NISTIR 88-3837

On-Line Concurrent

Simulation in Production

Scheduling

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

August 1988

Issued February 1989

N1STIR 88-3837

On-Line Concurrent

Simulation in Production

Scheduling

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

August 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 G. Kammer, Acting Director

ON-LINE CONCURRENT SIMULATION

IN PRODUCTION SCHEDULING

by

Wayne J. Davis

Professor

University of Illinois

Urbana, IL

Albert Jones

Automated Manufacturing Research Facility

National Institute of Standards and Technology

Gaithersburg, MD

ABSTRACT

Flexible manufacturing systems (FMS) have been installed in

many factories around the world. Production scheduling is the

function responsible for assigning FMS resources to various

manufacturing tasks. On-line simulation is being used as an

analysis tool to choose among several candidate scheduling rules.

This paper defines on-line simulation, and describes the inputs

to and outputs from the on-line simulation trials. It also

addresses the statistical analysis of those outputs to determine

the "best" compromise scheduling rule. Finally, it presents

results from some preliminary scheduling experiments on the

Automated Manufacturing Research Facility (AMRF) at the National

Bureau of Standards.

1

1. INTRODUCTION

Flexible manufacturing systems ( FMS ) have been installed in

many factories around the world. These systems typically contain

several advanced machining centers tied together by an automated

material handling system. Each piece of hardware is under some

computer control, and the whole system is managed by an FMS

supervisory computer. Considerable effort has been expended in

integrating these FMSs with existing factory systems (scheduling,

MRP, process planning, etc). Success varies considerably from

one implementation to another.

In most cases, the tasks to be done in the FMS and the data

required to perform those tasks are still generated by one or

more factory existing systems external to the FMS. The tasks and

data are transferred to the FMS supervisory computer over a

computer network. The tasks are then decomposed into a set of

commands for each piece of hardware in the system. The

controlling computers monitor the execution of those commands and

pass status feedback up to the FMS supervisor. This feedback is

analyzed and sent up to the appropriate factory systems.

This implies that, today, most FMS systems have very little

autonomy. They simply act as sophisticated executors of

decisions made by someone else. Although they exercise "control"

over internal systems to ensure that tasks are carried out

correctly, they have little or no authority to change external

decisions, even when unforeseen events occur.

This situation is slowly changing, and the FMS systems of

tomorrow are expected to have increased autonomy. This means
2

that they will make many of their own decisions and generate

much, if not all, of the data needed to carry out those

decisions. Consequently, the existing hierarchical structures

[JON85] used to control FMS activities must be expanded to

include decision-making.

Production scheduling is one such decision. Davis and Jones

[DAV88] have recently proposed a algorithm for doing real-time

production scheduling which can be integrated with most of the

existing FMS hierarchical control architectures. That algorithm

proposes to distribute scheduling across the levels of the

hierarchy. This implies the ability to 1) quickly analyze

alternatives at a given level, 2) perform contingency planning at

each level, and 3) resolve conflicts between decisions at

different levels. The primary analysis tools proposed for

achieving these goals are on-line and real-time simulation.

On-line simulation requires each simulation to be

initialized from a known state, which is tied to the actual state

of the manufacturing system. That state remains constant

throughout the scheduling analysis. Real-time simulation allows

the initialization state to change from one trial to another,

based on the actual evolution of the manufacturing system. This

paper describes the approach being developed to performed online, simulation. A companion paper is being prepared to address

the issues involved in doing real-time simulation.

2. PROBLEM STATEMENT

Before describing the techniques used in on-line simulation
3

for production scheduling, we present some necessary background

information on scheduling.

2.1 The Production Scheduling (PS) Problem

An FMS contains N distinct processes (see Figure 3) denoted

by Pn (n=l,...,N). These processes can be one of three types.

First, a process can perform operations that physically alter the

state of a job such as machining or deburring. Second, a process

can perform operations that ascertain the true attributes of the

job such as inspection or performance testing. Finally, a process

can perform operations that change the physical location of a job

such as robots, conveyors, or automated guided vehicles (AGV)

.

