NAT L INST. OF STAND & TECH R.I.C.

A111D4 ObEbflD

NATIONAL MroASCS

Research Information Center

Gaithersburg, MD 20898

NISTIR 88-3872

A 11 IDE ^7DUfl

A Functional Approach to

Designing Architectures for

Computer Integrated

Manufacturing

Wayne J. Davis

Professor of General Engineering

University of Illinois Urbana, Illinois 61801

Albert T. Jones

U.S. DEPARTMENT OF COMMERCE

National Institute of Standards and Technology

(Formerly National Bureau of Standards)

Center for Manufacturing Engineering

Gaithersburg, MD 20899

October 1 988

Issued February 1989

-QC

100

. U 56

#88-3872

1989

c.2
NATIONAL afflg* OF.|rANDAI®S 4

*%Si3S&28‘
NISTIR 88-3872

L-

A Functional Approach to

Designing Architectures for

Computer Integrated

Manufacturing

Wayne J. Davis

Professor of General Engineering

University of Illinois Urbana, Illinois 61801

Albert T. Jones

U.S. DEPARTMENT OF COMMERCE

National Institute of Standards and Technology

(Formerly National Bureau of Standards)

Center for Manufacturing Engineering

Gaithersburg, MD 20899

October 1988

Issued February 1989

National Bureau of Standards became the

National Institute of Standards and Technology

on August 23, 1988, when the Omnibus Trade and

Competitiveness Act was signed. NIST retains

all NBS functions. Its new programs will encourage

improved use of technology by U.S. industry.

U.S. DEPARTMENT OF COMMERCE

Robert A. Mosbacher, Secretary

Ernest Ambler, Acting Under Secretary

for Technology

NATIONAL INSTITUTE OF STANDARDS

AND TECHNOLOGY

Raymond Go Kammer, Acting Director

A FUNCTIONAL APPROACH TO DESIGNING

ARCHITECTURES FOR

COMPUTER INTEGRATED MANUFACTURING

Wayne J. Davis

Professor of General Engineering

University of Illinois

Urbana, Illinois 61801

Albert T. Jones

Deputy Project Manager, AMRF

National Institute of Standards and Technology

Gaithersburg, Maryland 20899

ABSTRACT

Developing effective CIM architectures is hampered by automation

and integration problems. The key to resolving these problems

lies in a better understanding of each manufacturing function and

how it is related to other manufacturing functions. Our view is

that mathematical models can provide this understanding. This

paper presents the results of our initial efforts to develop such

models. They can be used to guide the development of the

technology needed for automation. They also specify the inputs,

outputs, and inter-relations needed for integration, regardless

of the specific CIM architecture used.
.
1

1.0 INTRODUCTION

Throughout history, organization has played a vital role in

the success of large manufacturing companies. It provides the

means for meeting the strategic goals of the company. Designing

an organizational structure to meet those goals involves [18] 1)

defining the major functions, 2) decomposing those functions into

a set of activities to be performed by individual employees, and

3) developing a managerial structure to coordinate both activities

and emp 1 oy e e s . Traditionally, hierarchical managerial- structures have been

used to ensure this coordination. However, the number of levels

and the responsibilities of the employees at each hierarchical

level vary from one company to another. In some companies, all

decisions are made at the top, and lower level employees simply

implement them. In others, there may be several levels of

decision-making. Employees make decisions based on input from

their superiors and exert the control necessary to have

subordinates execute those decisions. The former strategy is

typical of very small shops, while the latter is imperative in

large shops. Each strategy can be successful [18] in meeting the

company's needs.

Recent attempts to design and build managerial structures for

Computer Integrated Manufacturing (CIM) systems have been based on

similar concepts. CIM functions are defined, decomposed into

smaller s ub f unc t i ons , and "logically" grouped to be performed at

some level within the managerial structure. Almost all existing

CIM structures are hierarchical [26]. A major distinguishing

characteristic of these CIM hierarchies is that they do not

contain people. They are made up of computers and other automated

manufacturing equipment. The goal is to eventually have all

decision-making, control, and information processing functions

performed by these computers.

Currently, these CIM systems are part of an existing factory.

In most instances, all of the decisions related to the jobs they

do are still made by factory systems external to the CIM system.

This includes which jobs will be done, when they will be done, and

the data needed to do them. This implies that CIM hierarchies do

very few of the functions they were designed to do. Although it

is imperative to expand their capabilities to include more of

those functions, it is proving to be far more complex than

originally thought. Although there are many reasons for this, two

stand out: automation and integration.

Most attempts to automate human - in tens ive functions such as

process control, production scheduling, and process planning have

met with little success. Automation has been difficult because we

do not understand enough about the functions themselves or the way

human beings perform those functions. There is no consensus on
2

the best computer algorithm or procedure to use in place of a

human being executing a given function. Furthermore, recent

efforts to develop expert systems to address these problems have

also stalled. Until such algorithms, procedures, or expert

systems are developed, human beings will continue to play an

important role in these functions. In fact, Davis [14] has

illustrated that the GIM environment may actually enhance the

human participation in certain manufacturing functions.

Integrating these automated functions into an "effective" CIM

architecture is a multi-faceted problem. First, there is no

agreement on 1) the number of levels, 2) the mapping of functions

onto levels, and 3) whether functions can be distributed across

more than one level [26] . The commonly accepted number of levels

ranges from four to six. In some models material handling is

supervised at the cell level. In others, it is managed at the

shop level. Jackson and Jones [25] have even advocated that

planning and scheduling be done at every level.

Second, although hierarchical approaches are commonly used in

decentralized control theory [28,34], integrating automated

decision-making methodologies into such a structure has been

difficult. The best known paradigm, decomposition of mathematical

programming [19,27], assumes a static, deterministic, single

criterion problem. Manufacturing problems, on the other hand, are

dynamic, stochastic, and mul t i - c r i te r ia . In addition, there are

several different time scales associated with these functions

which impact the way they can be grouped into levels and the way

control feedback information can be used to update decisions at

each level

.

Third, the data needed to carry out these manufacturing

functions must be collected, verified, and disseminated using

advanced database management systems (DBMS) and sophisticated

network communications systems (NCS). There are still many

unresolved design and management problems associated with the NCS

and DBMS. Finally, since data plays such an integral role in this

automated environment, the NCS, and the DBMS must be integrated

with whatever decision-making and control structure is used to

form an "effective" CIM architecture.

1.1 STATEMENT OF THE PROBLEM

As noted above, automation and integration are intrinsically

bound together. Effective methodologies for automation cannot be

developed without realizing that the results must be integrated

into a working system. Effective integration schemes cannot be

developed without realizing what is being integrated together.

The key to resolving these issues lies in a better understanding

of each manufacturing function and how it is related to other

manufacturing functions. Once we have this understanding, we can
3

then address the questions involving the best architecture for a given manufacturing environment.

