The Payload Processing for Space-Station Operations (PHITS) is a prototype modeling tool capable of addressing many Space Station related concerns. The system's object oriented design approach coupled with a powerful user interface provide the user with capabilities to easily define and model many applications. PHITS differs from many artificial intelligence based systems in that it couples scheduling and goal-directed simulation to ensure that on-orbit requirement dates are satisfied

Castillo, D.

Ihrie, D.

Mcdaniel, M.

Tilley, R.

English

NASA Technical Reports Server

N88-16425
An AI Approach for Scheduling Space-Station Payloads
at Kennedy Space Center
D. Castillo, D. Ihrie
HARRIS Corporation
Melbourne, FL 32955
by
M. McDaniel
MDAC-KSC
KSC, FL 52899
R. Tilley
NASA/SS-OCO
KSC, FL 32899
ABSTRACT
Payload Processing for Space-Station Operations, including
mission manifesting and its effect on KSC Ground Resource
Allocation, represents a class of ill-structured, complex
scheduling problems which are often unsuitable for applying
optimization algorithms. The situation has inspired the
development of AI-based planning aJad scheduling systems
specifically designed for Payload Processing activities. This
paper examines the application of am AI-based system, called
PHITS, to integrated payload scheduling and its effect on Ground
Resource Allocation at KSC.
Unique to the PHITS approach is the process by which
schedule generation occurs. Experiments are represented in terms
of objects which are semantically related based on mission goals.
Unlike conventional scheduling systems, task flows are only
defined for individual objects. Integrated schedules are
generated by evaluating object, attribute, value (OAV) triplets
for experiments considered candidates for flight. OAV triplets
contain user-defined constraints on object interaction. A goal
directed simulation subsystem examines the schedule and performs
conflict resolution as needed to achieve the on-orbit requirement
goal.
INTRODUCTION
Part of Kennedy Space Center's (KSC) responsibility is the
prelaunch preparation and integration of experiments for
transport to Space Station and the deintegration and processing
of returned hardware. As an example, consider an experiment
which operates in a pressurized laboratory module. The
experiment is first received at the launch site where it is
inspected and integrated into a laboratory equipment rack. A
high fidelity simulator then verifies the module-to-rack
interfaces prior to installing the rack in a logistics module.
Finally, the logistics module is installed in the orbiter for
transport to the Space Station. Each payload generates unique
and complex demands which require a large array of resources
including facilities, equipment, materials, personnel skills and
training. Furthermore, the process becomes even more complex
when processing multiple shuttle flights in parallel. As a
result of the relatively fixed level of available resources and
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https://ntrs.nasa.gov/search.jsp?R=19880007043 2020-03-20T07:26:10+00:00Z
highly structured set of schedule constraints imposed by the
shuttle launch and landing, planning and scheduling of processing
activities associated with new flights represents a nontrivial
task. Efficient planning and management of this process is a key
element in maximizing the effective use of the Space Station
while minimizing the cost.
The above situation has directed researchers toward AI-based
scheduling systems designed to operate in the domain of
resource-constrained scheduling. This effort has produced
systems such as MAESTRO [2], PLANNET [8], MARS [9], PEGASUS [4]
and others. A commonality among many of these systems is the
methodology used in generating conflict-free schedules. In many
cases, a schedule is generated using traditional CPM routines,
followed by heuristic methods that attempt to produce a conflict
free schedule. More recently, researchers have investigated the
effects of temporal reasoning applied to resource-constrained
scheduling _[I] [ii_ in hopes of automating deductions about time.
The Payload Handling Inventory Tracking System (PHITS),
developed by Harris Corporation [5], deviates from the above
philosophical methodologies in that it provides a modeling
environment that couples scheduling, simulation and AI
technologies in one unique modeling environment. Bruno et al.
[3] share this philosophy and have applied it to the domain of
Flexible Manufacturing Systems (FMS). However, the distinction
between PHITS and Bruno's FMS system is the way schedule
generation and simulation are performed. This paper examines the
application of PHITS to integrated payload scheduling and the
effects this has on Ground Resource Management giving particular
attention to the usability, scheduling and simulation aspects of
PHITS. The reader is referred to Ihrie et al. [6] for an
overview of the technologies used in PHITS.
STORAGE STUDY OBJECTIVES
MDAC-KSC was tasked to identify Space Station payload
storage policy alternatives at KSC. This required a forecast of
storage requirements in relationship to experiment types, sizes
and numbers that flowed through the processing facilities at KSC.
It was determined that a software tool capable of tracking
resources and forecasting their requirements in a very dynamic
environment would greatly assist MDAC-KSC in accomplishing the
objectives of the study. In addition, a "what if" feature for
performing sensitivity analysis on the primary variables defined
in the study would provide the flexibility for examining
competing scenarios. MDAC in cooperation with NASA-KSC agreed to
use the PHITS system for supporting their study efforts.
Having defined the manifest as the primary variable for
determining storage study policies, it was recognized that the
