In this paper, a micro-opportunistic approach to factory scheduling was described that closely monitors the evolution of bottlenecks during the construction of the schedule, and continuously redirects search towards the bottleneck that appears to be most critical. This approach differs from earlier opportunistic approaches, as it does not require scheduling large resource subproblems or large job subproblems before revising the current scheduling strategy. This micro-opportunistic approach was implemented in the context of the MICRO-BOSS factory scheduling system. A study comparing MICRO-BOSS against a macro-opportunistic scheduler suggests that the additional flexibility of the micro-opportunistic approach to scheduling generally yields important reductions in both tardiness and inventory

Sadeh, Norman M.

English

NASA Technical Reports Server

N93-32133
The MICRO-BOSS Scheduling System:
Current Status and Future Efforts
Norman M. Sadeh
Center for Integrated Manufacturing Decision Systems
The Robotics Institute
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213 - U.S.A.
sadeh@ri.cmu.edu
1. INTRODUCTION
lOver the past few years, several approaches
to scheduling have been proposed that attempt
to reduce tardiness and inventory costs by
opportunistically (i.e. dynamically) combining a
resource-centered perspective to schedule bot-
tleneck resources, and a job-centered perspec-
tive to schedule non-bottleneck operations on a
job by job basis. Rather than relying on their
initial bottleneck analysis, these schedulers
reexamine the problem each time a resource or
a job has been scheduled. This enables them to
detect the emergence of new bottlenecks during
the construction of the schedule. This ability
has been termed opportunistic scheduling [3].
Nevertheless, the opportunism in these systems
remained limited, as they required scheduling
large resource-subproblems or large job-
subproblems before allowing for a change in the
scheduling perspective (i.e. before permitting a
revision in the current scheduling strategy).
For this reason, we actually refer to these ap-
proaches as macro-opportunistic techniques.
In reality, bottlenecks do not necessarily span
over the entire scheduling horizon. Moreover
they tend to shift before being entirely
scheduled. A scheduler that can only schedule
large resource subproblems will not be able to
take advantage of these considerations. Often it
will overconstrain its set of alternatives before
having worked on the subproblems that will
most critically determine the quality of the en-
tire schedule. This in turn will often result in
poorer solutions. A more flexible approach
would allow to quit scheduling a resource as
soon as another resource is identified as being
more constraining 2. In fact, in the presence of
multiple bottlenecks, one can imagine a tech-
nique that constantly shi_ attention from one
bottleneck to another rather than focusing on
the optimization of a single bottleneck at the
expense of others. Therefore, it seems desirable
to investigate a more flexible approach to
scheduling, or a micro-opportunistic approach,
in which the evolution of bottlenecks is con-
tinuously monitored during the construction of
the schedule, and the problem solving effort
constantly redirected towards the most serious
bottleneck. In its simplest form, this micro-
opportunistic approach results in an
operation-centered view of scheduling, in which
each operation is considered an independent
decision point and can be scheduled without re-
quiring that other operations using the same
resource or belonging to the same job be
scheduled at the same time.
Section 2 describes a micro-opportunistic fac-
tory scheduler called MICRO-BOSS
(Micro-Bottleneck Scheduling System). Section
3 describes an empirical study that compares
iThis research was supported, in part, by the Defense
Advanced Research Projects Agency under contract
#F30602-88-C-0001, and in part by grants from McDon-
nell Aircraft Company and Digital Equipment Corpora-
t-ion.
275
211] describes an alternative approach in which
resources can be resequenced to adjust for resource
schedules built further down the road. This approach has
been very successful at minimizing makespan. Attempts
to generalize the procedure to account for due dates seem
to have been less successful so far [6].
https://ntrs.nasa.gov/search.jsp?R=19930022944 2020-03-17T05:16:35+00:00Z
MICRO-BOSS against a macro-opportunistic
scheduler that dynamically combines both a
resource-centered perspective and a job-
centered perspective. A summary is provided in
Section 4, along with a brief discussion of cur-
rent research efforts.
Consistency Checking): Con-
sistency enforcing techniques
prune the search space by infer-
ring new constraints resulting
