LIDS-R-1408
September, 1984
Revision of March, 1985
A CONTROL THEORIST'S PERSPECTIVE ON RECENT
TRENDS IN MANUFACTURING SYSTEMS
by
Stanley B. Gershwin *,
Richard R. Hildebrant **,
Rajan Suri ***,
and
Sanjoy K. Mitter
*
*
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139
** C. S. Draper Laboratory
Cambridge, Massachusetts 02139
Division of Applied Science
Harvard University
Cambridge, Massachusetts 02138
***
presented at the
23rd IEEE Conference on Decision and Control
Las Vegas, Nevada
December, 1984
page 2
PERSPECTIVE
1. Introduction
the
While
basic
understanding
plete.
These
the
ments),
large
uncertainty
multiple
machine
to
due
require-
production
in
variability
hierarchies,
level
and
studied
have
theorists
and
by randomness
complicated
control and systems
in
in manufacturing
a control
a view
and
in
essential
that
issues
to present certain
theorists
systems
thinking.
current
in
a
that
way
establish, in
issues
that
We then
research
will
summarize
of that framework.
make that
4)
facilitate
This
influenced
of
this area.
impact, it
paper
is
and become
intended
is
to control
this.
for manufacturing
by control
practice
and critique
of
has a vocabu-
management
2, a framework
current
in
perspective
and terminology
in manufacturing
heavily
(Section
helpful
directions.
Section
is
to
the problems
research
with recent
familiar
We
learn
they
group
the
this community
that can be
of systems
order for this
However,
systems from
believe that
theorist. We
interpretation
an
to present
is
this paper
of
purpose
progress
recent
view
scheduling,
planning,
contexts.
The
lary
and
incom-
remains
issues
(particularly
requirements,
data
that
other issues
are
They
rapidly, a
improving
is
production
environment
manufacturing
and
systems
the
include
issues
failures
other
of
--
including processes
--
manufacturing
software
of work-in-process.
control
in
and
hardware
computer
and
of
technology
and systems
(Section
them from the
3)
and
point
of
page 3
PERSPECTIVE
2.0 General Perspective
The purpose of manufacturing
system control is not different
in essence from many other control
problems:
it
is to ensure
that a complex system behave in a desirable way.
from control
realization
areas.
theory are relevant
is
quite different
here,
although their specific
from more
traditional
The standard control theory techniques
have not yet seen a manufacturing
represented
by a linear system
is not surprising;
application
do not apply:
we
system that can be usefully
with quadratic
standard techniques
what have been standard problems.
Many notions
have
objectives.
This
been developed for
Manufacturing
systems can be
an important area for the future of control; new standard techniques will be
developed.
Some central
plexity,
back.
hierarchy,
Important
control
issues in manufacturing
discipline,
capacity,
notions of control
variables,
the objective
model, and constraints.
It
systems
uncertainty,
theory include
function,
include
and
comfeed-
state and
the dynamics or plant
would be premature
to try to identify
these with all the issues outlined in this paper;
it
would even
go against the purpose of the paper, which is to stimulate such
modeling
activity.
2.1 Complexity
Manufacturing
systems are large scale systems.
volumes of data are required to describe them.
impossible; suboptimal strategies for planning
archical
being
Enormous
Optimization is
based on hier-
decomposition are the only ones that have any hope of
practical.
page 4
PERSPECTIVE
2.2 Hierarchy
There are many time scales over which planning and scheduling decisions must be made.
The longest term decisions involve
capital expenditure or redeployment.
The shortest involve the
times to load individual parts, or even robot arm trajectories.
While these decisions are
made separately, they are related.
particular, each long term decision presents an assignment
next shorter term decision-maker.
scale.
the capacity--explicitly
into
The definition of the capacity depends on the time
For example, short-time-scale
the set of machines operational
capacity is
Machine
capacity is
at any instant.
an average of short-time-scale
Level
trol. This includes
the calculation
robot arm trajectories:;
semiconductors.
control
of
There
capacity.
the machine level con-
Other
and other steps
short-scale-issues
tool:
of optimal
"ladder diagrams"
motors, and hydraulics
of furnaces
a cutting
in particular,
in
machine
for
tools,
in the fabrication
include
adaptive
of
the detailed
machining.
is no rule that determines exactly what this shortest
time scale is.
semiconductor
The issue
A robot arm movement can take seconds
while a
oxidation step can take hours.
at this time scale
individual operation.
is
the optimization
of each
Here, one can focus on minimizing
or other cost of each separate
material.
Long-time-scale
and implementation
the design of
microswitches,
and the control
a function of
Control
At the very shortest time scale is
relays,
to the
The decision must be made in a
way that takes the resources--ie,
account.
In
movement
the time
or transformation of
One can also treat the detailed relationships
among
page 5
PERSPECTIVE
operations.
problem.
An
example
of
this
is
the
Here, a large set of operations
to be performed
line
is
grouped
at stations along a production
tive is to minimize
balancing
line.
into tasks
The objec-
the maximum time at a station, which results
in maximum production rate.
Additional
control problems
detection of wear and breakage
temperatures
control
at this
time scale include the
of machine tools, the control of
and partial pressures
in furnaces,
of the insertion of electronic
the automatic
components
into printed
circuit boards, and a vast variety of others.
Cel
I
Level
At the next time scale, one
must consider
of a small number of machines.
includes
This is
the interactions
cell level control and
the operation of small flexible manufacturing
The important issues include routing and scheduling.
problem is
The control
that of ensuring that the specified volumes are actu-
ally produced.
At this level,
operations are
taken as given.
operations
systems.
the detailed
specifications
of the
In fact, for many purposes, the
themselves may be treated as black boxes.
The issue here is to move parts to machines in a way that
reduces unnecessary
idle time of both parts and machines.
The
loading problem is that of choosing the times at which the parts
are loaded into the system or subsystem.
The routing problem
is
to choose the sequence of machines the part visits and the scheduling problem is
to choose the times at which the parts visit
the machines.
The important considerations
in routing include the set of
PERSPECTIVE
machines
page 6
available
not desirable
that can do the required
to use a flexible machine
done by a dedicated one, since
to do jobs for which there
In scheduling,
required machines
quirements are
met.
system resources
the flexible
failures,
machine may be able
that parts visited
also guaranteeing
in an efficient
way.
operator
is
allocating
These resources
and storage
problem at this level is
their
that production re-
At this level, the issue
disruptions on factory operations.
chine
is often
are no dedicated machines.
machines, transportation elements,
A control
It
to do a job that can be
one must guarantee
while
tasks.
include
space.
to limit the effect of
Disruptions are
absences, material
due to ma-
unavailability,
sur-
ges in demand, or other effects that may not be specified in
advance
but which are inevitable.
This problem may be viewed as
analogous to the problem of making an airplane robust to sudden
wind gusts, or even to loss of power in one of three or more
engines.
Factory
Level
At each higher level, the time scale lengthens
under concern grows.
several
cells.
first stage
At the next higher level, one must treat
For example,
in
printed
is a set of operations
Metal
is
tion.
Later,
and the area
circuit
fabrication,
the
that prepare the boards.
removed, and holes are drilled.
At the next stage,
components are inserted.
The next stage is the soldering operaStill later,
the boards are tested and reworked if
they are assembled
takes much time and a good deal
Issues of routing
into the product.
necessary.
This process
of floor space.
and scheduling
remain important here.
page 7
PERSPECTIVE
However,
setup times become crucial.
That is,
after a machine or
cell completes work on one set of parts of the same or a small
number of types, it
is often necessary to change the system
configuration in some way.
the cutters in a machine
must remove the remaining
For example, one may have to change
tool.
In
printed circuit assembly, one
components from the insertion machines
and replace them with a new set for the next set of part types to
be made.
The scheduling problem
is now one of choosing
at which these major setups must take place.
called
the
tooling
This is
the times
often
problem.
Other issues are
important
at still
longer time scales.
One
is to integrate new production demands with production already
scheduled in a way that does not disrupt the system.
class of
decisions are those pertaining
Another
to medium-term capacity,
such as the number of shifts to operate, and the number of contract employees
to hire, for the next few months.
sion, at a still longer
capital
time
equipment of the
consider
such
strategic
scale, is
factory.
goals
duct quality, and responsiveness
Another deci-
the expansion
of the
At this time scale,
as
market
share,
one must
sales,
pro-
to customers.
2.3 Discipline
Specified
Manufacturing,
tems degenerate
operating rules are required for complex
communication,
and other large sys-
into chaos when these rules are disregarded
when the rules are inadequate.
participants
transportation
systems.
In
the manufacturing
must be bound by the operating
or
context, all
discipline.
This
includes the shop floor workers, who must perform tasks when re-
page 8
PERSPECTIVE
quired; and managers, who must not demand more than the system
can produce.
It is essential that constraints on allowable control actions
constraints
be imposed on all levels of the hierarchy.
must allow sufficient freedom for the
These
decision-makers
at each level to make choices that are good for the system as a
whole, but they must not be allowed to disrupt its orderly operation.
2.4 Capacity
An important element in the discipline
capacity.
will
of a system is
its
Demands must be within capacity or excessive queuing
occur, leading
to excessive
effective capacity.
costs, and possibly
High level managers must not be allowed to
make requirements
on their subordinates
city; subordinates
must be obliged to accurately
capacities
to reduced
to those
All operations
higher
which exceed their capareport their
up.
at machines take a finite amount of time.
This implies that the rate at which parts can be introduced into
the system is limited.
Otherwise, parts would be introduced into
the system faster than they could be processed.
would then be stored in buffers
system) while
(or
These parts
worse, in the transportation
waiting for the machines to become
available,
resulting in undesirably large
work-in-process
and reduced effec-
tive capacity.
that throughput
(parts actually
produced)
rate is
The effect
is
may drop with increasing
beyond capacity.
system carefully
loading
Thus, defining
rate, when loading
the capacity of the
is a very important first step for on-line
scheduling.
An additional
complication
is
that manufacturing
systems
page 9
PERSPECTIVE
involve
people.