ability to generate manifests was necessary since manifests were
generally unavailable for most flights considered in the study.
PHITS possessed the capability to generate manifests based on
362
experiments from the Civil Needs Database and their associated
constraints. Flights from 1994 to 2002 including 127 experiments
were considered. Based on the results of the manifest, PHITS
produced a payload schedule defining the timeline for all tasks
and resources. The schedule was then simulated to determine
storage requirements and resolve any resource conflicts that
occurred. The following sections describe this process.
BUILDING THE STORAGE STUDY MODEL
Developing the Storage Study Model required the user to
define the objects relevant to the study, such as experiments,
log-modules etc. PHITS provides a powerful user interface for
simplifying the process of identifying and defining objects. The
Genealogy Editor was utilized to identify each object as a class
or instance and graphically portray all parent-child
relationships. Figure I illustrates the Genealogy Editor.
Add I;_ Ck_r Ge_loa_, Oe_tt40bj6¢L Dele_ I_)_ _ _ CIm, I_¢ Ob)ect, Sevc
Genealogy
ouse-l: Select th_s frame to bo used I. relatlonuhlp.; Mouse-H; Move frame.; Mou_e-_; Menu.
• 6,.,. other comma,d,,, pr_,,, r.h_ft. ¢_nt_,,I. Con_,_l-';_,ft. Met_-_;h,_t. _ _,,))_r
Figure-l: PHITS Genealogy Editor
Object task flow scripts were then defined for each object.
Task flows were either inherited from a class of Experiment, or
uniquely created for the specific experiment being defined.
Task-based resources and storage types were identified during
this process. OAV requirements were also instantiated at this
time. OAV triplets represent constraints one object imposes on
other objects. In the Storage Study, for example, an experiment
object that affects available shuttle mass is represented by the
following OAV triplet:
((shuttle mass 2345))
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ORIGINAL PAGE IS
eoo Qu.4/j 
PHITS uses this property list to determine if the experiment is
capable of being attached to the flight. The scheduling
component utilizes OAV triplets for developing the entire task
network for a given mission.
Once all objects were identified and defined in terms of
their task network flows, the Attribute Editor was accessed for
defining object attributes. For example, object HB-S contains an
attribute called "launch-date" with a facet of "is" and a value
of "July lS, 1994." For the Storage Study, each experiment
contained an attribute called "sq-ft" which represented the
square-foot dimensions of the experiment. Furthermore, each
experiment class contained an algebraic expression attribute
utilized to compute storage requirements based on the sq-ft
attribute of each experiment.
PHITS provides a Structure Editor for attaching experiments
to a given flight. Attaching experiments in this manner
guarantees the experiment will be manifested during payload
scheduling. This is a useful feature when a manifest has been
previously set by NASA or when analyzing different manifests.
PAYLOAD SCHEDULING
Payload scheduling in PHITS consists of connecting
individual object definitions into a single integrated payload
flow that satisfies all connector constraints. Figure 2
illustrates the task flow script for individual objects. From
Figure 2 the object shuttle is defined by more than one set of
task flows. Each flow may contain one or more start and end
nodes. End nodes contain information corresponding to a
connecting set of tasks on another object.
w, m
w mm
H'....,,,l_t *_',lJ y Ih.t,d.p .I, ,JL..mt_n
FIGURE-2: OBJECTS TASK FLOW SCRIPTS
uf,
w, w,_
t-@
The scheduler operates on a user-selected set of missions
and experiments. A mission is considered an object with a launch
date and possibly containing a set of experiments. Additional
experiments are left unattached and may be considered candidates
for flight. For each manifested and unmanifested experiment, the
364 ORIGINAL PAGE I$
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ORIGINAL PAGE
_ POOR QUALIT_
scheduler first constructs a complete sta_d-alone task flow by
following connections between object task flows. OAV triplets
are not considered during this forward pass. Figure 5 represents
this process:
.)
FIGURE-3: STAND-ALONE TASK FLOWS
At this point, the scheduler must eliminate duplicate tasks
from experiment flows attached to a single mission, manifest the
unmanifested experiments, replace object classes with specific
objects and assign task dates to meet launch dates. The key
ingredient here is ensuring that all mission constraints are met.
Constraints are represented by the OAV triplets mentioned
previously. At each point in the flow where multiple objects are
integrated, the scheduler attaches as many objects as possible
without violating the OAV constraints. Manifested experiments
are attached to a mission first, followed by unmanifested
experiments wherever possible, based on a user-selected priority.
The final output is a fully integrated task flow for each
mission, such as that shown in Figure 4. Using this approach,
the scheduler guarantees each mission is internally conflict free
and satisfies all object constraints.
A CPM routine is then applied to the overall network to
instantiate the Early-Start, Late-Start, Early-Finish and
Late-Finish dates on the network. The critical path is defined
as the task path where Early-Start is equal to Late-Start and
Early-Finish is equal to Late-Finish.
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ORIGINAL PAGE IS
OF, POOR QUALITY
_T, n,-leQ _clt _-J
uc,| _-_I ommcl, _-|
FIGURE-4 : TASK NETWORKFLOW
As mentioned above, the scheduler instantiates the
early-start late-finish range for each task. This range dictates