Our view is that mathematical models are one way to provide

this understanding. Developing detailed mathematical models for

all manufacturing functions is clearly beyond the scope of this

paper. We present the results of our initial efforts to develop

such models for a subset of the functions depicted in Figure 1

which in turn is a subset of the functions that would be

considered within a CIM implementation. We address what each

model does and how it is related to other models, but we impose no

other structure. That is, we have purposely done this work in an

archi tec ture - free environment. In section 2 we describe models

for a single process while section 3 presents models to capture

the interactions among individual processes. Section 4 contains

models of production planning decisions.

Even though we do not know how to solve all of these models,

we feel that their definition is a first step. The models can be

used to guide the development of the technology needed for

automation. They also specify the inputs, outputs, and

interrelations needed for integration, regardless of the specific

decision-making and control architecture used. Finally, we have

attempted to develop generalized formulations for each function

which can be specialized for given applications. Through this

approach, the consequences of each assumption pertaining to a

given application can be mathematically analyzed through its

modification upon the generalized formulation.

2.0 MODELING INDIVIDUAL PROCESSES

We assume that the manufacturing shop floor contains N

processes P n (n-l,...,N). These processes can be machining

centers, inspection centers, material handling devices, etc. We

also assume that these processes are used in the fabrication of M

distinct products pm (m-1, . .

. ,M) . Modeling these processes is

based on the concept of state transition functions.

2.1 State Transition Functions

The behavior of process P n while producing product p m can be

modeled by the state transition function g nm £ x nm

(

c )

»

u nm

(

c )

.

c ] where x nm (t) is the state of the process P n and unm (t) is the

controlling input into the process P n at time t. Using a sequence

of discrete sampling times l c 0

»

c 1

,

c 2

> • • •

)

where

tk - tQ + k At for k - 0,1,2, ... (1)

we can define the evolution of x nm (tjc

) via the recursive relation

x nm ( t k+l ) “ §nm t x nm (t k > » unm (t k ),t k ] (2)
•

-
>

0

O

X

o

LU

£LD

O

X

0

2

o

<

o

LJL 00

5

o

J—O

D

O

oX

AGGREGATE

PRODUCTION

PLANNING

02

O

X

DQ

0

LUo

o

X

0

X

X

0

2

O

l—

3

LUX

LUo

2

UJQ

LUo

LUXX

PRODUCTION

QUOTAS

DETAILED

PRODUCTION

PLANNING

0

x

LUQ

X

O

Q

LUo

o

X

Q

2

<

I

Ui

Q

Q

LUH

0

<

O

LUX

OX

MARKETING

ORDER TRACKING

ISSUED JOBS WITH

ASSOCIATED DUE DATES

PRODUCTION

SCHEDULER

PROCESS

LINEUP

PROCESS

PLANNING

L

DETAILED

__l PROCESSING

INSTRUCTIONS

J fc

PROCESS

COORDINATION

PROCESSING STATISTICS PROCESS

SPECIFICATIONS

PROCESS

CONTROLLER

Figure I -An Abridged Functional Schematicfor a Computer-Integrated-Manufacturing Environment

4

There are several properties of g nm [ • ] that ultimately govern

the complexity associated with determining the controls unm (tk)

and predicting the states Xnm^k)' The fitst is the functional

dependence on time, which implies that the functional form of

g nm [ • ] can vary over time. The second is the fact that this

variability is often stochastic in nature. In metal removal

processes, the amount of input energy needed to maintain a

constant cutting force can change drastically as the tool becomes

worn. Another familiar example is the rolling of hot steel slabs,

where the rolling process produces permanent plastic deformation

upon the rollers. In both of these examples, it is impossible to

predict g nm [ • 3 or the behavior of Pn with certainty. Consequently

this means that the best that one can hope to do is to specify the

prob{gnm [»] € G nm } where G nm is a known subset of potential

functions for g nm [ • ]

. Another important limitation in modeling manufacturing

processes today is the fact that x nm (t^) is seldom observed

directly. Despite the abundant research in this area, on-line

process monitoring and control techniques are still not available

for many processes. Until this happens, we will continue to

measure the output of the process rather than the process itself.

This output, Ynm^k^* can be defined by

Ynin ( t k'1 “ ^nm C x nm <t k ) .UnmCtk) .t k ] (3)

Since the calibration of a measuring device may change randomly

over time, the same potential problems arise in estimating h nm [ •

]

as in the estimation of g nm [»] equation (2), including the

determination of the prob(hnm [*j E H nm ). Furthermore, it is

rarely possible to measure all the processing outputs in realtime. As an example, consider a turning operation. In this case,

measurements such as turning speed and the location of the tool

holder can be made in real-time. However, as the tool wears the

depth of the cut changes. Therefore, measurements such as the

precise depth of the cut or the resulting surface roughness cannot

be made in real-time, and must await for the completion or the

interruption of the process for a precise measurement. Let T nm

and tQ denote the anticipated duration and initiation time for the

processing task; C nm

- tQ + T nm be the anticipated completion time;

and y nm (C nm ) the final measured output. Then the modeler may only

have a rough estimate, y nm( t k)» °f the* true value for y nm (tk) or

Ynm ( c k) ^nm^nm^ ) > unm^ ) r t ^ k ^ ( ^

)

for t < t^ < C nm where t is the last sampling time prior to t^,

y nm ( t *) is c he last measured output, and h nm [ • ] is the estimating

function. Letting Ynm represent a set of acceptable outputs-

usually a set of predefined to ler ances - - the best information the

modeler can hope to supply is the prob{ynm E Y nm | y nm (t*) and

u nm ( t *)3 where y nm represents the entire output stream arising

from the implementation of the processing task.
• iv.
5

It seems clear that using these functional relationships to

monitor and control manufacturing processes will not be possible

in the near future. The task of estimating prob[g nm [*] e G nm ),

prob {

h

nm [ • ] € H^} , and prob{ ynm € | y^t*) and u^t*)} will

also be difficult. In most cases, these estimates must be based

on the experiences gained from past implementations of each

particular process upon a given product. This, of course, leads

to significant problems in estimating g nm [ • ] and h nm [ • ] for a given process upon a new product.

2.2 Process Control

To account for uncertainties in the evolution of a process, a self-tuning controller can be used [1,2]

.

As depicted in Figure

2, a self-tuning controller has two basic elements: a system

identifier and a controller. The system identifier uses the

recent time history for unm[ c kl and Ynm^kl* to develop

approximations for the g nm [ • ] and h nm [*], denoted by g^m [ #

] and

hnml'l, respectively. These approximations are then used by the

controller to generate the next control input. Specifically, the

controller computes the difference between the measured (or

estimated) output y nm (tk) and the desired output y^m ( t k^* This

error is used in conjunction with the optimal control law

U* f Ynra^ c k) » supplied by the Process Coordinator (PC) (discussed

below) to determine the next control input for the process. Thus,

the process controller continuously attempts to minimize

deviations from the desired output trajectory specified by the PC.