It
is
than machine capacity,
aspects.
since it
Human
harder
particularly
capacity
measure
at
all
outside
may be
harder
to
define
of
its capacity
the
capacity
hierarchy.
and it
is
No
system
may be possible
Just-in-Time
place over a relatively long time scale.
essential,
therefore,
a discipline
can
futile at best and dama-
On the other hand, it
This is a goal of the Japanese
then to develop
well,
are
expand the capacity of a given system by a learning
is
as
such as whether the environ-
and respecting
levels
ging at worst to try.
It
capacity
rapid changes.
Defining, measuring,
important
human
when the work has creative
can depend on circumstances
ment is undergoing
produce
to
process.
approach, which takes
See Section
to determine
for staying
to
4.4.
what capacity
within it,
is,
and finally
to expand it.
2.5 Uncertainty
All real systems are subject
to random disturbances.
precise time or extent of such disturbances
some statistical
The
may not be known, but
measures are often available.
For a system to
function properly, some means must be found to desensitize
it
to
these phenomena.
Control theorists often distinguish
between random events
and unknown parameters, and different methods have been developed
to treat them.
In a manufacturing
operator absences,
examples of random
parameters
system, machine
failures,
material shortages, and changing demands
events.
Machine
that are often unknown.
reliabilities
are
are examples
Desensitization
of
to uncertain-
page 10
PERSPECTIVE
ties is
one of the functions
of the operating
discipline.
In
particular, the system's capacity must be computed while taking
disturbances
into account, and the discipline
quirements to within that capacity.
must restrict
re-
The kinds of disturbances
that must be treated differ at different levels of the time scale
hierarchy: at the shortest time scale, a machine failure
influen-
ces which part is loaded next; at the longest scale, economic
trends and technological
changes influence marketing
decisions
and capital investments.
It
is our belief that such disturbances
effect on the operation of a plant.
take
these events
ty in doing
into account, in
can have a major
Scheduling
spite
and planning
must
of the evident difficul-
so.
2.6 Feedback
In order to make good decisions under uncertainty,
necessary to know something
amount
is
of material already processed.
goals.
strategies
essential, especially
decisions
At the shortest time
the conditions of the machines and the
designing good feedback
It
is
about the current state of the system
and to use this information effectively.
scale, this includes
it
is
Control
theorists know that
generally
a hard problem.
at the short time scale,
are calculated quickly and be relevant
that these
to long term
The tradeoff between optimality and computation gives
rise to many interesting
research
directions.
page 11
PERSPECTIVE
3.0 Survey and Critique
of Practical Methods
The manufacturing
ennvironment
of important and challenging
aware.
is one of the richest sources
control
problems of which we are
Until recently, however, the classical
and modern control
community has not been attracted to this opportunity.
is undoubtedly
that the manufacturing area has never been per-
ceived as needing the help.
manufacturers
One reason
Extreme competition from overseas
has, more than anything
else, changed this percep-
ti on.
Another reason why the manufacturing
the attention of control theorists
and some argue
This is
is
theory has to a large
automatically
manual
the
effort.
of
more fully automated
flexibility,
manufacturing
beginning
inexpensive
that
A wide variety of methods
and planning.
availCon-
assumed plant that
systems
run largely on
to change, however, due to
computation, the installation
systems, and the additional
quality, etc.
with scheduling
information
current or even correct.
extent, implicitly
controlled;
All this is
availablity
not been,
large complex systems is
Also, there has not been sufficient
able for feedback control that is
is
that the area has
still not, amenable to the their techniques.
because, in part, modeling
difficult.
trol
is
area has not enjoyed
are
placed
requirements
on these
are available
of
of
systems.
to industry to deal
The purpose of this section is
to
survey these methods and to critique them according to the outline of the previous
practice
section.
section.
in controlling
A representative
manufacturing
The intention is to give
current state
of manufacturing
systems
survey
is
of current
provided
the reader perspective
control.
in this
on the
PERSPECTIVE
page 12
3.1 Factory Level Control
3.1.1
Traditional Framework
The manufacturing community is accustomed to thinking about
production control within a particular mature framework.
All of
the functions necessary for planning and executing manufacturing
activities in order to make product most efficiently have been
grouped into a few large areas.
These areas and the general
interrelationships among them are shown in Figure 1.
This diagram shows a tremendous amount of interaction, where
information is fed forward and back, among the different areas.
Also, the diagram deals mainly with the resource allocation
aspect of the production control problem.
Other important traditional areas that are integral to a successful control system are
receiving, cost planning and control, and such financial functions as accounts receivable, accounts payable, etc.
Function
Descriptions
Brief descriptions of the functions
major areas are given below:
performed
within the
FORECASTING.
Demand is projected over time horizons of various
lengths.
Different forecast models are maintained by this function.
MASTER SCHEDULING provides a "rough-cut" capacity requirements
analysis in order to determine the impact of production plans on
plant capacity.
Comparisons are made between the forecast and
actual sales order rate, sales orders and production, and finally, scheduled and actual production.
MATERIAL REQUIREMENTS PLANNING (MRP) determines quantity and
PERSPECTIVE
page 13
timing of each item required
--
both manufactured
and purchased.
For each end-item, the quantity of all components and subassemblies is
determined, and, by working backwards
of final
assembly, MRP determines when production or ordering
these subassemblies
should occur.
A more detailed capacity
quirements analysis is made and operation
termined.
Also, lot-sizing is
from the the date
performed
tends to be highly detailed, there is
sequencing
at this
re-
is de-
stage.
no mechanism
of
While MRP
to take random
events and unknowns into account, so it
can lead to excess compu-
ter usage,
in providing
and
misleading
precision, delays
schedules,
rigidity.
CAPACITY REQUIREMENTS PLANNING
forecasts work-center load for
both released and planned orders and compares this figure with
available
capacity.
The user specifies the number and duration
of time periods over which the analysis
is
ORDER RELEASE is
manufacturing planning
and execution.
function
the connection
between
When an order is
creates
performed.
scheduled
the documentation
required
for release, this
for initiating
pro-
duc ti on.
SHOP FLOOR CONTROL
that is
responsible
function performs
well as ancillary
is a lower level,
for carrying
INVENTORY
tions
efficiency,
and
and tooling.
Data is
This
collected on the
of work centers
(uti-
productivity).
CONTROL performs general accounting
as well
plan.
and tracking of product as
disposition of product and the performance
lization,
control function
out the production
priority dispatching
material
"real-time"
as controlling
the storage
and valuation func-
location
of materials.
page 14
PERSPECTIVE
It
also often supports priority allocation
ducts
or orders,
and aids in
filling
of material
to pro-
order requisitions.
Procedure
Planning
Many companies
These functions have always been performed.
For example, separate
are organized according to these areas.
dedicated groups of people are often given the responsibility of
controlling
inventory,
planning
master
schedules,
etc.
The advent of the computer age brought software products
that mirror almost exactly the functional
above.
It
is
tional
area.
framework outlined
possible to buy software that addresses each funcIn
fact, the software
is
usually modularized
so that
the system may be acquired piecemeal.
Whether or not a manufacturing
company has software that
and control, the following
helps perform
production planning
procedure
very simplified form) is
(in
STEP 1: Forecast
is determined.
STEP 2:
usually used.
- A forecast of future production requirements
Master Schedule Planning
cast, current inventory,
- Using the production fore-
and other data, the master schedule
planning function makes a rough estimate of short and long term
demand on resources.
versus
demand,
Depending
a largely
upon available
manual
procedure
resource capacity
is
employed
to
make adjustments
to insure a feasible
STEP 3.
Requirements Planning and Capacity Planning
Material
master production schedule.
-
This function takes the master schedule for finished product and
estimated manufacturing lead times, and then determines an "order
release" date for each of all materials and components that
comprise the finished product.
At this step, a more detailed
page 15
PERSPECTIVE
analysis of resource
supply and demand is
performed.
STEP 4: Order Release - The Order Release function initiates the
production plan, as determined by the MRP function.
STEP 5:
Shop Floor Control - Once the material
has reached the
floor, the Shop Floor Control function takes over and insures
that the production schedule is met.
Materials are requisitioned, jobs are assigned and dispatched, and data is collected.
STEP 6:
Inventory Control - Coincident with the Shop Floor Control function, inventory is controlled in order to store material
and fill
requests
3.1.2 Recent
Finite
efficiently.
Trends in Production
Capacity
Material
Control
Requirements
Planning
Traditional MRP has offered little more than a computerized
method of keeping voluminous records on material, and the resulting resource, requirements.
There has never been an attempt, in
any but the most superficial
way, to account for the actual
resource
capacity in production planning and control.
It has
always been handled in an iterative, ad hoc, manual fashion.
The
manual approach is often a frustrating and impossible task.
There is
growing
interest in devising
models that integrate actual resource
better factory level
capacity with production
requirements.
In fact, one or two products that claim this capability have come onto the market within the past few years.
Products that attempt to perform finite capacity planning
often meet mixed reviews because ther treatment cannot be comprehensive.
A model formulation and its associated optimization
procedure
can be specified in a relatively
straightforward
way,
page 16
PERSPECTIVE
but solving
the problem with finite
impossible.
Practical approaches
computational
must reduce
king, what often turn out to be, limiting
or Kanban
The Just-in-Time
turing control is
a Japanese
The objective
above.
and sub-assemblies.
pliers
deliver
just
Just-in-Time
of this recent trend in material
at just the
the right items,
philosophy.
actually installed
inventories
control implementation
A kanban is
is
sup-
right place,
at
for forcing
a
a job ticket that accompa-
the assembly process.
in an assembly
variable
of material
may be met.
When the part is
or subassembly, the kanban is
sent back to its source to trigger the production
The control
control is
By requiring that external and internal
a particular
nies a part through
approach to manufac-
refinement to the approach discussed
just the right time, this objective
Kanban is
the problem by ma-
Approach
the need for large, expensive
to reduce
is
assumptions.
(JIT), or Kanban (Kb),
The Just-in-Time
resources
of a new part.
the number of kanban tickets in the
system.