the permissible time interval of each task. If the range is
violated, then the on-orbit requirement date is not guaranteed,
resulting in the possibility of a slipped schedule. One of the
designated tasks of the simulation subsystem is to ensure
schedules are maintained. Figure S illustrates the early-start
late-finish range for a task.
Early-Start o
< Task Duration ,_
Late-Finish
Permissable Task Range
FIGURE-5: EARLY-START/LATE-FINISH RANGE
SIMULATION
Simulation in PHITS represents a complex process of tracking
objects, resolving resource conflicts and dynamically
re-adjusting in order to achieve the on-orbit requirement goal.
An event calendar is utilized as the central control structure
that communicates with other objects by message passing in much
366
the same way as KBS [i0] and ROSS [7]. Tasks are examined by the
event calendar to determine if a violation in the early-start
late-finish range is imminent. If a violation is detected, the
task object is given to the Meta-level Resource Manager to
determine if a local resolution is feasible. Local resolution
implies conflict resolution is applied to the task in question
without disrupting the processing of other tasks.
Local resolution is an attempt to quickly resolve resource
conflicts by applying a small set of heuristic information on a
local scale. PHITS utilizes a Pre-processing facility as a
vehicle for performing local resolution. If a task range
violation is detected by the event-calendar or Resource Manager,
then Pre-processing is initiated. Figure 6 represents a
conceptual illustration of a task range violation. Notice the
Late-Start milestone has been exceeded, therefore, local
resolution will attempt to shorten the task duration so that the
task does not violate its Late-Finish range. Pre-processing
applies very simple heuristics such as overtime, increased
resources where applicable, etc. to satisfy the task's range
constraints. If this effort is unsuccessful, then global
resolution is initiated. Global resolution is defined as the
process where resolving a conflict for one task affects another
task's processing. The current version of PHITS contains the
architecture for supporting global resolution, however, a
significant amount of knowledge engineering is needed before full
scale implementation can occur. Although lack of global
resolution did not adversely effect the outcome of the Storage
Study, it was recognized that other candidate studies would
probably require this capability.
Permissable Task Range
FIGURE-6: TASK RANGE VIOLATION
Due to the object-oriented nature of PHITS, storage
requirements were easily generated. Attribute expressions
containing algebraic equations and sq-ft values were evaluated at
simulation time. Storage requirements based on experiment and
storage types were collected. Cumulative storage requirements
were reflected graphically as temporal representations which
proved useful for comparing multiple manifest scenarios. Gantt
charts were utilized for displaying the overall processing
schedule. Figures 7 and 8 illustrate these features.
367
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s _e other command_, press Sfl ft. Control. Control-Shift, Hera-shift, or Super.
FIGURE-7: RESOURCE UTILIZATION GRAPH
PHIT8 OANTT CHART OISPLAY
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I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I
I I I I I 1 I I I I I I I l I I I I I I I l I I I I I I I I I I I I Ii
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i I I I I I l t t I ! I I I I I I t I I I I I I I t I I l t I I I I I
i I I I I I I I I I I 1 I 1 I l I I I I I I F I I I I I I I I I I I 1
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I l I I I &, , , ' : i : 1 1 1 1 1 t * i i _ i , i , , i i , _ l . | , i , , , a J ,
m_e-L -R: Olht¢ by months.
_e other c_aod'_, pr_'_', e_ if, f_omrol, Contr_t-Sflift. Neta-Shtft, or Super
FIGURE-8: GANTT CHART OF SCHEDULE
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ORIGINAL PAGE Lg
OF POOR QUALrI_
Although not utilized during the Storage Study analysis,
PHITS features an animation component which demonstrates a
pictorial view of the simulation process. Object icon attributes
are updated dynamically to represent the discrete changes in the
simulation status. This proves valuable when an analyst is
concerned with tracking specific objects throughout the
simulation, or discovering potential bottlenecks of a process.
FUTURE RESEARCH
Future research efforts for PHITS will focus on the Resource
Manager and global resolution. Temporal relations will be
examined in an effort to better understand and manipulate
conflicts on the global scale. Other directions include porting
the technology from a Symbolics environment to an 80386
environment. Incorporating intelligence features into the
manifesting capability of PHITS also has potential for future
research. Finally, the technology contained in PHITS will be
investigated for modeling other problem domains within the Space
Station Program.
CONCLUSION
PHITS is a prototype modeling tool capable of addressing
many Space Station related concerns. The system's
object-oriented design approach coupled with a powerful user
interface provide the user with capabilities to easily define and
model many applications. PHITS differs from many Al-based
systems in that it couples scheduling and goal-directed
simulation to ensure on-orbit requirement dates are satisfied.
ACKNOWLEDGMENT
This work would have not been possible without the patience
and support of the people at MDAC-KSC, particularly John Weber
and David Dixon. The authors express their appreciation to these
individuals.
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An AI approach for scheduling space-station payloads at Kennedy Space Center

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