We assume that jobs JOBj (j = l,...,J) are available for

scheduling. We also assume that each JOBj has a specified due

date Dj and requires the fabrication of a single preplanned

product type p ^(m=l, . . . ,M) . The number of units of a given

product type comprising the JOBj will be denoted as #(JOBj). We

assume, without loss of generality, that #(JOBj) is less than or

equal to the maximum number of units of product p n that can be

transported in a single trip by the material handler, i.e. the

maximum number of parts that will fit on a pallet or fixture. If

more than one delivery is needed, we simply created new JOBs.

We note that this disaggregation is imperative to truly

capture the material handling interactions which have been

[GRA81, RAM85] neglected in most other formulations of the

scheduling problem. We further note that the aggregation

required to get any desired information about an original

customer order from our definition of JOB is very simple.
-

’

'

'
STATE

OF

THE

MANUFACTURING

SYSTEM ( + i o

flQuec

'l•

KC/H'

T/^cI

^hg^l/a c

/4l<; 0r

rn/ 
.
4

The production scheduler determines the following quantities

Ej n

- the planned arrival time for JOBj at process Pn ,

Lj n

- the planned pickup time for JOBj at process Pn ,

Ej n - planned time for Pn to begin processing JOBj, and

Lj n - planned time for Pn to complete processing JOBj

These quantities are chosen to optimize some multi-criteria,

utility function subject to several types of constraints: due

dates, precedence relations, processing capacity, resource

availability, and material handling. The optimization criteria

could include minimizing tardiness, maximizing production

throughput, or maximizing process utilization.

2.2 Solution Methodologies

Mathematical programming approaches to solving the PS

problem have received considerable attention in the literature.

Graves [GRA81] and Raman [RAM85] have provided excellent surveys

on these techniques. However, their computational requirements

and restrictive assumptions, particularly about material handling

constraints, tend to limit their applicability in a real FMS

environment. Recently, off-line simulation studies [MIL86, NOR86]

and AI heuristics [JAC86] have also become a popular means of

generating schedules. Although these techniques do allow more

realistic assumptions, they still have unacceptable computational

inefficiencies. Furthermore, they only generate feasible

solutions with no measure of optimality. In addition, these
.

.

-
5

methodologies have not been able to respond quickly to unexpected

events in the FMS

.

Davis and Jones [DAV88] proposed a algorithm for real-time

production scheduling (see Figure 1) . The foundations of this

algorithm are two forms of simulation. The first consists of R

on-line, concurrent evaluations of candidate scheduling rules.

These are invoked whenever a new schedule needs to be generated,

and are initialized to the current "state of the system". This

occurs whenever a new job is ready for production or a shop floor

problem invalidates the existing schedule. The second consists

of continuously-running evaluations of potential scheduling rules

to provide a contingency planning capability. This approach

creates several problems in output analysis not covered in the

existing simulation literature [LAW86].

This paper focuses on on-line simulation analysis. It

first describes the data inputs to and outputs from the on-line

simulation trials. It also addresses the statistical analysis of

those outputs to determine the "best" scheduling rule. Finally,

it presents results from some preliminary scheduling experiments

on the Automated Manufacturing Research Facility (AMRF) at the

National Bureau of Standards.

3. INPUT DATA FOR THE SIMULATIONS

The on-line, concurrent simulations described in [DAV88]

require simulations to be initialized to the current "state" of

the system. That state contains status information about the
6

processes, buffers, and jobs currently on the shop floor. In

addition, it includes the current schedule and information about

the new jobs to be added to that schedule.

3 . 1 Processes

The state of each type 1 and type 2 process Pn contains the

following information for each JOBj at the process: job ID, the

product type m corresponding to JOBj, the batch size #

(

JOBj )

,

and Ej n , Ej n , L

j

n , and L

j

n

. Although there are a variety of type

3 processes, material transportation devices, we limit our

discussion to automatic guided vehicles ( AGV) . We note that

expanding the definition to handle other devices is straight

forward. In addition to a BUSY/IDLE indicator, the state of each

AGV contains the following information for each JOB it is

transporting: the JOB ID, destination and path being used,

current location, expected completion time (Ej n for deliveries,

J_j n for pickups) . The topology of the transportation network has

direct impact on the complexity of both location and path

definitions. In small, simple networks the last node visited may

suffice for location, and a list of nodes for the path. In more

complicated systems, the network can be partitioned into sectors.