The high risks of interrupted production due to low inventories are somewhat mitigated by imposing
pline on all
scheduling
facets of manufacturing.
producing,
Maintenance
to reduce the need for
inspections.
elaborate,
and
and inventory-
Also, very good predictability
suppliers that participate
of
procedures
Outside suppliers must insure high quali-
portation times and strong communication
benefits
of disci-
must be tightened up, lest the flow of parts that are
needed downstream stop.
ty in order
a great degree
this discipline
of trans-
ties are required of
in a JIT program.
The
long term
can lead to productivity
increases
page 17
PERSPECTIVE
beyond the simple reduction of inventory carrying costs
berger,
This point is
1982; Hall, 1983).
elaborated
(Schon-
in Section
4.4.
JIT usually results in smaller and more fre-
Implementing
of material.
quent deliveries
the still
This can exacerbate
necessary task of inventory management.
Although zero inventories
goal, it
to the extent that
is an appealing
should be moderated
required to achieve
costs
it
increase.
The JIT philosophy for production control
where production requirements
and where
effects
buffering
of
process
are
is
most applicable
known and fixed far in advance,
is not required to smooth the unavoidable
time
variations.
This last point is
illus-
with FMS's, job shops, or
trated by the material flow associated
any other system where a variety of parts with wide variations in
process times share the same resources.
failures,
buffers
are required
Even without machine
to reap the maximum production.
such as the
The JIT approach works best in applications
assembly
process
for products
tors, automobiles,
The
etc.).
with
uncertainties
tions are not high enough to require
order to achieve
predictable
sales
(refrigera-
in these
intermediate
applica-
buffering
in
the maximum production rate possible.
3.2 Cell Level Control
3.2.1
Traditional
There have
the activities
to determine
planning
Approach
been very few successful
in a cell.
scheduling
problems.
Simulation
strategies,
However, it
is
approaches
one that is
floor layout,
is expensive
to scheduling
widely used
and for
other
in both human and
page 18
PERSPECTIVE
computer
time since
simulations, to be credible,
complex and require a great deal of data.
Many simulation
the decision
are required to make a decision;
parameter
optimal, or at least satisfactory,
"tuned" until
tend to be
runs
must be
is
behavior
found.
in Cell Design and Control
3.2.2 Recent Developments
Recent developments
process are beginning
of the manufacturing
"flexibility"
concept
the traditional
One
in automation and new constraints on the
of
direction
Flowlines,
of a cell and how it
Cellular
or
called
development,
Manuf acturing,
in a single cell.
This is
is
and greater
Technology
was
manufactured from
intended
of product flow and scheduling,
less inventory,
to be controlled.
Cells,
stimulated
A family of products with very
by reports of Japanese successes.
similar operation sequences
Group
is
to alter
start to finish
to lead to a simplification
of operations,
tighter coupling
worker coordination.
A second, stimulated by advances in automation and control
technology,
described
is
below.
the
Flexible Manufacturing
A good overview
cepts can be found in Black
Flexible
Manufacturing
System,
of cellular manufacturing
(1983) or Schonberger
System
which
is
con-
(1983).
Control
A modern example of a cell is a flexible manufacturing
system (FMS) which consists of several
storage elements, connected
system.
ters.
It
machines
and associated
by an automated materials handling
is controlled by a computer or a network of compu-
The purpose
configuration
of the flexibility
is to meet production
and versatility
of
the
targets for a variety of part
page 19
PERSPECTIVE
types in the face of disruptions
machine
such as demand variations and
failures.
In an FMS, individual
of two factors:
part processing
is
practical
because
system; and the
the automated transportation
setup or changeover time (the time required to change a machine
which is small in
from doing one operation to doing another),
The combination of these
with operation times.
comparison
tures enables
redistribute
the FMS to rapidly
different parts.
fea-
its capacity among
Thus, a properly scheduled FMS can cope effec-
tively with a variety
of dynamically
changing
situations.
The size of these systems range from approximately
5 to more
than 25 machines.
They are also specifically designed for the
concurrent processing of a number of different parts (5 - 10
unique parts-types
variety
of
is
not unusual), each of which may require
(milling,
processing
A Flexible Manufacturing
The operational
cell whose main
that must be made include:
decisions
that the following,
may be met:
(and
often conflicting,
sub-objectives
are evenly
balanced among
Workload requirements
Machine
other
such
tools) to machines
the machines and material handling
*
etc.).
master production schedule.
Allocation of operations
v
boring,
System is a simple
to meet a predefined
objective is
*
drilling,
system.
failures have a minimum effect on
machines'
·
Work-in-process
*
Processing
work availability.
requirements
redundancy
are
(duplicate
minimized.
tooling)
is
page 20
PERSPECTIVE
maximized.
Re-allocation
*
machines
of operations
fail such that, in
tives listed above,
*
and tools to machines
Real-time allocation
tool
when
addition to those objec-
changing
of resources
effort is
minimized.
for processing
piece-
parts such that:
*
Workload requirements
are evenly
balanced among
the machines and material handling
v
Quality of processed parts
The first two areas are generally
is
system.
maximized.
not well addressed
by the
vendor community and, in fact, often cause the users major operational problems when trying to run an FMS.
Because of the diffi-
culty in juggling the conflicting
under sometimes
severe
constraints
machine
(limits on the number of tool pockets per
and on the weight the tool chain may bear), it is
difficult
recent
objectives
to manually
strides have
capability
The real-time
to resources.
been taken in solving
is not yet widespread
into the operating
addressed
allocate processing
very
Some
this problem, but the
and has not yet been integrated
software that controls FMS's.
scheduling
of parts to machines, however, is
directly by the vendors
that supply
Each vendor usually takes a unique
problem (this
is motivated,
each vendor's
design) and, because
edge, is
often reluctant
tation.
Nevertheless,
approach
"turn-key" FMS's.
to the scheduling
in part, by the unique aspects of
to divulge
of a perceived
the
after analyzing
proprietary
details of its implemen-
the behavior
many FMS's
page 21
PERSPECTIVE
over a period of time, we can make the following
The decision
the
classes,
activity in an FMS are listed below.
a detailed schedule
construct
however,
the complexity
possible
decision
source
choices
availability)
that
of
are needed.
these
questions
v
related
In practice,
the fact.
of FMS control
taken by the practioners
scheduling.
Decisions
and constraints
to dispatch
scheduling
into
FMS
are
as
made
they
considered when making
The criteria
Part Sequence
and re-
in material
this.
Very little information is
decisions.
In principle, one can
and uncertainty
prevents
dispatch
before
for scheduling
of the problem (the large number of
The general approach
is
or control variables,
observations:
-
for a variety
of
are:
Since
an FMS
can
pro-
cess a number of different parts and since these parts
are
required
active
v
in certain ratios
control
Sequencing
of
relative
to one another,
of the part input sequence is
Fixturings
-
Many
parts
required.
must make
a
number of passes through the system in order to process
different sides.
The sequence for these separate passes
could be chosen to enhance the performance
of the
system.
l
the
system,
part must often visit a number of different
machines
Sequencing
before
of
Operations
-
processing is complete.
Once
in
a
The sequence of these
separate machine visits could be chosen to enhance the
performance
of the system.
page 22
PERSPECTIVE
v
Machine
Choice
-
Of ten
operation
a particular
may
be performed at more than one machine.
When this is
true, a choice must must be made among
the posibili-
ties.
+
Cart Choice
a
Many FMS's employ
-
number
of
sepa-
rate carts for transporting parts from machine to maWhen the need arises for transporting
chine.
a part, a
choice among the carts of the system must be made.
*
moving except
cart movement: Carts are always
while undergoing
unload operation
load/
queuing at an occupied node.
chosen when there is
checked for
a
a destination.
Backlogs
are
of parts are
The closest cart with the correct
chosen for loading parts.
pallet is
Deadlocks
computed and
Intervals are
parts are introduced.
is
routes are
periodically.
requests for carts:
tracked.
Shortest
or while
pallet
The closest empty
chosen for the each part coming
off a
shuttle.
*
Operation
Check
-
and Frequency
FMS's
Many
Selection
being
built
are
a Coordinate Measuring Machine (CMM).
this
machine
is
Quality
for
equipped
with
The purpose of
to monitor the quality of
being processed as well as the processes
the parts
themselves.
Through the measurement of part dimensions, the nature
PERSPECTIVE
page 23
of process errors
fixture
the
(tool
misalignment,
CMM resource
of operations
is
quality standards
are
misalignment,
etc.) may be inferred.
limited, the
to measure
to measure them is
errors
wear, machine
Because
intelligent selection
and the frequency
with which
required in order to insure that
are satisfied
quickly
and that processing
identified.
3.3 Machine Level Control
The
bottom tier
of the manufacturing
of individual work stations,
even lone workers
Control
at the
machine
or other
logistical
that have
rather
for instrumenting
include
material
considerations.
These
is-
at this level are sometimes more in
been traditionally
and modern control
continuous,
or
for at the cell and factory level.
The problems encountered
classical
comprised
be actual machinery
level does not really
been accounted
line with those
is
(as is the case with manual assembly systems).
flow, scheduling,
sues have
which may
structure
framework.
treated within the
The domain is often
than discrete, and there
is
often opportunity
the machinery for full automatic
control and
feedback.
3.3.1
The Traditional
In
Approach
the beginning, there
and feedback was accomplished
were just hand tools.
through eye-hand
All control
coordination.
This
continued to be the case, for the most part, up until recently
(1950's).
and more
computers
was
The
tools
complex,
were
the result.
(lathes,
drill
but the principle
presses,
etc.)
remained the
applied
and Numerically
Controlled
Here
the position, feed,
became
same.
(NC)
and speed
of
larger
Then
machinery
the tool
PERSPECTIVE
page 24
relative to the part is
techniques.
In
controlled
through standard
addition, the different operations
feedback
a part required
could be programmed to occur automatically on one machine in the
proper sequence.
Operation
sequencing
has not been sufficient
changing
is generally performed open-loop;
reason to alter the sequence.
in some environments
there
This is
where there is full automation.