For deterministic systems in which g nm [ • ] is known with

certainty, the system identification element is not needed. For

processes where no formulation of the state transition function

exists, the controller block would not exist. In this case, the

system identification element would attempt to evaluate available

system characteristics and a process coordinator would

subsequently define the initial process settings or control

parameters. After initiation, the process would evolve in open

loop fashion. As an example, consider the Basic Oxygen Furnace

process in s te e 1 - making . Here the charge of molten iron, scrap

steel, and alloy additives are introduced into the process. After

approximately thirty minutes the finished steel is produced with

little or no opportunity to sample the steel's chemistry during

the processing duration. As a consequence, there is only a 70

percent probability that the correct steel chemistry will be

achieved. Post processing techniques can often generate an

acceptable product, but this eventual outcome cannot be

guaranteed

.

Although limitations on process modeling exist, the selftuning controller is being adopted in industrial environments.

Astrom e_t a_l.. [2] provide an excellent overview of the topic as

well as several examples. Watanabe [36] recently reported the

development of a similar controller for a milling process.
. ' - - - ' ... - '
Figure 2=-Detailed Schematicfor Process Control

6

Papapanagio tou e_t a_l. [31] have performed work demonstrating the

applicability of self-tuning controllers to controlling the

rollers in a hot strip mill for steel-making. Blattner e_t al_. [9]

have developed a formulation for controlling the blast furnace in

steel-making. Their formulation includes not only parameter

estimation within the system identification element, but also a

simulation to predict the system response under various control

strategies

.

2.3 Process Coordination

Process Coordination (PC) determines the desired output

trajectory y^m

(t) for a given process and the optimal control law

U* £ 7nm

(

c k) » t k 1 to b® used in achieving that trajectory. Although

this law is written as a function of time, it is typically set at

the beginning of the processing and remains constant until some

major problem arises. The PC detects potential problems by

monitoring feedback from the process and its controller. Whenever

the PC determines that a new control law and/or desired output

trajectory is needed during the implementation of a processing

task, ideally it should also estimate the resulting probability

that the process will be able to implement that law correctly, and

thus, successfully complete the processing task.

To understand the relationship between the PC and the process

controller, consider the tool wear example from * Section 2.1.

First, the PC specifies an initial optimal control law,

U* t ynm ( tfc)

»

c k ] » which identifies the exact tool, speed, feed,

cutter paths, and cutting time to be used in the process. This

data is then used in negotiation with the Production Scheduler

(PS) to determine start and finish times. But, as the process

evolves in time, unanticipated changes in the feed, speed, or

cutting time may be required to account for unforeseen tool wear

or other changes in the processing environment. These changes,

which are a function of the integrated measurements of y nm (tk),

are dictated by the process controller's success in implementing

the desired trajectory y^m

(t) • These changes are typically minor

with respect to their consequences upon processing durations, but

nevertheless, updated completion times are reported to the

production scheduler. If the tool breaks, major modifications

could follow. First, the PC must determine if the job can be

salvaged. If yes, then the PC must specify a new optimal control

law with the associated probabilities for successfully

implementing the task. These decisions would be based on the last

measured value of ynm^k^* the desired output y^m

(t) , and the

current production schedule. Furthermore, operator intervention

as well as potential additional material handling may be required.

Upon reinitiation, a new completion time must be generated. It

can be concluded that the focus of the PC is the total

implementation of the processing whereas the process controller is

focusing the immediate restoration of the desired output

trajectory. We will now give two potential formulations for the
.

-'

.
7

determination of the optimal control law.

2.3.1 On-line Determination of the Control Law . We will begin

with the on-line generation of the control law U* [ y^ (

t

k ) , t k ] at

the implementation of process P n upon JOBj. From the process

controller, both gnmt*^ and ^nmt*] have been developed. We next

assume that from process planning an admissible set of control

laws has been specified which we will denote by T{ g^t • ] .h^J • ] ) . This set is determined off-line and the selected control law JU U [

y

nm

(

t

k ) * c k 1 must be contained within this set. For example,

g^m [ * ] and h^m [ • ] could be used to estimate the remaining toollife. Given this estimate, acceptable ranges for tool speed,

cutting depth, and metal removal rates could then be specified to

establish T{ g^f • ] ,hnm [ • ] } . The methods through which process

planning would make this specification will be discussed in the

next section. However, in Figure 1, we note that there is

feedback to process planning from the process coordinator who

monitors each implementation of the given process upon each job.

Another input from process planning is the minimum desirable

completion probability pr^n for the implementation of process P n

in the manufacturing of product pm which places a minimum bound

upon the prob{ynm (t k ) e Ynm } . Upon the implementation of the

processing task, the determination of the acceptability of y nm (t k ) is made by the process coordinator. However, the basis for this

determination is specified by the process planner. In most cases,

selecting U* [ y

d

( t k ) , t k ] from T{ g^ • ] ,hnm [ •]) should necessarily

guarantee this probability constraint is met. However, as a

coordinative input to the PC, the PS may increase this probability

on a given JOBj which we will denote as pr^j- 11

. Specifically, only

the PS can determine the consequences upon the near-term

production flow if a given implementation of a process fails. Two

additional inputs to the PC are presented by the PS:

Ej n

- the arrival time for JOBj at process P n , and

L

j

n

- the planned pickup time for JOBj at process Pn.

To assess the consequences upon the planned production flow, it is

assumed that the PS has a collection of L objective functions of

the form f ^ (Ej n

, Lj n , pr“j- n

) for i-l,...,L with an overall utility

function W [ f

1 (Ej n , Lj n , pr“j n

) , . . . , f

i (Ej n

, Lj n

,prgjn

) ]

. To permit

the PC to coordinate its decision with that of the PS, we will

assume that there are L' compatible objectives for the PC,

^ j n t xnm * unm 1 for with x nm and unm denoting state and

input stream occuring during the processing [tQ,tQ+T nm ] with tQ

being the planned initiation time and T nm being the planned

duration. Note that both tg and T nm will be variables, not

presepecifed constants, within the proposed optimization. An

utility function, u>j n { 5

j- n [ •] } will quantify the

tradeoffs among those objectives. A formal statement of the realtime optimal control problem can now be given.
8

W

j

^ *

* * * » ^jrJ * 3 ) (5)

the following constraints for t^-tQ ,

.