These sectors IDs can then be used to define both pieces of data.

3.2 Buffers

Buffers are used as temporary storage repositories for workin-process or raw material inventory. They can also be used to

store other types of inventory such as tools, fixtures, and robot
7

end effectors. Buffers typically have several distinct

characteristics which impact the complexity of their state

definition. Some buffers are located near and only store

inventory for a unique process. Others can store inventory for

more than one process regardless of their location. Some buffers

have no natural ordering, such as bins. Others can have a two or

three dimensional ordering, such as tables and shelves. Some

buffers can hold one item per storage slot; others can hold

several items per slot.

We limit our discussion to tables and shelves with one item

per slot. Other implementations can be modeled as simple

extensions of these two. For buffer tables, the state is a

simple ordered list containing B entries, where B is the maximum

number of items that the table can hold. Each entry contains the

ITEM_ID and the time the item entered the buffer. For buffer

shelves, the state is either an NxM or an NxMxL matrix. Each

entry in these matrices has the same definitions as above.

3.3 Current Schedule

The current schedule contains timing data on all jobs and

processes on the shop floor for some period T into the future.

(Typically, T is one day or one shift.) For each process, that

data includes the expected start and finish time for each JOB to

be executed during T. For each job, that data includes the

sequence of processes to be visited, and the start and finish

times at each process. GANNT [BAK74] charts are the conventional

method for representing all this information on one diagram (see

Figure 2)

.
8

Figure 2 . Sample GANNT Chart

3.4 Current Jobs

The "state of the system" also contains the progress of each

job on the shop floor. The status of each job includes job ID,

current location (buffer, transporter, or process) , due date,

expected completion time, shop floor release time, list of

process to be used and any alternates, and expected/actual start

and finish time at each process. The list of processes can be

derived from the GANNT chart and is depicted in Figure 3.

Figure 3. List of Processes to

be visited by each JOB

3.5 New Jobs

Several pieces

to be schedule: a

completion time,

of information are required for each NEW_JOB

JOB_ID, due date, release time, expected

and a routing. A routing is either a
9

completely-ordered or partially ordered listing of the processes

needed to produce, transport, and inspect this NEW_JOB and the

expected time spent at each process. ordered pairs (PROCESS_ID,

DURATION). If we allow only one, completely-ordered, M step

routing then a simple ordered list processes and durations is

sufficient. If we allow the routing to be a partially-ordered

list of M activities, then we must include the precedence

relations among processes. This can be visualized using the

concept of a PERT [BAK74] diagram (see Figure 4). Precedence

relationships are enforced using the following convention: a

given activity cannot begin until all activities ending at its

start node have been completed.

Figure 4. Sample PERT Diagram

If we allow the scheduler to consider more than one routing

for each NEW_JOB, then the preceding definitions are inadequate.

One possible representation for such a generalized routing uses

an AND/OR graph (see Figure 5). This is an extension of the PERT

graph used above. Each arc represents an activity, each activity

has a start node and an end node, square nodes represent OR

branches and circular nodes represents AND branches. Precedence

relations are handled exactly as they described for the PERT

diagram

.
10

I

Figure 5. Sample AND/OR Graph

3 . 6 Remarks

We have described the information needed to define the

"state" of the system which is used to initialize the real-time

simulations. We are in the process of examining different data

structures for storing and updating this information. Substantial

testing is required to estimate the robustness and efficiency of

various structures in both the laboratory and the real-world.

There is an additional problem in a real-world FMS because the

data required to generate those structures will come from the

shop floor sensors and computers, the process planning data base,

and the production scheduling data base. This "raw" data must be

converted to the aforementioned structures before they can be

used to initialize the R concurrent simulations.