It
may happen that a tool breaks part way through a "tape segment".
If the part has to leave the machine and come back for any reason
(quality
control
check,
cult to pick up where
extract
broken
the processing
tool,
etc.),
it
is
diffi-
left off.
3.3.2 Recent Developments
Until
recently,
the position
of the tool, and its feed rate
and speed relative to the part has been controlled in an entirely
open-loop manner.
tools, anomolies
variables
Regardless of what was happening
in
casting
would remain constant.
monitoring
the power requirements
tion of the casting/tooling
adjustments
and quality, etc.), these
This is
beginning
to change.
can be determined
and
vision is another means
by which feedback
is
for the presence
at the machine level.
or absence
for measuring
By
of a particular cut, the condi-
combination
being used in control
employed.
of
made.
Electronic
techniques
dimensions
(wearing
These systems check
of tools in the spindle.
Other
the wear on these tools are also being
page 25
PERSPECTIVE
4.0 Survey and Critique
of Recent Research
This section reviews recent developments
optimization of manufacturing systems.
the outset
that there is
traditional
approaches
a large
in analysis and
We should emphasize at
body of literature
to manufacturing.
of Production and Operations Management
available
on
For example, the area
(POM)
occupies a signifi-
cant place in most business schools, and many textbooks exist for
this well-developed
area (e.g.
restrict
to the
ourselves
problems,
Tersine, 1980).
systems
could
significantly
In addition,
proposed
luative
aspects
of
manufacturing
to areas relevant to the framework as developed in
Section 2, and to recent developments
believe
Here we will
in
affect the progress
we find it
Suri
(1984a),
techniques
or
in these areas
useful
to adopt
between
models.
A
of
set of decisions
An evaluative
and predicts
generative
generative
system under those decisions.
tive, and descriptive,
are
technique
(evaluates)
and
as
eva-
technique
is
and generates
is one which takes
the performance
a
of a
(The terms prescriptive or norma-
also used for these
Separation of these two categories
means to clarify existing
the field.
the distinction,
one which takes a set of criteria and constraints,
a set of decisions.
which we
two categories.)
is useful not just as a
research, but also from a practitio-
ner's point of view, since solutions based on one or the other
type of technique have quite different behavior
actual manufacturing
found in Suri
(1984a).
systems.
when applied to
Details on this latter
point can be
We recognize that not all techniques
fit
easily into these two broad categories: some may be a hybrid of
both, others may truly fall in between the two; examples of all
PERSPECTIVE
of these
page 26
follow.
4.1 Early Research
Early research
in manufacturing
management science
and operations research
this was directed at production
(Planning
quirements
ling
involves
over
systems
determining
the
detailed
the aggregate
planning often refers to decisions
refers
to low-level
In particular,
techniques
with the mathematical
(e.g.
Dzielinski
zation
often
problems
resource
while
these
re-
scheduresources
Thus
high in the hierarchy
and
decisions.)
problem of fitting
of a large
number
and Gomory,
1965).
are
and planning
very
difficult
together
the produc-
Such combinatorial
in
the sense
amount of computer
machine failures
was concerned
of discrete, distinct parts
more, they are limited to deterministic
including
of
problems.
time period at hand.
made
scheduling
require an impractical
effects,
Much of
a great deal of the work on generative
for production
tion requirements
periods,
allocation
to particular tasks for the immediate
scheduling
literature.
planning and scheduling
a set of future time
determines
can be found in the
optimi-
that they
time.
problems
Further-
so that random
and demand uncertainties,
cannot be analyzed.
In order
to deal with
alternative
approaches
extensive
investigation
complex
have
these
of
large problems.
difficulties,
been explored.
of
heuristics
systems under realistic
development
practical
hierarchical
An excellent
The first involves
for
conditions.
approaches
two
use
in
scheduling
The second involves
to
solving
review of production
these
scheduling
page 27
PERSPECTIVE
both exact and heuristic methods, can be found in
approaches,
Graves
(1981).
Typical hierarchical
Hax and Meal
(1975),
and Graves
approaches
are described in
(1982).
The early work on evaluative
models was mainly an attempt to
represent the random nature of the production process by using
models.
queueing-theoretic
Most industrial
engineers
are taught the basics of single server queueing
and M/G/1 queues
the classic M/M/1
nately, however,
that is
theory, such as
Unfortu-
1975).
(Kleinrock,
most industrial engineering
where
courses stop, and IEs today often have the impression
models
(IEs) today
of queueing
simple, and impractical.
as esoteric,
What is less well known in the manufacturing community is
that the applicability
of queueing
theory to manufacturing
was
enhanced by the development of network-of-queues
considerably
theory by Jackson (1963), Gordon and Newell (1967), coupled with
algorithms
the more recent development of efficient computational
and good approximation methods.
for reasonable
manufacturing
"first-cut"
The state-of-the-art
evaluative
models of
today allows
fairly complex
systems.
Another early development in the area of evaluative
was the use of computer-based
simulation methods, which employ a
"Monte Carlo" approach to system evaluation.
accessability
simulation
this area
of computing
With the growing
power, the development
of easy-to-use
packages, and the advancement of simulation
has made
area "close"
models
major strides forward recently.
It
theory,
is
also an
to control theory in many ways.
In the following, we describe
time-scale hierarchy
recent research using the
of Section 2, rather than the framework of
page 28
PERSPECTIVE
practical
methods of Section 3.
from the control
amenable
The former is more appealing
theorist's point of view, and perhaps more
to rigorous
development.
4.2 Long Term Decisions
In this section we consider decisions
that involve consi-
derable investment in plant, equipment or new manufacturing
thods.
me-
Typically, such decisions may take over a year to imple-
ment, and may have an operational
lifetime of five to twenty
years during which they are expected to pay back.
Generative
Techniques
Traditional
term decisions
approaches
systems-based approaches for generating long
include the production planning and hierarchical
mentioned
casting, decision
above, as well as strategic
planning, fore-
analysis, and location analysis.
We
do not
deal with these here, but an overview and literature survey can
be found in Suri (1984b).
In the context of automated manufacturing,
programming
of equipment
techniques
(LP
and IP)
have been applied to selection
and of production strategies
Stecke, 1983; Whitney and Suri, 1984).
(e.g.
programming
be very complex.
(1984) has proposed
sions,
which
is
a new
frame-work
Graves,
1982:
However, the constraints
involved in the mathematical
Whitney
mathematical
for
problem formulations
sequential deci-
developing
algorithms for solving these complex optimization
can
heuristic
problems.
It
has been successfully applied to the problem of selecting parts
and equipment for manufacturing
Some recent approaches,
in a vary large organization.
which should be of interest to the
page 29
PERSPECTIVE
community, use dynamic
control
The decision
to invest in alternative manufactu-
(and equipment),
over a period of time, is formu-
decision-making.
ring
strategies
problem (Burstein and Talbi, 1985;
lated as an optimal control
1985: Kulatilaka,
Gaimon,
insight to help
decision-makers
models offer
Such
1985).
However, practical
qualitative
faced with the complex
alternatives that modern manufacturing
investment
volve.
investment models for long term
application
set of
systems in-
of these models
and use
remains to be seen.
Another
set of recent models
tie in long term decision-making
use control-theoretic
ideas to
with shorter term decisions.
These are dealt with in the next section.
Techniques
Evaluative
The evaluation of long term effects of a decision on an
enterprise
is
difficult problem,
a particularly
and evaluative
models for long term planning deal primarily with strategic and
accounting
issues
Kulatilaka,
1985).
(Hutchinson and Holland, 1982: Jaikumar,
Strategic
issues
1984:
involve such questions
as
how improved product quality or response time to orders will
affect
the market
trade off current
streams.
expenditures
However,
that evaluative
issues are currently
the
issues require models to
with future
(uncertain)
revenue
Neither of these areas is of primary interest to the
current audience.
first is
Accounting
share.
(relative)
we should mention two factors.
models for strategic
undergoing
failure of U.S.
radical
The
and accounting
changes,
industry to make
in the face
of
prudent invest-
PERSPECTIVE
page 30
ments (Abbott and Ring, 1983: Leung and Tanchoco,
1983).
The second important point, which is often missed by those
undertaking
decisions
modelling/analysis
are influenced
accounting
issues.
studies, is that the long term
to a large extent by these strategic
Even though we do not cover them here, it
important for analysts working
Many modelling
efforts fail to be useful to the manufacturing
they focus on minor technical
overall insight that is
and analysis
community because
points and do not provide the
needed
for this stage of the planning
Professor Milton Smith (of
Texas Technological
versity) said at the recent First ORSA/TIMS
ible Manufacturing
is
in this general area not to lose
sight of the forest for the trees.
process.
and
Conference
Uni-
on Flex-
Systems that around a hundred man-years had
been expended on solving minimum makespan scheduling problem, but
he did not know of a single company that used minimum makespan to
schedule
their shop floor operations!
4.3 Medium Term Decisions
Here we are concerned with a time period ranging anywhere
from a day to
primarily
a year, and the scope of the decisions involves
tradeoffs
between different modes of operation,
but
with only minor investments in new equipment/resources.
Traditionally, such decisions have been the domain of master
scheduling, MRP, and inventory management systems, which are reviewed in Section 3.
uncertainty
in a direct manner,
through the use of
or other quantities.
deterministic
These systems generally do not account
but rather,
"safety" values, whether
Master scheduling
plan, which gets
in an indirect
for
way
in stocks, lead times
and MRP systems work to a
updated periodically
(say once a
page 31
PERSPECTIVE
week, or once a month).
Inventory
uncertainty
theory
models
to derive optimal
and analyzes
the
stock policies.
effects
of
Inventory stocking
policies assume that each item stocked has an exogenous demand,
modelled by some stochastic
stocking
process, and attempt to find the best
policy for each item.
The fact that the demand
the master scheduling
is
and MRP decisions
clear that much useful information
being
thrown away.
manufacturing
entire
Of course, it is
system that makes it
problem simultaneously.
able structures
the size and complexity of a
Nevertheless
to solve the
we feel
that suit-
Some attempts
more
in this direc-
Techniques
decision-making.
cept of time
generative
Control
scale
planning
techniques
theorists are
decomposition
should therefore readily
problem
for decision making is
in this section.