• * * t 0 + ^nm

^ . g I^ C^.) > t , ] nm k+1 nm nm k nm k k (6)

y ( t. ) “ h ' J [ x ( t. ) , u ( t. ) , t. ] nm k nm nm k nm k k

(7)

U

nm

(t

k

} “ U

nm ly nm

(t k ),£ k

3 (8)

U

nm ty nm

(t

k ),t k

1 e T{ SnnJ * 3 H (9)

prob{y

nm

€ Ynm } £ max{pr^n

, prgjn

) (10)

+ ^nm - ^j

n

(11)

c 0 - ^ j

n

(12)

A more detailed discussion of the decomposition procedure is

given by Davis and Jones [15]. Nevertheless, some particular

points merit discussion here. Note that both g^m [*] and h^m [ 6

] are explicit functions of t^ . That is, models for continued

process modification (or degradation) can be included within this

formulation. These models would be supplied from process

planning. However, the stochastic nature of the process evolution

makes a deterministic specification of g^m [«] and h^m [»]

impossible to achieve. To insure the feasibility of the

deterministic solution approach, conservative models for process

evolution might be employed. Stochastic optimization approaches

might also be adopted, but these approaches are complex and

computationally intensive, making them difficult to implement in

real-time. It can be concluded that the PS's decision-making must

always address the uncertainties that remain.

The decision defined in relations (5) through (12) indicate

two types of dynamic coordination that are to be employed within

the planned interaction of the PC with the PS. The decomposition

procedure uses a goal coordinative scheme by having the PS specify

E

j n , L

j n » and prTMj n which serve as the r i gh t - hand - s i de of the

process coordinator's constraint sets. • Also we note that

w in { 0jnM *VnM > is indexed upon both j and n. This implies

that a price directive coordination scheme can also be imposed to

permit the PS to modify the PC's utility function on a given JOBj.

To support this type of coordination, additional pricing

informations would be required as inputs from the PS to the PC.

Using sensitivity analysis approaches from mathematical

programming, the PC can study the effects of varying any cost or

right -hand- s ide coefficient. Similarly the PS, who makes the

specifications, can determine the consequences of its decision as
9

particular parameters are varied. These variational procedures

provide the basis of negotiation which can lead to the real-time

modification of E

j n , L

j n , P r nj

n or additional vaues specified

within the pricing inf oramat ion

.

Finally, the existence of relations (5) through (12)

highlight the shortcomings of assuming an invariant control law

for every implementation of a given process upon a given product.

First, it ignores the true time-varying nature of the process, and

second, it eliminates any potential benefits that can be derived

from the negotiation between the PS and the PC (see Davis [14]).

2.3.2 Off-line Determination of the Control Law. The off-line

determination of the control law represents a subfunction within

the larger function of process planning. Ideally, the entire

function of process planning should also be addressed. However,

space limitations will simply not permit a full discussion.

Rather the focus will be directed toward supporting the on-line

determination of the control law and serve as a transition to the

production flow considerations which will be addressed in the next

section

.

Process planning provides several important inputs toward the

on-line determination of the control law. The first input is the

models for the evolution of the state transition and output

functions, g nm [«] andh^t*], respectively. Certainly, analytical

modeling of the processes provides a useful input for developing

this input. However, the complexity of many manufacturing

processes limits this mathematical definition. Perhaps the

greatest source of information for generating these models would

be gained through prior experience with the processes. As

mentioned earlier, there is the potential for feedback from the PC

to process planning pertaining to each implementation of the

process . Although a manufacturing system may be faced with a variety

of products, the physical implementations of a given process are

typically composed of machine - leve 1 instructions which are

universal to all products. Hence, the process planning function

is to translate the engineering design for the considered product

into these machine - leve 1 instructions. The first stage of the

planning effort is to determine ordered sequence(s) of the

processes which are to be employed. From this step, the input and

output configuration for the given product at each processing

stage emerges.

Several issues must be considered in generating the

manufacturing sequence. The first consideration is the selected

process's capability in making the desired transformation. To

this end, not only must the physical processing limitations be

considered, but also the process's projected availability. Often

a given process's capability is not unique, and choices must be
10

made. In this case, the process's reliability in performing the

desired task and the associated cost are considerations. The

implications for material handling are also major concerns. This

fact leads to production flow consideration.

Given the input and output configuration for a given product

at each processing stage, the next step is to develop the

instruction set that will permit the process to complete the

transformation. Typically certain processing parameters will be

determined during this phase while others can remain undefined.

For example, in milling a flat surface, a choice of the cutting

tool and the number of machining passes could be determined. This

specification would generate the desired output trajectory that

the tool would attempt to follow. One could, however, reserve the

specification of the speed that the tool traverses this trajectory

as a variable. Using historical processing information, process

planning could develop acceptable values to be employed given the

current measurements of g^m[ * ] and h^m [*], thereby defining

TUW-l.hAmM)-

Process planning should also specify the means by which the

acceptability of a given process's implementation will be

determined. For example, bounds might be defined for the maximum

deviation between the measured and desired output trajectory. In

short, quality assurance is a critical component of process

planning. Ideally, it is desired that the determination of a successful implementation of a process will insure that the output

of that process will be an acceptable input to the subsequent

processing steps and that ultimately the product will perform as

designed. Two major obstacles remain to this realization: The

inability to accurately model the processes and the inability to

make accurate real-time measurements for the output of the

process. To date, off-line inspection is still a primary

mechanism for determining the acceptability of a process's output.

Using the set Tlg^J • ] .h^J • ] } and the desired output

trajectory y^m (tk) estimates of the processing duration and the

consumption of processing resources can also be made. These data

are essential to the production planning functions above the PC in

Figure 1. In addition, the precedence relations determining the

routing that a product will traverse the processes are also

essential to the production planning functions.

3.0 MODELING INTER- PROCESS INTERACTIONS

3 . 1 Combining Process Models

Thus far, the discussion has focused upon a single process.

The state of the entire shop floor system at time t^ is

represented by the composite state vector

{X

1 ,m(l) (tk) ' * ’ ’ X N,m(N) (tk))
11

where m(n) for n-1 ,

. . . ,N is the number of the product being

fabricated at process n and the state transition function for the

process P n

is given by g n m ( n )[ # ]- Similarly, the output from this

collection of processes is given by the composite output vector

{y l,m(lS tk) y N,m(N) (tk)

}

* We note that as soon as process P n completes one product and

begins another, these representations are no longer valid. For

each process we define a universal set of transition functions

S n “ {G n iM (13)

where <f> is the idle state which could result from planned or

unplanned events (i.e. the process breaks down). Assuming the

next JOBj arriving at process P n will request product pm , then

G nm [*] tke collection of potential process transition functions

from which the implemented g nm [ • 3 will be chosen by the PC. If

E

j

n and L

j n represent the anticipated start and finish times for

process P n to work on JOBj, then they also indicate the times

where the transition function for the manufacturing system may

change. These times can be derived from the current production

schedule (see section 3.3).

3.2 Production Flow

For each product pm , process planners must determine

potential routing sequences. A given sequence yields a set of

precedence relationships among processes and operations stating

the order in which processes will be applied in the fabrication of

the product. We can model this production flow using a process

transfer function, T m (n), which determines the successor process

to process P n for product p m

. We define n' - T m(n) provided

process n' immediately succeeds process n in the routing for

product p m

. The exact form of the transfer function and the

complexity of these process planning activities depend on the type

of manufacturing system.