Second, commercial simulation packages cannot easily be

initialized to a predetermined state defined by an arbitrary
11

collection of data structures. In SIMAN 1

, this can be

approximated by using the interactive debug facilities. The

system state is saved in SIMAN format at prespecified time

intervals. The generated system state file is then sent to each

of the real-time concurrent simulators. Through their

interactive debug facilities, each of the R concurrent

simulations is initialized using the most recently transmitted

file. However, the information contained within the SIMAN

initialization file can be used to define the .above data

structures or the above data structures could also be employed to

evaluate the SIMAN initialization file. The user must write all

translators needed to carry out this conversion. Other

simulation languages have similar features.

4. SIMULATION OUTPUT DATA

The output from each simulation trial k can be limited to

j n ' E

j

n ' Lj n , Lj ( n ) we

E j n ' Ej n , Lj^, Ljj-j) . If we let ^jn — (E

can use the following matrix notation to visualize the output

from one trial run, for one potential scheduling rule, for J

JOBs. Distinct values for the elements of this data structure

will be derived on each simulation trial for a given rule. Each

potential scheduling rule will be analyzed in precisely the same

manner

.

1 Certain commercial equipment, instruments, or materials

are identified in this paper. Such identification does not imply

recommendation or indorsement by the National Bureau of

Standards, nor does it imply that the material or equipment

identified are necessarily the best available for the purpose.
12

v

11

V =

v

IN

v v

J1 JN

The above definition for V obviously contains all the

essential data to. determine the times when each JOBj arrived at

given process Pn , when it was processed and when it departed from

the given process. From this data, we can clearly determine the

queue population at each process at any specific time. Also

details pertaining to the specific material handling procedures

used is given. Since the data were derived from a detailed

simulation of the manufacturing system, we can be assured that a

feasible material handling strategy does exist to effect the

events as given. In summary, the defined data structure does

impose some limitations upon the information that can be gathered

from the simulation. However, as we will show, the compact

structure does provide essential data to compute the performance

criteria that are often considered in the simulation of

manufacturing system.

Several performance criteria, can be defined using the

output matrix V from a given simulation of a proposed scheduling

rule. We give several examples:

job tardiness = max{ 0, max [ Lj n ] } = Dj

n

(1)
average tardiness ( 2 ) =

process utilization

Z D* /J

j=l

Ejn ) / T (3)

where the summation is over all JOBs that have process Pn in

their preferred routing and T is the length of the simulation run

on a given trial.

job flow time = max { L

j

n } - min {Ej n } (denote this FT j ) (4)

n n

N

job productivity = £ ( Ljn ~ E jn ) / FT

j

( 5 ) n=l

We now discuss the statistical analysis required to analyze

those performance measures.

4. STATISTICAL ANALYSIS OF SIMULATION OUTPUT

We first discuss methods for estimating performance measures

fl(V^) for 1=1,..., L for a single scheduling rule r=l,...,R from

the output V^: for k=l,...,K of K simulation trials. We then

address the issue of comparing these performance measures across

different scheduling rules to determine the "best" rule.

4.1 Estimating Performance Measures for a Single Rule

Assume, for now, that r is fixed. As noted above, on-line

simulations are run whenever a new schedule must be generated.

The inputs and the internal model must be updated to reflect the

current state of system. All K simulation runs will use this as
14

their initial state, will schedule the same J JOBs, and will run

for the same amount of simulated time T. This is graphically

depicted in Figure 6.

Forecasted system response

Figure 6. Graphic Depiction of On-Line Simulation

The output from trial k can be used to get one estimate for

each performance measure f ^

(

•

)

for scheduling rule r. Using the

output V1 ,...,VK from the K runs, we get a collection of

estimates f ^ (V^ ),..., fl (VK ) from which we define an empirical

cumulative density function for the prob{ f-^(-) < } as

F

1 (Z 1

) = # { f

1

(

• ) < Z

1

} / K (1)

From this empirical density, we compute the following statistics

f-*- = Sample Mean or Ex [f 1

] (2)

1

)

2 = Sample Variance or Ex [fl - f^] (3)

These statistics provide a summary for the performance of a given

rule with respect to the various objective functions. We can

also derive confidence intervals for the true mean Ex [f^] using
15

the terminating simulation results given in [ LAWS 6 ] . For

example, if all simulation trials are run using different random

number seeds, the 90% confidence interval is given by

f ± **-1, .05 \ K

(4)

The preceding analysis can be carried for each of the R potential

scheduling rules and each of the L objectives. The next step is

to develop the best compromise scheduling rule across all

objectives which we call r*

.