We begin by reviewing
production
ignored, and thus it
very difficult
these components.
tion are described
Generative
is
can be developed to make the decision-making
coherent across
of
on inventory comes as a result of
familiar with the con-
and hierarchical
understand
(See references
above.).
It
The
are many: in addition to computational
approach
requires
less detailed data, and it
structure
(Meal,
and
the
shor-
imposes con-
advantages of the hierarchical
approach
organizational
partitions
with successively
The solution of each subproblem
straints on lower subproblems.
control,
the idea behind hierarchical
into a hierarchy of subproblems,
ter time scales.
for this level
1984).
savings, this
mimics
the actual
page 32
PERSPECTIVE
The
original
structure
on
theorists
should
by
ideas
intuitive and
find it
the use of an
liers, that the
hierarchy,
based
derations,
is
derived
by
the
planning
manufacturing
explicitly
in
the
the
around
is
failure
the
be replaced
By
is
difficulty
state
using
assume
over
of
derived
from
but
the
of
solving
by a static
(or each capacity
set of policies
re-solving
ment what Bellman
this
termed
failures
as
to
a
is
primarily
hierarchical
heuristic
arguments
interaction
of the
in
of
problem.
based
This
in
periodically,
feedback"
on
the
To get
optimization
the time
to do
procedure
between
system
condition).
an "open loop
randomly
the solution.
a stochastic
problem
includes
an intractable
approaches
to what
uncertainty
(hence
leads
average
developments
uncertainty
programming
that the dynamics
methods.
of time, and then
This
proposed
alternative
feasibility consi-
Recent
to approximate
(1980)
as a natural
An
to represent
the new
showed,
that demand and capaci-
a period
usually
control
multip-
multiplier
periodically.
on a mathematical
propose
"open loop"
state.
this
propose
and Suri
problem.
formulation.
considerations,
based
lem, they
states
problem
Since
the hierarchy
tractability
levels
(1981)
approaches
the contribution
Hildebrant
(1982)
but rather
but also equipment
ways that they
where
Suri
problem
capacity).
problem,
that Graves
systems have sought
not just demand,
varying
However,
could be derived
optimality
known and deterministic
re-solve
the hierarchical
formulation and Lagrange
hierarchy
not on
based
arguments.
a primal optimization
These hierarchical
in
heuristic
appropriate
of
approach
interesting
Hax-Meal
decomposition
ty are
for this
prob-
between failure
spent in
leads
each
to an
each failure
they
can
policy.
impleThe
PERSPECTIVE
page
technique showed reasonable
for automated
improvement
manufacturing
(Hildebrant,
over existing
33
heuristics
1980).
Kimemia (1982) and Kimemia and Gershwin (1983) have derived
an alternative,
proach
sues
closed loop solution to this problem.
has also
(the
backlogs
been to separate
response
to
machine
and surpluses)
dispatching.
the relatively
failures
from the
short
and
Their ap-
long term isto
production
term problem
of part
The long term problem accounts for the dynamics be-
tween failure states, as well as that due to demand, in more
detail
than the above approach.
The
mic
long
term problem
programming
given
problem.
the control
objective
is
to
production and demand
be within a capacity
tional machines.
as
vector of
process.
and there
minimize
modeled
The
by a discrete Markov
tor is
is
a continous
machine
states
The production
a specified
demand
the
cumulative
difference
set that is
determined
is
rate vec-
is
and the production rate is
dyna-
rate.
The
between
constrained
to
by the set of opera-
A feedback control law, which determines the
next part to be loaded
of current machine
and when it
should be loaded as a function
state and current
production surplus, is
sought.
This
machine
formulation,
failures,
Suri (1980),
exactly.
which reflects
had previously
this involves
approximation
disruptive
proposed
but they had concluded that it
The contribution
find a good
been
the
was too hard to solve
exact solution.
two steps in a dynamic
of
by Olsder and
of Kimemia and Gershwin
to the
nature
has been
to
Essentially,
programming framework:
page 34
PERSPECTIVE
the top level
separating
(the solution to
problem
a Bellman
into a number of subproblems, obtained formally through
equation)
a constraint-relaxation
procedure,
value function for each subproblem
principle
and then approximating
by a quadratic.
the middle level of the
is
to a sub-optimal
control u.
times
The maximum
which gives rise
hierarchy,
this case, the maximum principle
problem.
is a linear programming
dispatch
In
the
Then, at the lower level, one
to attempt
chooses
part
rate u.
Simulation experiments indicate
to achieve
this
flow
that these procedures
give rise to quite good policies in the face of the various
uncertainties.
More recent work by Gershwin, Akella, and Choong (1984)
Akella,
Gershwin, and Choong
computational
(1984)
has further simplified the
Simulation results
effort.
system is
vior of a manufacturing
and
indicate
that the beha-
highly insensitive
to errors in
the cost-to-go function, so the Bellman equation can be replaced
by a far simpler procedure.
simplifies
the on-line
Evaluative
The quadratic approximation
linear program.
Models
Evaluative models for this decision-making
approaches
both analytic
the ability to represent
chine failures)
intractable.
Koenigsberg
and simulation.
(1959).
(1967, 1971, 1976)
that give
features
(such
work in this
surveyed
by
were made by Buzacott
who looked at various approximate
insight into these
off
again analytically
field is
Notable contributions
are
as ma-
buffer stocks, in order to trade
For large systems, this is
The earliest
level involve
Important
production uncertainties
and limited
between the two.
models
further
issues.
analytic
page 35
PERSPECTIVE
Most of these analytic studies are based on Markov models of
transfer
lines and other production
systems.
An appreciation
for the difficulty of the problem is seen from the fact that the
largest general
model for which an exact solution
is available
involves three machines with two buffers between them (Gershwin
and Schick, 1983).
of a technique
for decomposing a production line into a set of
(Gershwin, 19831.
one-buffer subsystems
two-machine
for solving this system is analogous
point boundary value problems.
the method is
problems
cycle
is
results indicate that
Numerical
very accurate, and what is
times
however,
The procedure
to the idea of solving two-
more, fairly large
(20 machines) can be solved in reasonable
technique
has come out
A promising recent development
currently restricted
to the
time.
case where
Altiok (1984)
of the machines are identical.
recently developed methods for systems with more general
type
time
processing
Other evaluative
the
has
phase-
distributions.
queueing
techniques include
and simulation.
Both of these methods
decision-making
as well.
models are
The
However,
network models,
can be used for short term
we feel
that queueing network
best suited to more aggregated decision making, while
simulation is more suited
to detailed decisions.
Therefore
we
discuss the former here and the latter in the next subsection,
although the particular application
may suggest the use of
the other technique for either of these
A fairly
recent
manufacturing
network
development
systems has
models
in
been
for system
levels.
(analytic)
the growing
planning
one or
evaluative
models
of
use of queueing
and operation.
A simple-
page 36
PERSPECTIVE
minded,
static, capacity
account
the system
model
allocation
does not take into
and uncertainties
dynamics, interactions,
inherent in manufacturing
systems.
Queueing
these features,
able to incorporate
tions, and thus enable
with some restric-
albeit
more refined
network models are
evaluation of decisions
for
The increased use of such models stems
manufacturing systems.
algorithms
primarily
from the advances
made in the computational
available
to solve queueing
networks, both exactly and approxima-
tely.
(1980)
Solberg
tractable.
made the solution of these systems
algorithm (1973)
Buzen's
FMS, and Stecke
(1981)
applied this to capacity
for solving production
used it
for
planning
developed a number of approximate
Shanthikumar (1979)
problems.
planning
queuing models for manufacturing.
The development of the mean
technique for solving these
networks
(Reiser
value analysis
(MVA)
and Lavenberg)
opened up a host of new extensions and approxima-
Various approximate MVA algorithms have
tions.
(Schweitzer, 1979: Bard, 1979)
solution
which enable
of very large networks.
Hildebrant (1980)
fast and accurate
Hildebrant
applied MVA techniques
been developed
and Suri (1980).
and
to both design and real-
time operation problems in FMS.
An extension (Suri and Hildebrant. 1984)
solution of
systems with machine-groups,
enabled efficient
and has been incorpo-
rated in at least one on-line decision support system for an FMS
(Suri and Whitney, 1984).
(Shalev-Oren,
features
et al., 1984)
Another recent extension, called PMVA
allows
a wide variety of operational
to be modelled.
An important
reason for the increasing
popularity of
such
page
PERSPECTIVE
models in manufacturing
is that they have proved
in the area of computer/communication
of giving
reasonable
performance
have given a theoretical
network models
The disadvantages
Recent results
basis to the robustness
of queueing
situations
of queueing
(Suri,
represent
space.
(Some recent
steady-state
modelling
way, and they
certain other features, such as limited buffer
and Suri and Diehl
output measures
1983).
network models are that they
model many aspects of the system in an aggregate
fail to
their usefulness
systems modelling, in terms
predictions.
for use in practical
37
developments,
(1983)
e.g.
do allow
Buzacott and Yao (1982)
limited buffer sizes.)
they produce are average
operation of the system.
transient effects
The
values, based on a
Thus they are not good for
due to infrequent
but severe disrup-
tions such as machine failures.
However,
the models tend to give reasonable
system performance,
require
estimates of
and they are very efficient: that is,
relatively little input data, and do not use
ter time.
A typical
FMS model
(Suri
they
much compu-
and Hildebrant,
1984)
might
require 20 to 40 items of data to be input, and run in I to 10
seconds on a microcomputer,
bers for simulation.
to quickly arrive
can
then refine
in contrast to the much larger num-
Thus these
at preliminary
these
models
can be used interactively
decisions.
More detailed models
decisions.
Queueing network models suggest themselves for use in the
middle level
models
of a hierarchy.
Development
along with suitable control
and higher levels
of the hierarchy,
of queuing
aspects to tie
network
in the lower
could be a useful topic
of
page 38
PERSPECTIVE
research.