In a pure flow shop, all jobs proceed through the processes

in exactly the same order. We can number the processes in a

manner such that T m

(n) * n+1 and T m

(N) - <f> for any m - 1, . . . ,M.

Here 4> indicates that there are no remaining processes in the

sequence. In this case, process selection and operation

assignment is trivial.

In the more general flow line, or multiple path flow shop, we

again number the processes such that T m

(n) * (n' u <jJ) for any m -

1, . . . ,M where n'> n. Whenever a process is required, it must

always be used after a fixed subset of the preceding (lower

numbered) processes. But, not all processes are employed in the

manufacture of a given product pm

. Once the product is identified,

process selection and operation assignment for JOBj is

straightforward.
12

In a job shop, there is usually no preferred path among the

processes. This implies that the range of successors for process

P n is contained within the complimentary set of processes lfc (n) -

(ji,l,...,N) - (n). Further specification of the successor set to

process P n

is often impossible. For the job shop, process

selection and operation assignment can be quite complex. For a given product pm , there will be a feasible set of processes which

tan be employed, each having a capacity constraint, which can be

used in performing a given operation. The collection of feasible

processes could lead to multiple routings which differ both in the

processes that they employ and the order in which the processes

are visited. The lack of ordering among the processes with the

potential for multiple routings for a given product pm

considerably complicates the production scheduling function. The

selection of the routing to be employed in the manufacturing of

JOB-: often depends upon the current state of the manufacturing

system and the queued jobs awaiting production. The lack of an

ordering of processes also complicates the material handling

constraints which again are a major component of the production

scheduling function.

In the "preferred path job shop" or flexible manufacturing

cell the goal is to partition the entire set of processes into

disjoint subsets with little or no interaction. This is done

using group technology [23] and significantly reduces the set of

potential successors to a given process P n

. The adoption of this

approach simplifies both the process selection subfunction and the

material handling concerns. Therefore, the production scheduling

problem is also simplified. However, the complexity of scheduling

function for the flexible manufacturing system typically remains

greater than that for the flow line configuration.

Routing information with estimated processing durations is

used by the production scheduling function to determine the start

and finish times for the operations at various processes.

3.3 Production Scheduling

Another important part of the int e r

- p r oc e s s coordination is

production scheduling (PS). A formal statement of the PS problem

is as follows: Assume that a set of jobs JOB.; ( j — 1 ,

. . . ,J) has

been issued to the PS with associated due dates Dj (j— 1, . .

. ,J) and

that JOB^ requires the production of a specific product p m

. Also,

assume that the processes P n (n*=l,...,N) are on-line. Then, if we

define

E

j

n (j“l,...,J; n-l,...,N) - earliest start (arrival) time

for P n upon JOBj »

Lj n (j— n«l,...,N) - latest finish (pick-up) time

for P n upon JOBj , and
13

P r j*n

n “ t ^ie m i n i- mum acceptable probability for completing the

processing task upon JOBj by process P n by L

j

n ,

the production scheduling problem is to maximize the utility

f unc t i on

Max W[f 1 (E 11 , . . . . >Ejn; l 11« . . . . .Lj N ;prTt n ,prg>j n

) , . . . , f L

(*)]

where f^(») for i-l,...,L are the criteria to be considered in the

optimization. These criteria may include minimizing tardiness,

maximizing production throughput and maximizing process

utilization among others. Several constraints can be considered

including due date, material handling, resource availability,

precedence relations, and alternate routings. The job list and

due dates are supplied by production planning. The remaining data

are derived from off-line production planning and from negotiation

with the PC

.

The presented production scheduling statement has included

the events which will insure the satisfaction of the above cited

constraints. The reader may note a particular focus toward the

events associated with material handling which is a major concern

of the PS. Indeed, it is these constraints that often lead to a

distributed scheduling function throughout the manufacturing

organization. For example, consider a manufacturing shop

consisting of several manufacturing cells. At the shop level,

there could be an automated guided vehicle system to move the

parts from one cell to another. Within a given cell, there could

be a dedicated robot for material handling to move the part among

various processes within the cell. Both material handling systems

would typically possess their own dedicated controllers and

coordinators. In this sense, they are processes. A distributed

scheduling approach would assume that there would be a scheduling

function at both the shop and cell levels. At the shop level, the

cell would be regarded as a process from a material handling point

of view. Through negotiation with the cell level scheduler, the

residence time for the job at the cell would be determined. To

implement this task, the cell level scheduler would consider the

material handling constraints within the cell and the durations

associated with the subprocesses that would occur within the cell.

On. the other hand, a centralized scheduling approach would

schedule all processing tasks considering the constraints imposed

by all material handling systems. The adopted approach will

likely depend upon the size of the manufacturing system and the

resulting number of material handling constraints that must be

cons ider ed

.

An exact mathematical representation of the objectives and

constraints for a given production scheduling problem is quite

complex. For a generic representation of the PS problem, and a
14

detailed formulation, the reader should consult [29,33]. For a

survey of mathematical programming approaches to solving the

production scheduling problem, the reader should consult [21,32].

For a summary of some of the recent work in this area the reader

is referred to [24].

Although it is usually assumed in the literature, the

production scheduler rarely knows the durations that will be used

in the various operations. Hence, the stated start and finish

times are only estimates of the true start and finish times. As

discussed earlier, the PC can often change the durations either to

speed up or slow down the process. This implies the potential for

negotiation between the production scheduler and the PC. Davis

and Jones [14,15] have recently developed a scheduling framework

which allows this type of negotiation.

4.0

MODELS FOR PRODUCTION PLANNING

A manufacturing system is driven by two stochastic inputs:

materials from vendors and demands from customers. These inputs

generate a stochastic output, namely finished goods. The overall

objective is to optimally track the input demand with the output

of finished products. In the preceding sections, we have

addressed models of the processing aspects of this problem. We

now discuss issues related to planning. We include planning

strategies, aggregate planning, and detailed planning.

4.1 Production Planning Strategies

Today, two complementary production planning strategies are

often discussed: push and pull.

4.1.1 Push . The push strategy attempts to push each order

through the system just before its due date occurs. The planning

begins with this due date and works backward to schedule the times

at which each required process must begin and end. The planning

also considers the capacity and availability constraints of each

process in the manufacturing system as well as the material

handling requirements. Constraints pertaining to the availability

of input materials must also be considered.

The push strategy is often adopted within the MRP-II

(Manufacturing Resource Planning) strategy of production planning

and scheduling. MRP-II, however, is itself a methodology for CIM.

Typically, an MRP-II implementation would consider business

planning, sales planning, production planning, inventory planning,

resource allocation, master production scheduling, material

requirements planning (MRP), and capacity planning as an ensemble

of planning functions. A detailed discussion of each of these

functions and their associated mathematical relations could easily

constitute another paper. The interested reader is referred to

Orlicky [30] and Fox [17] for an overview of these functions.
15

4.1.2 Pull . An alternative approach, which has found more

application in the flow shop environment, is the pull strategy for

which the Kanban approach is one potential implementation [25].