4.2 Comparing Scheduling Rules Across Performance Measures

We note that if R=2 and L=l, we can calculate a confidence

interval for the difference between the two means to determine

the best rule. Law and Kelton [LAW86] give two methods for

computing such a confidence interval. One requires the estimates

from the individual trials to be independent and one does not.

We note further that whenever L=l, regardless of the value of R,

we can use the "best of R systems" method described in [LAW86] . This method does not use confidence intervals. It attempts to

select the "best" rule given a specified probability of making

the correct selection. The method is straightforward, but does

require independence across all trials.

For arbitrary R and L, we have developed the following

approach to choose the best rule across all performance measures.

First we determine the nondominated set of scheduling rules,
16

denoted by R* u sing the approach in [ DES8 6 ] . The set R* will be

defined here such that r £ R* if for every r'C R there exists an

1 £ [1,...,L] such that

?r > f r ' (5)

Th^at is, scheduling rule r is in the nondominated set R* if and

only if it maximizes one of the L performance measures in the

mean sense.

Since it is highly unlikely that a given rule will

simultaneously maximize all L performance measures, we must

calculate "compromise intervals" [m^,M^] for each objective.

Here, m1 is the minimum for each performance measure over R and

M-'- is the maximum for each performance measure over R*

.

m^ = min

r c R

( 4 > M

1 = max { f £

}

reR*

( 6 )

We also define r

1 rm to be the rule which corresponds to m^ and

r

—

1

u£

to be the rule which corresponds to M

1

. This gives the

decision manker all the information needed to choose the "best"

compromise strategy r* £ R*

.

5. EARLY EXPERIMENTAL RESULTS

Preliminary testing of the on-line simulation approach is

being done on the Automated Manufacturing Research Facility

(AMRF) at the National Bureau of Standards (NBS) . Before

presenting some of our early results, we describe some of the

relevant features of the AMRF.
To

Tool

TURNING

VERTICAL

HORIZONTAL

Crib

WORKSTATION

WORKSTATION

WORKSTATION

AUTOMATED

MANUFACTURING

RESEARCH

FACIUTY
.
17

5.1 The AMRF

The AMRF is a prototype FMS built at the National Bureau of

Standards ( NBS ) where scientists conduct standards-related

research for automated manufacturing systems [SIM82]. The AMRF

contains six processes - three machining centers, a cleaning and

deburring center, an inspection center, and a material transport

center (see Figure 7). For our experiments, we assumed that each

process had one input buffer and one output buffer, but could not

queue any JOBs. We also assumed that each JOB had exactly one

route that it could use to go through the processes.

L«g»nd

Figure 8. PETRI Net Representation of Cart Path

Material transportation posed some interesting problems in

trying to generate schedules for the AMRF. Since there are two
18

bi-directional AGVs operating on a single track which contains no

loops, there is a very real potential for deadlocks. We used a

PETRI net [AGE79] model (see Figure 8) to analyze potential

deadlocks for material transfers. The nodes represent the

various places that the cart can stop.

5.2 Initial Simulation Results

We are still in the model verification and time testing

phase of the simulation analysis of the AMRF. To complete this

phase, we are using a single scheduling rule and three different

performance measures: time in system, productivity, and process

utilization. Using a SIMAN simulation package on an INTEL 80286

based personal computer, we can simulate the future response of

the system at approximately 1000 times the speed at which a

system emulation takes place. These numbers are based on

results obtained from making 100 runs with each run scheduling 50

JOBs. We are in the process of analyzing the performance

measures obtained from the various trials to verify the current

AMRF SIMAN model.

6. SUMMARY

We have discussed data requirements and statistical analysis

techniques for using on-line simulation as a tool for real-time

production scheduling. We have also presented some very early

results from our testing on the AMRF. Our future work falls into

several areas. First, we will complete the model verification

and time testing of the AMRF. We expect to increase the speed by
19

a factor of 10. Second, we will expand the current on-line

analysis to include several scheduling rules. We intend to use a

network of PCs to conduct the concurrent simulation trials.