Lasserre
(1978) and Lasserre
and Roubellat
the medium term production planning
program of special
technique
(1980) represent
planning problem as a linear
structure, and develop an efficient solution
for it.
4.4 Short Term Decisions
Decisions at this level
typically have a time
a few minutes up to about a month.
have included those
exact approaches
Traditional
frame of from
generative
models
for lot sizing and scheduling, using both
as well as heuristics or rules.
a number of recent interesting
developments
There have been
in this area, which
are now described.
Traditional
lot sizing models traded
off the cost of setting
up a machine with the cost of holding inventory, on an individual
product basis.
The Japanese
(Just-In-Time and Kanban) approaches
have challenged these concepts as being
narrow-minded and myopic
in terms of the long term goals of the organization.
cate operation with minimal
or no inventory,
not only saves inventory carrying
They advo-
claiming
that this
costs, but also gives rise to a
learning process which leads to more balanced production in the
long run ISchonberger,
This thesis is
try as well.
becoming more widely accepted
However,
and inefficiencies
1982; Hall, 1983).
reduced inventory
in the short term.
It
ask what is the optimal rate of reducing
in U.S.
indus-
leads to line stoppages
is
therefore
logical
to
inventory, so that short
term losses are traded off against long term gains due to increased learning.
of this point
(Suri
There has been some preliminary
and DeTreville, 1984).
It
is
investigation
a problem
that
PERSPECTIVE
page 39
would fit naturally into an optimal control framework, and further investigation
would be useful.
There have also been some recent studies indicating
that lot
sizing in a multi-item environment ought to be treated as a
vector optimization
problem.
each product affects
The
idea is that
the production
primarily through the queueing
rate of other products,
of each lot of parts waiting for
other lots to be done at each machine.
consider
This integer programming
computationally
tem.
Therefore, one ought to
the joint problem of simultaneously
lot sizes.
intractable
the lot size of
optimizing
problem would normally be
for any realistic
manufacturing
However, by modelling the system as a queueing
then solving a resulting nonlinear program some
have
been obtained
development
that needs
Hitz (1979,
duling
(Karmarkar
further
et al., 1984).
recent results
This is
a promising
deterministic
sche-
systems:
In these systems, parts follow a common
path from machine to machine.
He found that
appropriately,
a periodic sequence
times.
network and
class of flexible manufacturing
flexible flow shops.
sys-
exploration.
1980) studied the detailed,
of a special
all the
he could design
This substantially
reduced
by grouping parts
of loading
the combinatorial
optimization
problem.
Erschler, Roubellat,
nistic, combinatorial
class
of
optimal
and Thomas
scheduling
decisions.
technique
Rather
part to send into the system next, it
of candidate
choices.
(1981)
describe
a determi-
that searches for a
than
deciding
which
presents to the user a set
This flexibility is
intended
as a response
PERSPECTIVE
page 40
to the random events such as machine
to represent
failures
that are
difficult
explicitly in a scheduling model.
Perhaps the most widely used evaluative tool for manufacturing
this
systems
today
is
simulation.
context refers specifically
simulation.
The
term
"simulation"
to computer-based
discrete event
Such a model mimics the detailed operation of the
manufacturing
system, through a computer program which effective-
ly steps through each event that would occur in the system
be more precise.
simulation models can be made very accurate
is the programming
time to generate
detailed
time the model is
analyst tries
(or to
each event that we wish to model).
In principle,
the price
in
run.
time to create the model,
--
the input
data sets, and the computer time each
In addition, the more phenomena that the
to represent, the more complex
the code, and the
more likely there are errors, some of which may never be found.
In addition, it
is
simulations is
limited by the judgment and skill of the program-
mer.
Detail
sometimes forgotten that the accuracy of a
and complexity are not necessarily
accuracy, if major classes of phenomena
synomous with
are left out.
(While
simulation can be used at any of the levels of the decisionmaking
process, we choose to describe it
here since it
mine the most detailed operation of a manufacturing
can exa-
system.)
Two reasons for the recent popularity of simulation are the
number of software tools that have been developed to make simulation more accessible
in
computing costs
factors make it
lation before
to manufacturing
designers; and the decrease
and the availability of microcomputers.
well worth an organization's
making large investments.
In
These
effort to use simuaddition, there have
page 41
PERSPECTIVE
been many developments
in the design and analysis of simulation
experiments, which have contributed
tion as
a valid and scientific
Recent developments
categorized
to the acceptance
methodology
development
output graphics).
in this field.
in software tools for simulation can
into simulation languages,
active model
of simula-
"canned packages",
(or graphical
(or
features will
not be discussed further here, but good examples are SIMAN
input)
inter-
input), and animation
The two input/output graphics
be
(for
and SEE WHY (for output).
Although simulation languages
have been around for a while,
the last five years have seen the development
languages,
such as GPSS/H, SIMSCRIPT II.5, and SLAM II, as well
as the development
facturing
user
now available
of languages specially tailored
(e.g.
SIMAN and MAP/I).
on microcomputers
UI.5, SIMAN, MICRONET).
the manufacturing
packages,
of many powerful
Also, most languages
as well (e.g.
are
GPSS/PC, SIMSCRIPT
Another development,
designer, has
to the manu-
specially geared to
been the development
of canned
which do not require programming skills, but are com-
pletely data driven (e.g.
GCMS,
GFMS, SPEED).
have a number of structural assumptions
of how the manufacturing
very quick analysis
of
they
built into them, in terms
system operates, but can be useful for
a system.
trum, for very detailed simulation
to a programming
Of course,
At the other end of the specit
may be necessary
to resort
language such as FORTRAN or PASCAL.
Clearly then, there are tradeoffs
(1982)
involved among these op-
tions.
See Bevans
for a discussion.
Even
though simula-
tion is
perhaps the most widely used computer-based
performance
PERSPECTIVE
page 42
evaluation tool for manufacturing
systems, we would recommend
greater use of analytic and queueing
conducting
the more expensive
to the numbers
network
models
simulation studies
--
prior to
in comparison
quoted for queueing network models, a simulation
model might require
100 to 1000 data items and 15 to 10,000
seconds to run on a microcomputer.
In the area of simulation design and analysis there have
been several developments
control
community.
tion, detection
control
The analysis of simulation outputs -identification,
parameter
involves
--
spectral
that should be of interest to the
bias
of
has used many techniques from time series analysis and
methods
(see Law,
1983, for a survey).
mization in simulations involves stochastic
niques
1984),
(e.g.
Meketon, 1983;
Parameter opti-
approximation
Ho and Cao, 1983;
tech-
Suri and Zazanis,
which again are familiar ground to our community.
A recent development,
crete
and run length
transients,
and initial
genera-
interval
confidence
which
event systems,
meters in simulations
technique
is
enables
very efficient
(see Ho et al., 1984
related to linearization
again has parallels
Cassandras,
called perturbation
with conventional
1982; Ho, 1985).
analysis
optimization
of para-
for a survey).
This
of dynamic
dynamic
Essentially
of dis-
it
systems,
and
systems
(Ho and
enables
the gra-
dient vector of system output with respect to a number of parameters to be estimated
sense it
is
an evaluative
not only evaluates
improving
by observing
only one sample path.
and "semi-generative"
decisions
but also suggests
In this
tool, since it
directions
for
the decisions.
While much of the original
work on perturbation analysis
PERSPECTIVE
page 43
relied on experimental
results
to demonstrate
its
accuracy
Ho et al., 1979 and 1983), recent analyses have given it
rigorous
it
is
foundation
(Suri, 1983a and 1983b),
probabilistically
Cao, 1983:
Suri and
correct
for certain
Zazanis,
better than repeated
systems
(Cao, 1984;
a more
and also proved that
1984; Cao, 1985),
simulation
(e.g.
(e.g.
as
Ho and
well as
Zazanis
and Suri,
1984).
Another recent interesting
development
tion of Petri net theory to the performance
turing
systems
(Dubois and Stecke,
use of Petri nets (in computer
titive
questions
as: will
1983).
science) was to answer such quali-
there be any deadlocks?
of
in the
However,
there
theory of timed
nets.
Following
al.
analysis of manufacIn the past, the main
have been some important recent advances
Petri
has been the applica-
[11983,
some work by Cunningham-Greene
1984)
have developed
production processes.
complex
set of
e.g.
performance
control-theoretical
transfer
functions,
main disadvantages
with completely
evaluative,
it
It
controllability,
to some
situations,
However, it
events systems,
observability.
has currently are that it
not generative.
answers
also gives rise to a parallel
concepts for discrete
determinstic
Cohen et
a linear system theoretic view
This enables efficient
questions.
(1982),
can only deal
and that it
is
The
is
only
a promising new
development.
One
that of
level.
of the most useful areas that require more research is
real time control
Little
theoretical
of manufacturing
research
systems, at a detailed
has been done on this, apart
PERSPECTIVE
page 44
from the large body
that exist
of heuristics
A few researchers
(Graves, 1981).
formal
way (Buzacott,
Akella,
Choong,
have treated the issues in a
1982: Gershwin,
and Gershwin,
for scheduling
1984;
Akella,
and Choong,
Cassandras, 1985).
1984:
This
seems to be an area for control theorists to apply their experti se.
Indeed, Ho et al. (1984)
Dynamic
have coined
System,
the term DEDS, for
Discrete
Event
to emphasize
that
manu-
facturing
systems are a class of dynamic systems, and that there
are concepts from dynamic system theory that need to be developed
or applied for DEDS as well.
analyzed
either by purely
chains, queues)
In the past we have seen DEDS
probabilistic approaches
or by purely deterministic
and other combinatorial
methods).
well as the perturbation
(e.g. Markov
approaches
(scheduling
The work by Cohen et al.
analysis approach, have shown the
as
use of
a dynamic systems view of the world.