This approach begins by defining an ideal state for the

manufacturing processes and then releases orders to them so that

the ideal state is maintained. For example, one Kanban approach

would be to limit the number of orders that have been processed by

a given process, but have not yet completed processing at the

successor process. In the flow shop environment, this Kanban

implementation is particularly useful since it allows a given

process to function at maximum capacity whenever bottlenecks do

not exist at successor processes, but inhibits processing when

excessive processing output is building up in the system.

Consequently, an inherent benefit of the Kanban approach is to

stabilize the response characteristics of the overall system.

4.1.3 Remarks . The designation of push versus pull strategy is

unfortunate as it appears to imply that one or the other can be

applied. The fact is that both strategies can be implemented

simultaneously. Recently, Just* in-Time (JIT) has become nearly

synonymous with the Kanban approach as an attempt to minimize

Wo rk = in - Pr o gr e s s (WIP) inventory [10,16,22]. Certainly, push

strategies can also address this performance criteria. From a

system's point of view, the Kanban approach is most likely to

improve overall system performance when the probability that a

given process will be critical is nearly constant. In the job

shop environment, this may not be the case. For example, it may

be desirable to increase the WIP for a given process when the

process is likely to become critical for an extended period in the

near future. The Kanban approach can constrain this buildup and

perhaps eventually limit overall throughput. As demonstrated,

minimization of WIP may not be the only performance criteria that

must be considered. Ironically, an often cited feature or the

Kanban approach is its minimization of scrapped production. This

results from the fact that the successor process often serves as a

quality check for its predecessors. When processing faults arise

in the Kanban environment, they can often be recognized and

corrected before excessive WIP is generated. This observation

again confirms the reality that in manufacturing systems, there

are alternative performance criteria which must be considered.

4,2 Aggregate Production Planner

Aggregate Production Planning (APP) attempts to generate

production quotas for individual products or groups of products to

be manufactured over an extended planning horizon. In addition,

the APP must specify target inventory levels for considered

buffers. These decisions are made subject to capacity constraints

and both the real and forecasted demand for finished goods.
16

4.2.1 Problem Formulation . As discussed above, the APP's decision

can be based upon individual products or an aggregate product

grouping. To minimize the introduction of additional notation,

the formulation for the APP's decision will be given in terms of

the basic products pm (m-l,...,M). Assuming that T planning

periods will be considered, the primary decision inputs into the

APP are the actual and forecasted demands for each basic product

type in each planning period f, denoted by d^O) for m-l,„..,M and

r-t+1

,

. . . , t+T . Let a nm (r) represent the anticiapated production

capacity for process P n consumed in the production of one unit of

product pm

in period r while c n

(r) will represent the anticipated

availability of process P n in period r ( r-t+1 ,..., t+T) . Finally,

letting p m

(r) represent the planned production of product pm in

period r, the basic production capacity constraint is given as

M

2 a (r) p (r) < c (r) for n-l,...,N and r-t+1 t+T (14)

m=l n “ m n

Note this formulation assumes that the production quota pm(t) for

the current period t has already been submitted to the Detailed

Production Planner (see below) for implementation. Also in the

more general case, we might desire to differentiate between

regular and overtime production capacity.

We next consider a set of balance constraints for material

inventory. Let I m(r) be the level of planned inventory for

product p m in planning period r while B m

(r) represents any

backorder for product pm

that results from production planning for

period r. The resulting inventory constraints have the form

P m

(r) + I m

(r-1) - Bm

(r-1) - I m(r) + Bm(r) - d^r) (15)

for m = 1 ,

. . . , M and r-t+1 , . . . , t+T

.

In addition, the production

planner can impose production smoothing limiting the fluctuations

in p m(r) from period to period as well as inventory smoothing

constraints which limit the fluctuations in inventory.- An

additional set of constraints, which must be considered but are

difficult to include due to their product specificity, are the

constraints dealing with the availability of input materials for

each product type. Finally, there may be constraints in addition

(15) relating the production in a given period p m

(r) to the demand

in that period d m

(r) and the inventory and backorders from the

previous period(s).

The APP will typically have a collection of objective

functions to be optimized. Let

**[ lP m ( r )

»

xm( r )

>

Bm(r)

•

dm ( r )I r-t+1, . .

.

, t+T;m-l, . .

. ,M)

]

for i-1 , . . . ,L"

represent the collection of objectives to be optimized subject to

the above constraints. We also assume that the utility function

0[$x

( •),... (•)] has been defined to represent the comprise

among those objectives. This is a stochastic optimization problem

since the demands d m

(r), the process consumptions a. nm (r), and the
17

process availabilities c n

(r) are never known with certainty.

4.2.2 Potential Problems . The inclusion of these nonde terminis t ic

(or stochastic) elements of this strategic problem- is a major

concern. The resulting decision must be robust in nature,

considering all possible contingencies. As one approach to

develop deterministic approximations to stochastic op timizaions

,

Charnes and Cooper [11,12] introduced chance - cons trained

optimization. Several shortcomings exist in the adoption of this

approach. First, the inclusion of the chance - cons trained elements

significantly complicates the structure of the problem. For

example, linear constraints can become nonlinear when the

probabilistic definition of the constraint is made. To develop an

appreciation for the complexity that results when deterministic

approximations are developed for stochastic formulations of the

APP problem, the reader is referred to Bitran and Yanasse [8] . Second, the decision optimizes only the expected values, foregoing

an extensive contingency analysis. This fact is particularly

troubling since the APP's decision is often strategic in nature.

Finally, the approach currently considers a single objective only.

Davis and West [13] recently merged the approaches of Monte

Carlo simulation and mathematical programming to develop a method

for strategic project scheduling. Using Monte Carlo simulation,

one thousand potential linear programs were generated and solved.

Subsequently, an empirical probability density function for the

optimal solution was developed which could be employed in

contingency analysis. Although their approach was computationally

expensive, the comparison of the method to other stochastic

decision-making approaches provided potential avenues for

simplifying the approach. The need for a contingency analysis was

clearly defined. To date, the authors are unaware of any reported

investigations of chance - cons trained , mul t i - c r i t e r i a optimization

approaches

.

4.2.3 Feedback and Updates . Although the APP plans for the

production periods t+1 through t+T only the product quotas p m

(t+l)

for m-1 M are implemented. During the next planning period,

feedback indicating the actual production from the previous period

is considered to update resulting inventory and backorder

information, and a new pm

(t+l) will be generated. Thus, the APP

must respond to the deviations between actual and planned

production quotas. Furthermore, the APP must continually update

its planning as forecasted demand becomes realized with actual

booked orders. However, the time scale upon which this updating

must proceed is on the order of a week or more. Finally, the APP

must continually learn processing parameters through the

acquisition of real production data that arises from its previous

p 1 ann ing

.
18

4.3 Detailed Production Planner

The Detailed Production Planner (DPP) considers the specified

production quotas over a shorter horizon, and issues a request

that specific products be produced along with their associated due

dates for finished production. Before the request for a given

product is issued, the DPP ensures with some probability that all

essential inputs for the product will be available when required

during production and that the essential processes to manufacture

the product will be available. The DPP also attempts to execute

all the inventory policies established by achieving the preplanned

target inventory levels. A variety of models exist to implement

the detailed planning function, and our discussion will summarize

three which illustrate a dichotomy of approaches.

