations sta#. Short term scheduling the whole cycle
at one time would also result in a combinatorial explosion in the search space
e#ectively preventing optimization of the schedule. Long range planning to
week long bins was the approach used in the first four HST observation cycles.
The approach was not successful. If the long range planner filled week bins to
capacity (or less) then the resulting short term schedules would be ine#cient
as the short term scheduler would not have the right mixture of observations
to select from. If the long range planner oversubscribed week bins then the
resulting short term schedules would be e#cient. However, the resulting long
range plan would be unstable as unscheduled observations in a week bin would
have to be rescheduled. This approach would only be feasible if the long range
planner knows enough of the short term scheduler constraints to produce week
bins with the right mixture of observations.
The plan window approach has advantages over the other approaches. First,
it supports the creation of e#cient short term schedules without oversubscrib­
ing the long range plan. E#cient week long schedules can be created because
overlapping plan windows provide a large pool of observations to select from. A
second advantage of this approach is stability. It helps to insulate the long range
plan from changes in observation specifications and HST operations. A third
advantage is that it divides the problem into tractable components. The short
term scheduler handles creating e#cient schedules while the long range planner
handles global resource optimization and stability.
References
Johnston, M., & Miller, G., 1994, in Intelligent Scheduling, eds. Zweben, M.,
Fox, M. (San Francisco: Morgan Kaufmann), 391, 422