As an example, Suri and Zazanis
tion analysis
ly optimize
combined with stochastic
while a facility
is
have used perturba-
approximation
to adaptive-
This could be used, for example,
a queuing system.
for improving the choice
parts,
(1984)
of lot sizes for a number
operating
--
of different
the approach
is
to implement and has obvious applications in real systems.
ever, many interesting
questions
be answered for this adaptive
of convergence,
method.
etc.
simple
How-
remain to
page 45
PERSPECTIVE
5.0 Conclusion
We have described a framework for many of the important
problems in manufacturing
people
systems that need the attentions of
trained in control and systems theory.
existing
practical
fall short.
methods solve those problems, and where
they
We have also shown how recent and on-going research
fits into that framework.
been to encourage
analysis
We have shown how
efforts
very important
An important goal
of this effort has
control theorists to make the modeling and
that will lead to substantial
field.
progress
in this
page 46
PERSPECTIVE
REFERENCES
Abbott,
R. A.
Effects
on
and E.
A.
Ring
(1983,
"The
MAPI Method
Productivity: An Alternative
is
--
Needed,
Its
" 2, 1,
1983,
15 -30.
Akella, R., Y. Choong,
Hierarchical
and S. B. Gershwin
Production
on Components,
Scheduling
(1984),
Policy,
" IEEE
and Manufacturing
Hybrids,
"Performance
of
Transactions
Technology,
Sep-
tember, 1984.
Altiok, T., "Approximate
with Blocking,
" Eur.
Analysis of Exponential
J.
Bard, Y., "Some Extensions
sis,
" in
Performance
Oper.
Res.
11,
390-398,
Queueing
to Multiclass
of
Tandem Queues
Systems,
Computer
1982.
Network Analy(M.
Arato,
ed.
North Holland, Amsterdam, 1979.
Bevans,
J.
P., "First, Choose
Machinist,
pages
Black, J.
"An Overview
T.,
Comparison
Vol.
15, No.
to
143-145,
an FMS Simulator,
May
1982.
of Cellular Mnaufacturing
Conventional
Systems,
11, November, 1983,
Talbi,
C. and M.
Introduction
of Flexible Manufacturing
"Economic
a Dynamics
Operations
Research,
Buzacott, J.
A.,
" Industrial
Systems and
Engineering,
pp. 36-40.
Burstein, M.
Procedures Versus
" American
Justification
Technology:
Based Approach",
to
appear,
"Automatic
Transfer
for the
Traditional
Annals
of
1985.
Lines with Buffer Stocks",
),
page 47
PERSPECTIVE
Int.
J.
Prod.
5,
Res.
Systems,
"
1 8 3 - 2 0 0.
of Inventory Banks in Flow-Line Pro-
Buzacott, J. A., "The Role
duction
1 9 6 7,
3.
J.
Int.
Prod.
Res.
Vol.
9,
No.
4.,
pp.
1971.
425-436,
Buzacott, J.
Limited
A., "The Production Capacity of Job Shops
Storage
1976, pages
Space,
"
Int.
J.
Prod.
Res.
,
with
14,
Vol.
No.
5,
597-605.
Buzacott, J. A., "Optimal Operating Rules for Automated Manufacturing
Systems,
trol
AC-27,
Vol.
Buzacott, J.
Flexible
on Automatic
" IEEE Transactions
1,
No.
pages
80-86,
A. and Shanthikumar,
Manufacturing
J.
Systems,
February
G., "Models
"
AIIE
Trans.
Con-
1982.
for Understanding
,
pages
339-
350, December 1980.
Buzacott,
J.
A.
tems! A Review
and Yao, D. D. W.,
"Flexible Manufacturing
of Models, "Working Paper No. 7, Department
IndustrialEngineering,
University
Buzen,
J. P.,
"Computational
works
with Exponential
of
Toronto, March, 1982.
of
Algorithms
Servers,
Sys-
"
for Closed Queueing
C.
ACM, Vol.
16,
Net-
#9,
September 1973, 527-531.
Cassandras,
rial
C., "A Hierarchical
Handling
appear,
1985.
Systems",
Routing Control
Annals
of
Scheme for Mate-
Operations
Research,
to
page 48
PERSPECTIVE
Cao, X. R., "Convergence
of Parameter Sensitivity Estimates
Stochastic
Proc.
trol,
Experiment",
Dec.
Cohen,
Conf.
to
Function Analysis of Queuing
Operations
View
IEEE
and
Conf.
Research,
1985.
Dec.
of Discrete-Event
Control,
G., D. Moller,
J. P. Quadrat and M.
Theory
For Discrete
Event Systems",
Control,
December
Cunninghame-Green,
Approximating
Vol.
Erschler,
Decision
J.,
R. A.,
F.
June,
Draper
and
dans L'Ordancement
Toulouse,
Lab.
Viot,
Proc.
"Linear System
23rd IEEE
Conf.
Dec.
Behaviour",
Processes
Op. Res.
and
Quar-
1982.
Roubellat,
Scientifiques
22nd
1983.
"Describing Industrial
95-100,
Journ6es
Proc.
1984.
Their Steady-State
13,
"A Linear-
Processes",
December
Cohen,
terly,
and Con-
Networks,"
G., D. Dubois, J. P. Quadrat and M. Viot,
System-Theoretic
and
Decision
1984.
Cao, X. R., "Sample
submitted
IEEE
in a
et
V.
Thomas
d'Atelier
(1981)
en Temps
Techniques
"Aide
R6el,
Automatis6e,
a
" 3e
Manufacturing
Systems
Handbook,
The Charles Stark Draper Laboratory, Cambridge, Massachusetts,
1982.
me
ADEPA,
1981.
The Flexible
la
page 49
PERSPECTIVE
Dubois, D. and K. E. Stecke,
duction Processes",
Proc.
"Using Petri Nets
22nd IEEE
Conf.
to Represent Pro-
Dec.
and Control,
December 1983.
Dzielinski,
Sizes,
Vol.
B. P. and Gomory, R.
Inventory
11, No.
and
Labor
9, 874-890,
E., "Optimal Programming
Allocations",
Manag.
of Lot
Sci.
July 1965.
Gaimon, C., "The Dynamic, Optimal Mix of Labor and Automation",
Annals
of
Gershwin,
Operations
S.
Research,
to
appear,
1985.
B., "An Efficient Decomposition Method for the
Approximate Evaluation of Production Lines with Finite Storage
Space,
" Massachusetts
Institute
of Technology Laboratory for
Information and Decision Systems Report LIDS-R-1309, December,
1983.
Gershwin,
S. B., R. Akella,
and Y. C. Choong,
"Short Term Produc-
tion Scheduling of an Automated Manufacturing
chusetts
Institute of Technology
Facility,
" Massa-
Laboratory for Information and
Decision Systems Report LIDS-FR-1356,
February,
1984.
Gordon, W. J. and G. F. Newell, "Closed Queueing Systems with
Exponential
Servers",
Op.
Res.
15,
No.
2,
April
1967,
254 -265.
Graves,
S. C.,
"A Review of
Production
Scheduling,
" Ope-
pp.
page 50
PERSPECTIVE
rations
Research,
Vol.
29,
No.
4,
1981,
pages
Graves, S. C., "Using
Lagrangean
Techniques
Production
Problems,
" Management
28, No.
Planning
to Solve Hierarchical
Procedure
for Manufacturing
IEEE
t e r s ,
Hall,
(ed.
R.
International
W.,
Zero
Programming
System Design and Evaluation, "
Conference
Inventories,
C. and Meal, H.
Planning
),
Vol.
on Circuits
and Compu-
1 9 8 0.
Hax, A.
tion
Science,
3, March 1982, pages 260-275.
Graves, S. C. and Lamar, B. W., "A Mathematical
Proc.
646-675.
and
C.,
"Hierarchical
Scheduling,
North Holland,
Dow-Jones
"
in
Irwin,
1983.
of Produc-
Integration
Logistics,
M.
A.
Geisler,
1975.
Heidelberger, P. and P. D. Welch, "A Spectral Method for Confidence Interval
C.
ACM
24,
Hildebrant,
Machines
Generation and Run Length Control
4,
233-245,
April
R. R., "Scheduling
are Prone
xible Manufacturing
sium
on Large
1981.
Flexible Machining
Systems when
to Failure", Ph. D. Thesis, MIT,
Hildebrant, R. R. and Suri, R.,
Algorithm Structure
in Simulations",
"Methodology
for Scheduling
Systems,
Engineering
and Multi-Level
and Real-Time
" Proc.
Systems,
1980.
Control
of Fle-
3rd International
Sympo-
Memorial
University
of
page 51
PERSPECTIVE
Newfoundland, July 1980, pages 239-244.
Hitz, K. L. (1979),
Laboratory
for Information
879, January,
Laboratory
Ho, Y. C., "A
"Scheduling
of Flexible Flow Shops--II,
" MIT
and Decision Systems Report LIDS-R-
1980.
Survey of the Perturbation Analysis of Discrete
Dynamic
Systems,
"
Annals
of
Operations
Research,
to
1985.
Ho, Y. C.
of
and Decision Systems Report LIDS-R-
for Information
1049, October,
appear,
Shops, " MIT
1979.
Hitz, K. L. (1980),
Event
"Scheduling of Flexible Flow
and X. R.
Queuing
Cao,
Networks,
"Perturbation
JOTA,
Vol.
Analysis
40, No.
4,
and Optimization
1983,
pp.
559-
582.
Ho, Y. C.
and C. Cassandras,
Discrete
trol,
Event
Dec.
Systems",
"Computing
Proc. 19th
Determine
Parameter
Science,
Vol.
Conf.
Dec.
and Con-
29,
Chien, "A New Approach to
Sensitivies of
No.
6,
1983,
Ho, Y. C., M. A. Eyler, and T. T.
Buffer Storage
International
IEEE
for
1980.
Ho, Y. C., M. A. Eyler, and T. T.
for General
Costate Variables
Journal
6, 1979, pp. 557-580.
of
Design
Transfer
pp.
Chien,
Lines,
" Management
700-714.
"A Gradient
Technique
in a Serial Production Line,"
Production
Research,
Vol.
17,
No.
PERSPECTIVE
Ho,
page 52
Suri, R.,
Y. C.,
Cao,
X. R., Diehl,
W., Dille,
G.