Bitran e_t a_X,. [6,7] have developed a three-level model to

implement the aggregate/detailed production planning functions.

The basis of their approach is to develop an aggregation of

individual product items into production families and then into

production types. Their aggregate production planning problem

considers the aggregate production cost subjected to a similar set

of constraints as discussed above except the decision is defined

with production types as the decision variables. The second level

of their hierarchy represents a disaggregation of the top level's

decision over a shorter horizon with production families serving

as decision variables. At this level, setups are considered. The

third level disaggregates production families into individual

production items. In their approach, each of the two lower

levels' problems is posed as a knapsack problem within the

constrained specifications arising from the solution of the

problem above it. In this manner, the disaggregation approach

becomes a single pass algorithm with no formal mechanism for

negotiation and feedback among the hierarchical levels.

Axsatter and Jonsson [5] have developed an alternative

disaggregation approach. Axsatter [3,4] first developed a

methology for aggregating both processes and products. In his

aggregation scheme, the errors arising from the aggregation are

quantified and procedures which minimize this error are defined to

generate an optimal approximate aggregation. Axsatter and Jonnson

then developed a detailed recursive relationship relating

production of products and parts in a given period to Inventory

and production in subsequent periods. With this recursive

relationship and the approximate aggregation scheme, aggregate

recursive relationships are then derived. A solution to the

aggregate relationships is then defined to provide an aggregate

control strategy. Using MRP principles, this aggregate production

control strategy is disaggregated into a master production

schedule for the production of individual parts and products.

Gershwin [20] developed his aggregation scheme based upon the

dynamics of the manufacturing system. Specifically, he argued
19

that dynamic responses can be decomposed into slowly varying and

rapid components. He then states, as has been demonstrated here,

that the dynamic response arises from activities which are

characterized by their initial and terminal events. By looking at

the frequency at which the initial events for a given type

activity occur, he argues that these frequencies for different

event types will naturally cluster into groups. That is, certain

events will occur at a frequency which is significantly greater

than the characteristic frequency of the next group, which in turn

is significantly greater than that of the next group. The

structure of the disaggregation is to schedule the least frequent

event group first at the highest hierarchical level. The

decisions at this level then specify a constant system response

for the next level which in turn schedules the next most frequent

events. Finally, the most frequent events are scheduled at the

1 owe s t 1 eve 1

.

As demonstrated, there are a variety of disaggregation

schemes for transferring an aggregate production schedule into a detailed job list for production scheduling. In all cases,

capacity planning was considered. All approaches also assume that

the lower level will consider a time scale that is shorter than

the considered time scale of the decision above it, and therefore,

considered events will occur more frequently. Both B i t r an e t al . [7] and Axsater and Jonnson [5] have discussed the relationship of

their approach to the MRP functions cited earlier. Still in

viewing this literature, a universal modeling approach and a

functional definition for detailed production planning has not yet

emerged

.

5 . SUMMARY

This paper discusses a collection of mathematical models of

various decisions which impact the design and implementation a CIM

system. We have included objectives, constraints, decisions, and

control strategies inherent in every CIM system. Although this

list is incomplete, hopefully it will provide a contribution to

the automation and integration problems being addressed in CIM.

The models can be used to guide the development of the technology

needed for automation. They also provide the inputs, outputs, and

interrelations needed for integration, regardless of the specific

CIM architecture used.

We plan to continue this modeling work. We are particularly

interested in data management and communications and the impact

they have on the models described above. For example, timing

requirements on data may introduce constraints into the decisionmaking similar to the material handling constraints considered in

scheduling. In addition, we plan to investigate CIM architecture

questions in more detail.
20

REFERENCES

[1] Astrom, K. J . , "Theory and Applications of Self-Tuning

Regulators," Automatiea, Vol. 13, 1977, pp. 457-476.

[2] Astrom, K.J. and Wittenmark, B., Computer Controlled Systems:

Theory and Design, Prentice -Hall ( Englewood Cliffs, New

Jersey , 1984.

[3] Axsater, S. "On the Design of the Aggregate Model in a Hierarchical Production Planning System," Engineering and

Process Economics, Vol. 4, 1979, pp . 89-97.

[4] Axsater, S. "Aggregation of Product Data for Hierarchical

Production Planning, Operations Research, Vol. 29, No. 4,

1981, pp. 744-756.

[5] Axsater, S. and Jonsson, H., "Aggregation and Disaggregation

in Hierarchical Production Planning," European Journal of

Operations Research, Vol. 17, No. 3, pp . 338-350.

[6] Bitran, G. R. , Haas, E. A. and Hax, A. C. , "Hierarchical

Production Planning: A Single Stage System," Operations

Research, Vol. 29, No. 4, 1981, pp . 717-743.

[7] Bitran, G. R. , Haas, E. A. , and Hax, A. C. "Hierarchical

Production Planning: A Two Stage System," Operations

Research, Vol. 30, No. 2, 1982, pp . 232-251.

[8] Bitran, G. R. and Yanasse, H . H .

, " De t e r mini s t i c Approximations

to Stochastic Production Problems," Operations Research, Vol.

32, No. 5, pp. 699-1018.

[9] Blattner, J.L., Mika, L.M., Tanski , J.A. and Schiller, T.L.,

A Blast Furnace Dynamic Mode 1 - -Recons true t ion of the PLAIC

Blast Furnace Model (Volumes I, II and III), PLAIC Report

Number 149, Purdue Laboratory for Applied Industrial Control,

Purdue University, W. Lafayette, Indiana, 1986.

[10] Buzacott, J.A., 1968, "Prediction of the Efficiency of

Production Systems without Internal Storage," International

Journal of Production Research, Vol. 9, 1968, pp . 173 - 188.

[11] Charnes, A. and Cooper, W., " Chanc e - Cons t r a ine d Programming,"

Management Science, Vol. 16, No. 1, 1969, pp . 73 - 79.

[12] Charnes, A. and Cooper, W. W., "Response to 'Decision

Problems Under Risk and Chance Constrained Programming:

Dilemmas in the Transition'," Management Science, Vol. 29,

No, 6, 1983, pp. 750-753.
21

[13] Davis, W.J. and West, S.M. , "An Integrated Approach to

Stochastic Decision-Making: A Project Scheduling Example,"

IEEE Trans, on Systems, Man, and Cybernetics," Vol 0 SMC-17,

No. 2, 1987, pp. 199-209.