from earlier reservation assign-
ments [2, 5].
2. A MICRO-OPPORTUNISTIC
APPROACH
In the micro-opportunistic approach im-
plemented in MICRO-BOSS, each operation is
considered an independent decision point. Any
operation can be scheduled at any time, if
deemed appropriate by the scheduler. There is
no obligation to simultaneously schedule other
operations upstream or downstream within the
same job, nor is there any obligation to schedule
other operations competing for the same
resource.
MICRO-BOSS proceeds by iteratively select-
ing an operation to be scheduled and a reser-
vation (i.e. start time) to be assigned to that
operation. Every time an operation is
scheduled, a new search state is created, where
new constraints are added to account for the
reservation assigned to that operation. A so-
called consistency enforcing procedure is ap-
plied to that state, that updates the set of
remaining possible reservations of each un-
scheduled operation. If an unscheduled opera-
tion is found to have no possible reservations
lei_, a deadend state has been reached: the sys-
tem needs to backtrack (i.e. it needs to undo
some earlier reservation assignments in order
to be able to complete the schedule). If the
search state does not appear to be a deadend,
the scheduler moves on and looks for a new
operation to schedule and a reservation to as-
sign to that operation.
In MICRO-BOSS, search efficiency is main-
tained at a high level by interleaving search
with the application of consistency enforcing
techniques and a set of look-ahead techniques
that help decide which operation to schedule
next (so-called operation ordering heuristic) and
which reservation to assign to that operation
(so-called reservation ordering heuristic).
1. Consistency Enforcing (or
. Look-ahead Analysis: A two-
step look-ahead procedure is ap-
plied in each search state, which
first optimizes reservation assign-
ments within each job, and then,
for each resource, computes con-
tention between jobs over time.
Resource/time intervals where job
contention is the highest help
identify the critical operation to
be scheduled next (operation or-
dering heuristic). Reservations for
that operation are then ranked
according to their ability to min-
imize the costs incurred by the
conflicting jobs (reservation order-
ing heuristic). By constantly
redirecting its effort towards the
most serious conflicts, the
scheduler is able to build
schedules that are closer to the
global optimum. Simultaneously,
because the scheduling strategy is
aimed at reducing job contention
as fast as possible, chances of
backtracking tend to subside
pretty fast too.
The so-called opportunism in MICRO-BOSS
results from its ability to constantly revise its
search strategy and redirect its effort towards
the scheduling of the operation that appears to
be the most critical in the current search state.
This degree of opportunism differs from that
displayed by other approaches where the
scheduling entity is an entire resource or an en-
tire job [3], i.e. where an entire resource or an
entire job needs to be scheduled before the
scheduler is allowed to revise its current
scheduling strategy.
276
3. PERFORMANCE EVALUATION
MICRO-BOSS was compared against a
variety of scheduling techniques, including
popular combinations of priority dispatch rules
and release policies suggested in the Operations
Management literature [5].
This section outlines a study comparing
MICRO-BOSS against a macro-opportunistic
scheduler that dynamically combined both a
resource-centered perspective and a job-
centered perspective,likein the OPIS schedul-
ing system [3].However, while OPIS relieson a
set ofrepair heuristicsto recover from inconsis-
tencies[4],the macro-opportunisticscheduler of
thisstudy was builtto use the same consistency
enforcing techniques and the same backtrack-
Lug scheme as MICRO-BOSS 3. The macro-
opportunistic scheduler also used the same
demand profiles as MICRO-BOSS. When
average demand for the most critical
resource/time "interval was above some
threshold level(a parameter ofthe system that
was empirically adjusted), the macro-
opportunistic scheduler focused on scheduling
the operations requiring that resource/time in-
terval,otherwise it used a job-centered perspec-
tive to identify a critical job and schedule some
or all the operations in that job. Each time a
resource/time interval or a portion of a job was
scheduled, new demand profdes were computed
to decide which scheduling perspective to use
next. Additional details on the implementation
of the macro-opportunistic scheduler can be
found in [5].
In order to compare the two schedulers,a set
of 80 scheduling problems was randomly
generated to cover a wide variety of scheduling
conditions: tight/loose average due dates,