Form)
Queueing
Networks Using Perturbation
Scale
Systems,
to
Hutchinson, G.
Flexible
appear,
Volume 1, No. 2, pages
"
Journal
J.
R., "Jobshop-like
10,
1,
October 1963,
of
Manufacturing
Systems,
Systems", Manag.
Sci.,
Systems: A Managerial
Per-
Queueing
131-142.
spective", Harvard Business School, working
U. S., Kekre, S. and Kekre,
Item Multi-Machine
Large
R., "The Economic Value of
Jaikumar, R., "Flexible Manufacturing
Karmarkar,
Analysis",
215-228, 1982.
Jackson,
No.
and
1984.
K. and Holland, J.
Automation,
W.
(Non-Product-
of Large Multiclass
Zazanis, M. A., "Optimization
J.
paper, 1984.
S., "Lotsizing
Job Shops", Univ. of Rochester,
in Multi-
Grad. School
of Mgt., Working Paper QM8402, March 1984.
Kimemia,
J. G. (1982),
Flexible Manufacturing
"Hierarchical
Control
Systems, Massachusetts
of Production in
Institute of Tech-
nology Laboratory for Information and Decision Systems Report
LIDS-TH-1215.
Kimemia,
J. G. and S. B. Gershwin
Computer Control
tems,
"
IEE
pp. 353-362.
(1983),
"An Algorithm for the
of Production in Flexible Manufacturing
Transactions,
Volume
15,
No.
4,
Sys-
December,
1983,
page
PERSPECTIVE
in
Investments
Annals
FMSs",
1975.
Wiley,
Support System to Evaluate
N., "A Managerial Decision
Kulatilaka,
appear,
John
Systems,
Queueing
L.,
Kleinrock,
53
to
Research,
of Operations
1985.
Research,
Operations
6,
31, No.
Vol.
Output Data, "
of Simulation
Law, A. M., "Statistical Analysis
983-
1983,
December,
102 9.
J.
Lasserre,
de
"Etude
(1978),
B.
la
Planification
Moyen
a
Terme d'une Unite de Fabrication, " thesis presented to Universite
Sabatier
Paul
Lasserre,
for
degree
the
J. B. and F. Roubellat
Docteur
of
(1980),
"Une
Resolution de Certaines Programmes Lineaires
lier,
" RAIRO
191, May,
Operations
Research,
Volume
a Structure en Esca14,
No.
2,
pp
171-
1980.
on Productivity Analysis
Manuf.
,
Sys.
Vol.
-2,
An Alternative
No.
2,
Business
Review,
Vol.
62,
175-188,
No.
Decision Based
to the MAPI Method",
1983.
Meal, H. C., "Putting Production Decisions Where
Harvard
de
Methode Rapide
Leung, L. C. and J. M. A. Tanchoco, "Replacement
J.
Ingenieur.
They Belong,
2, March,
1984,
102-
111.
Meketon, M.
S.,
"Optimization
in Simulation:
A Tutorial",
pre-
page 54
PERSPECTIVE
sented at Winter Simulation Conference, WSC83, December
t1sder,
G. J.
and Suri, R., "Time-Optimal
Manufacturing
IEEE
Conf.
Dec.
Operations
No.
Systems
with Failure
and
Research,
Control,
Special
Issue
Multichain
Networks,"
Schonberger,
Press,
R.
19th
1980.
on
Simulation,
Vol.
31,
J.,
S. S., "Mean
J.
ACM
Japanese
27,
Value Analysis
pages
313-323,
Manufacturing
of Closed
April
1980.
Techniques,
Free
1982.
Schonberger,
R.
Just-In-Time
J.,
"Integration
Production,
11, November, 1983,
Schweitzer, P.
"
of Cellular Manufacturing
Industrial
Engineering,
Stochastic
Seidman,
J., "Approximate
Analysis
Control and Optimization,
A. and Schweitzer,
a FMS Cell,
Working
of Multiclass
Vol.
15,
P.
Paper
J.,
Amsterdam,
Real-Time
QM8217,
On-Line
Graduate
S., A. Seidman and P.
Schweitzer,
Control
School
Shalev-Oren,
Systems with Priority
on
1979.
University of Rochester, New York, 1982.
J.
Closed
Conference
Management,
Flexible Manufacturing
and
pp. 66-71.
Networks of Queues," Presented at International
of
Proc.
6, 1983.
Reiser, M. and Lavenberg,
No.
Control of Flexible
Prone Machines",
Dec.
1983.
"Analysis
of
of
Scheduling: PMVA",
page 55
PERSPECTIVE
Annals
of
Research,
Operations
Shanthikumar,
to
J.
Solberg,
J.,
CAN-Q
School of Industrial
IN,
User's
Guide,
Purdue
Engineering,
facturing
Ph. D.
Systems,
Engineering,
Production Problems
Sci.
R.,
No.
9
(Revised),
University, W.
Lafa-
School
W. Lafayette,
IN,
29, No.
Industrial
of
1981.
and Solution of Nonlinear
for Flexible Manufacturing
Integer
Systems",
3,
273-288,
March
Management
Concepts
for Large
, Vol.
Resource
for Flexible Manu-
Problems
Dissertation,
Purdue University,
Stecke, K. E., "Formulation
Suri,
Report
1980.
Stecke, K. E., Production Planning
Manag.
Engineering, Uni-
Industrial
Toronto, 1979.
versity of
yette,
1985.
Queueing Models of Dynamic Job
J. G., "Approximate
" Ph. D. Thesis, Department of
Shops,
appear,
1983.
Systems,
Pergamon Press, 1981.
Suri, R.,
ACM,
"Robustness
Vol.
30, No.
of
3,
Suri, R., "Implementation
Carlo
1983b.
Experiment",
J.
Queueing
564-594,
of
Network
July
Formulae,
J.
1983a.
Sensitivity Calculations
Optim.
"
Theory Applic.
on a Monte
40, 4,
August,
PERSPECTIVE
Suri, R.,
Dynamic
and
page 56
"Infinitesimal
Systems,
Control,
Perturbation Analysis
A General
Dec.
Theory",
Proc.
22nd IEEE
pear,
Event
Conf.
Dec.
1983c.
Suri, R., "An Overview of Evaluative Models
facturing
of Discrete
Systems",
Annals
of
for Flexible Manu-
Operations
Research,
to
ap-
1985.
Suri, R., "Quantitative
Techniques
Chapter
of
in
Handbook
for Robotic
Industrial
Suri, R. and DeTreville, S.,
"Getting
Systems
Robotics,
Wiley
from Just-In-Case
Analysis",
1984b.
to Just-
In-Time: Insights from a Simple Model", Harvard Business School,
Division of Research
Suri, R. and Diehl,
Working Paper, September,
G.
W., "A Variable
Use in Analyzing Manufacturing
ber,
1983,
Surf,
R. and Hildebrant,
Systems
3, No.
submitted
Using
1, 27-38,
Mean
to
Buffer Size Model and Its
Networks
with Blocking", Septem-
Management
R. R.,
Value
1984.
Science.
"Modelling
Analysis,
"
Flexible
J.
Mfg.
Manufacturing
Systems.
,
1984.
Suri, R. and C. K. Whitney,
"Decision Support Requirements in
Flexible
Journal
No.
1,
Manufacturing",
61-69,
Vol.
1984.
of
Manuf.
Sys.
, Vol.
3,
page 57
PERSPECTIVE
Suri, R.
and Zazanis, M. A., "Perturbation Analysis
Gives Strong-
ly Consistent Estimates for the M/G/L Queue", submitted to
Management
Suri, R.
Science,
and Zazanis,
in Manufacturing
Sept.
1984.
M. A., "Adaptive
Simulations
Using Perturbation
king paper, Harvard University,
Tersine,
R.
J.,
Production
Optimization
of Lot Sizes
Analysis",
wor-
1984.
and
Operations
Management,
North
Holland 1980.
Whitney,
C. K.,
submitted
Whitney,
Selection
tions
to
"Control Principles
J.
Manufacturing
in
Flexible
Systems,
Manufacturing,"
1984.
C. K. and R. Suri, "Decision Aids for Part and Machine
in Flexible Manufacturing
Research,
to
appear,
Yao, D. D. W.
and Buzacott,
Manufacturing
Systems
J.
Systems",
Annals
of Opera-
1985.
A., "Modeling
a Class
with Reversible Routing,
of Flexible
" Tech. Rep. 8302,
Dept. of IE, Univ. of Toronto, August 1983.
Zazanis, M. A. and R. Suri, "Comparison
with Conventional
tic
Systems,"
Sensitivity
submitted
to
of Perturbation Analysis
Estimates for Regenerative
Operations
Research,
Stochas-
1985.
Customers )
Forecasting
~Orderorders
OrdrOrders
Delivery
Dates
Processing
,tMasteria
Available
Capacity
Scheduling
Availability
Schecdr
......... ..
Manufacturing
Capacity
Requirements
Planning
....
Resource
Planning
Sd.
.........
inventory
Floor
Shop
Control
s hedu/e
PFloorolhManufacturing
Acrx
.eeds
Management
i....
:.:
:
Inventory
HaiMntengance
Product
Figure 1 Traditional Production Control Framework
Customers )
(Forecasting
Oraders
al,/very
Order
Processing
Dates
Available
Capacity
terial
_
Availability
Master
Scheduling
::::.:..:. .......
::::
............-.-.-. .
... '"""""":-:'
........
.....-:.....
::- :::::.-
Manufacturing
Resource
Planning
Capacity
Requirements ..
Planning
Needs
::...:::. " ".::
....
fl.rder
R!elease
Release
.. Purchasing
:I. nventory
Shop Floor
Management
schiedu/e
<
jI
::::i.
. /
Manufacturing
Services
_ ,
V.ee.i..'
AInual
Prdo
Schedule
Results
Control
*...
.. . . . . . . . . . .ii.
IProductionl
I
Too/s
MJaintenance
ProdUct
OCSS
M
r
Figure 1 Traditional Production Control Framework