[14] Davis, W.J., "Evolving Apllications for Artificial

Intelligence in Real-Time Production Scheduling," Proc . of

the ASME Computers in Engineering Conf . , San Frnacisco,

California, Aug. 1-4, 1988, pp . 215 - 224.

[15] Davis, W. J. and Jones, A. T. , "A Real-time Production

Scheduler for a Stochastic Manufacturing Environment",

International Journal of Computer Integrated

Manufacturing, Vol. 1, No. 2, 101-112, 1988.

[16] Finch, B.J. and Cox, J.F., "An Examination of Just-In-Time

Management for the Small Manufacturer: with an Illustration,"

International Journal of Production Research, Vol. 24, 1986,

pp. 329-342.

[17] Fox, K.A., "MRP-II Providing a Natural 'Hub' For ComputerIntegrated Manufacturing,' Industrial Engineering, Vol. 16,

No. 10, 1984, pp . 44-50.

[18] Galbraith, J.K., Organizational Design, Addison- Wes ley , Menlo

Park, California, 1977.

[19] Geoffrion, A.M., "Elements of Large-Scale Mathematical

Programming, Parts I and II," Management Science, Vol. 16,

No. 11, 1970, pp. 652-691.

[20] Gerswhin, S.B., A Hierarchical Framework for Discrete Event

Scheduling in Manufacturing Systems, Technical Paper LIDS-P1682, Laboratory for Information and Decision Systems,

Massachusetts Institute of Technology, Cambridge,

Massachusetts, 1987.

[21] Graves, S, C., "A Review of Production Scheduling,"

Operations Research, Vol. 29, 1982, pp . 646-675.

[22] Hall, R. W., Zero Inventories, Dow Jones-Irwin Co., Homewood,

Illinois, 1983.

[23] Ham, I. , Hitami, K. and Yoshida, T. , Group Technology

Applications in Production Management, Kluwen Publishing

Company, Boston, Massachuttes , 1985.

[24] Jackson, R. H. and Jones, A. T., (eds.)

,

Proceedings of

the Symposium on Real-time Optimization in Automated

Manufacturing Facilities, National Bureau of Standards

Special Publication 724, Gai thersburg , Maryland, 1986.
22

[25] Jackson, R. H. and Jones, A. T. , "An Architecture for

Decision Making in the Factory of the Future", Interfaces,

Vo X . 17, No. 6, 1987, pp . 15-28.

[26] Jones, A.T. and Whitt, N., ed.

,

Proceedings on Factory

Standards Model Conference, National Bureau of Standards,

Gaithersburg, Maryland, November 20-21, 1985.

[27] Lasdon, L.S., Optimization Theory for Large Systems,

Macmillan, New York, 1970.

[28] Libosvar, C. M., "Hierarchies in Production Management and

Control", MIT Technical Report, LIDS-P-1734, January, 1988.

[29] McPherson, R. F. and White, K. P. , "A Management Control

Approach to the Manufacturing Planning and Scheduling

Problem", Proceedings of the Symposium on Real-time

Optimization in Automated Manufacturing Facilities, National

Bureau of Standards Special Publication 724, Gaithersburg,

Maryland, 1986, pp . 145-166.

[30] Orlicky, J., Materials Requirement Planning, Mc-Graw Hill,

New York , 1975

.

[31] Papapanagio tou , I., Maliotis, G. and Koivo, A.J., A SelfTuning Controller for Rolling Mill Processes, Report No. 141,

Purdue Laboratory for Applied Industrial Control, Purdue

University, W. Lafayette, Indiana, 1984.

[32] Raman N. , "A Survey of the Literature on Production

Scheduling as it Pertains to Flexible Manufacturing Systems",

National Bureau of Standards Report NBS - GCR - 8 5 - 4 9 9 , U.S.

Department of Commerce, National Bureau of Standards,

Gaithersburg, Maryland, 1985.

[33] Rodammer, F. A. and White, K. P. , "Design Requirements

for a Real-time Production Scheduling Decision Aid",

Proceedings of the Symposium on Real-time Optimization

in Automated Manufacturing Facilities, National Bureau

of Standards Special Publication 724, Gaithersburg,

Maryland, 1986, pp . 401-422.

[34] Saleh, A., "Real-time Control of a Flexible Manufacturing

Cell", unpublished Ph . D dissertation, Lehigh University,

19 8 8.

[35] Sugimori, Y., Kusunoki , K., Cho , F. and Uchikawa, S., "Toyota

Production System and Kanban System: Materialization of

Just- in-Time and Re spe c t - for - Human System, International

Journal of Production Research, Vol. 15, pp . 553-564.
23

[36] Watanabe, T., "A Model-Based Approach to Adaptive Control

Optimization in Milling," Trans, of ASME : Journal of Dynamic

Systems, Measurement and Control," Vol. 108, No. 1, 1986, pp

.

56-64.
NBS-1 14A i RE v. 2 -ec > U.S. C" E P T . OF COMM. 1. PUBLICATION OR 2. Performing Organ. Report No. 3. Publication Date

BIBLIOGRAPHIC DATA

REPORT NO. !

SHEET (See instructions) NISTIR 8b- 3872 FEBRUARY 1989 1

4. TITLE AND SUBTITLE

A Functional Approach to Designing Architectures for CIM

5. AUTHOR(S)

Wavne Davis. Albert Jones

6. PERFORMING ORGANIZATION (If joint or other than NBS, see in struction s) NATIONAL BUREAU OF STANDARDS

7. Contract/Grant No.

U.S. DEPARTMENT OF COMMERCE

GAITHERSBURG, MD 20899

S. Type of Report & Period Covered

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City . State, ZIP)

National Institute of Standards & Technology

Bldg. 220 Boom A-127

Gaithersburg, MD 20899

10. SUPPLEMENTARY NOTES

P" Document describes a computer program; SF~I8S, FlPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most si gn ifi cant information . If document includes a significant

bi bl iography or literature survey, mention it here)

Developing effective CIM architecture is hanpered by automation and integration

problems . The key to resolving these problems lies in a better understanding of

each manufacturing function and how it is related to other manufac Luring functions.

Our view is that mathenatical models can provide this understanding . This paper

presents the results of our initial efforts to develop such models. They can be

used to guide the development of the technology needed for automation. They also

specify the inputs, outputs, and interrelations needed for integration, regardless

of the specific CIM architecture used.

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

automation, computer integrated manufacturing, functional decomposition

,

hierarchy, mathematical models, process control, production planning, prod, scheduling

13. AVAILABILITY 14. NO. OF

PRINTED PAGES

f~X) Unlimited

[~~~] For Official Distribution. Do Not Release to NTIS 29

\ Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C.

20402. 15. Price

[TF! Order From National Technical Information Service (NTIS), Springfield, VA. 22161

1

&L2.95

USCOMM-DC 0O43.P8O