narrow/wide due date ranges, one or two bot-
tleneck machines. Each problem involved 20
jobs and 5 resources for a total of 100 opera-
tions(see[5]for furtherdetails).
SAn alternative would have been to implement a varia-
t-ion of MICRO-BOSS using the same repair heuristics as
OPIS. Besides being quite time-consuming to implement,
such a comparison would have been affected by the
quality of the specific repair heuristics currently im-
plemented in the OPIS scheduler.
i $0.
I _" _ _ MACRO'OPPORTUNISTIC t
26. _ MICRO*BC6S
/ \ / _ r"'"
// \_#,/
,, / _ / /
'< ,4',1iT/
0
Figure 3-1: Tardiness performance of
MICRO-BOSS and the
macro-opportunistic scheduler
on eight different problem sets.
l 80.
. 70. I \ /
//P\\ / \ /
_ eo. i/'_,_.,#/ \ I
_' _
,._.
ZO,
_*-- _ _ MACRO-OPPORTUNISTIC10 0------,-4 MICRO-BOSS
0
Probb_m ,_t
Figure 3-2: Flowtime performance of
MICRO-BOSS and the
macro-oppertunistic scheduler
on eightdifferentproblem sets.
Figures 3-1, 3-2 and 3-3 summarize the
results of the comparison between MICRO-
BOSS and the macro-opportunistic scheduler 4.
The macro-opportunistic scheduler was consis-
tently outperformed by MICRO-BOSS (under
all eight scheduling conditions) both with
277
4The results presented in this section correspond to the
69 experiments (out of 80) that were each solved in less
than 1,000 search states by the macro-opportunistic
scheduler.
,/.',,' ",,Vk',,-'.
10. I _ MIGRO-Bo6sMACRO'OPPORTUNISTICI
0
Prab/em Set
Figure 3-8: In-system time performance of
MICRO-BOSS and the
macro-opportunistic scheduler
on eight different problem sets.
respect to tardiness, flowtime (i.e. work-in-
process) and in-system time (i.e. total inventory,
including finished-goods inventory). More
generally, these results indicate that highly con-
tended resource time intervals can be very
dynamic, and that it is critical to constantly fol-
low their evolution in order to produce quality
schedules.
In most problems, MICRO-BOSS achieved a
search efficiency of 100% (computed as the ratio
of the number of operations to be scheduled
over the number of search states that were
visited), and required about 10 minutes of CPU
time to schedule each problem. The current sys-
tem is written in Knowledge Craft, a frame-
based representation language built on top of
Common Lisp, and runs on a DECstation 5000.
scheduling strategy. This micro-opportunistic
approach has been implemented in the context
of the MICRO-BOSS factory scheduling system.
A study comparing MICRO-BOSS against a
macro-opportunistic scheduler suggests that the
additional flexibility of the micro-opportunistic
approach to scheduling generally yields impor-
tant reductions in both tardiness and inventory.
Current research efforts include:
• Adaptation of MICRO-BOSS to
deal with sequence-dependent
setups
• Development of micro-
opportunistic reactive scheduling
techniques that will enable the sys-
tem to patch the schedule in the
presence of contingencies such as
machine breakdowns, raw
materials arriving late, job cancela-
tions, etc.
APPENDIX" PROBLEM SETS
Problem Sets
Number Avg. Due Date Problem
of Bottlenecks Due Date Range Set
1 loose wide 1
1 loose narrow 2
1 tight wide 3
1 tight narrow 4
2 loose wide 5
2 loose narrow 6
2 tight wide 7
2 tight narrow 8
4. CONCLUSIONS
In this paper, a micro-opportunistic approach
to factory scheduling was described that closely
monitors the evolution of bottlenecks during the
construction of the schedule, and continuously
redirects search towards the bottleneck that ap-
pears to be most critical. This approach differs
from earlier opportunistic approaches, such as
the one described in [3], as it does not require
scheduling large resource subproblems or large
job subproblems before revising the current
REFERENCES
1. J. Adams, E. Balas, and D. Zawack. 'The Shii_ing
Bottleneck Procedure for Job Shop Scheduling".
Management Science 34, 3 (1988), 391-401.
2. A.K. Mackworth and E.C. Freuder. "The Complexity
of some Polynomial Network Consistency Algorithms for
Constraint Satisfaction Problems". Artificial Intelligence
25, 1 (1985), 65-74.
3. Peng Si Ow and Stephen F. Smith. 'Viewing Schedul-
ing as an Opportunistic Problem-Solving Process".
Annals of Operations Research 12 (1988), 85-108.
278
4. P.S. Ow, S.F. Smith, and A. Thiriez. Reactive Plan
Revision. Proceedings of the Seventh National Con-
ference on Artificial Intelligence, 1988, pp. 77-82.
6. Norman Sadeh. Look-ahead Techniques for Micro-
opportunistic Job Shop Scheduling. Ph.D. Th., School of
Computer Science, Carnegie Mellon University, Pitts-
burgh, PA 15213, March 1991.
6. P. Serafini, W. Ukovich, H. Kirchner, F. Giardina, and
F. Tiozzo. Job-shop scheduling: a case study. In
Operations Research Models in FMs, Springer, Vienna,
1988.
279


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