Third, we will relax the assumption that all trials must be

initialized to the exact same state. This "real-time" simulation

approach recognizes that the system continues to evolve during

the scheduling analysis. We intend to examine the impact of this

on both the input and output structures and the statistical

analysis. This type of simulation has not been investigated.

Finally, we will initiate a study on both conventional and AI

approaches to developing data structures for representing the

state of the system and the simulation output.

draft
20

[AGE79]

[BAK74]

[ DAV88

]

[DES86]

[GRA81]

[ JAC86

]

[ JON85]

[ LAW8 2

]

[MIL86]

[NOR86]

[RAM85]

[SIM82]

7. REFERENCES

Agerwala, T . , "Putting Petri Nets to Work", Computer,

85-93, December, 1979.

Baker, K. R., Introduction to Sequencing and

Scheduling, John Wiley & Sons, New York, 1974.

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.

Dessouky, M. I., Ghiassi, M. and Davis, W. J.

"Estimates of the Minimum Nondominated Criterion Values

in Multiple-Criteria Decision-making," Engineering

Costs and Production Economics, Vol. 10, 1986, pp. 95-

104.

Graves, S, C. , "A Review of Production Scheduling,"

Operations Research, Vol. 29, pp, 646-675, 1981.

Jackson, R. , and Jones, A., (eds.), Proceedings of the

Symposium on Real-time Optimization in Automated

Manufacturing Facilities, National Bureau of Standards

Special Publication 724, Gaithersburg, Maryland, 1986.

Jones, A. and Whitt, N. (eds.), "Proceedings of Factory

Standards Model Conference", National Bureau of

Standards, November, 1985.

Law, A. M. and Kelton, W. D. , Simulation, Modeling and

Analysis, McGraw-Hill, New York, 1982.

Miles, T. , Erickson, C. and Batra, A., "Scheduling a

Manufacturing Cell with Simulation," Proc. of 1986

Winter Simulation Conference, Washington, D.C., pp.

668-676.

Norman, T. A. and Norman, V. B. , "Interactive Factory

Scheduling Using Discrete Event Simulation, Proc. of

1986 Winter Simulation Conference, Washington, D.C.,

pp. 665-667.

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

Scheduling as it Pertains to Flexible Manufacturing

Systems", National Bureau of Standards Report NBS-GCR85-499, Gaithersburg, Maryland, 1985.

Simpson, J., Hocken, R. , and Albus, J., "The

Automated Manufacturing Research Facility of the

National Bureau of Standards," Journal of Manufacturing

Systems . Vol. 1, No. 1, pp. 17-31, 1982.
HlBS-f 14A rev. 2 -ec

U.S. PERT. OF COMM. 1. PUBLICATION OR 2. Performing Organ. Report No. 3. Publ ication Date

BIBLIOGRAPHIC DATA

SHEET (See instructions )

REPORT NO.

NISTIR 88-3837 FEBRUARY 1989

4. TITLE AND SUBTITLE

On-Line Concurrent Simulation in Production Scheduling

5. AUTHOR(S)

Wayne Davis, Albert Jones

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

NATIONAL BUREAU OF STANDARDS

U.S. DEPARTMENT OF COMMERCE

GAITHERSBURG, MD 20899

7. Contract/Grant No.

8. Type of Report & Period Covered

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

National Bureau of Standards

Building 220 Room A-127

Gaithersburg, MD 20899

10. SUPPLEMENTARY NOTES

Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most significant in formation . If document includes a significant

bibliography or literature survey, mention it here)

Flexible manufacturing systems (FMS) have been installed in many factories around

the world. Production scheduling is the function responsible for assigning FMS

resources to various manufactring tasks. On-line simulation is being used as an

analysis tool to choose among several candidate scheduling rules. This paper

defines on-line simulation, and describes the inputs to and outputs from the

on-line simulation trails. It also addresses the statistical analysis of those

outputs to determine the "best" compromise scheduling rule. Finally, it presents

results from some preliminary scheduling experiments on the Automated Manufacturing

Research Facility (AMRF) at the National Bureau of Standards.

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semi colon s) automation, flexible manufacturing, production control, simulation, scheduling

statistics

.

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