A COMPUTER SlMULATl‘ON OF
A PACKAGiNG LINE

Thesis for the Degree of M. S.
MlCHIGAN STATE UNWERSITY

JAMES RANDOLPH STONEMAN
1975

III II III III II III II III III II III L

1293101403909 LIBRARY

Michigm State

University

 

This‘is to certifythat the

thesis entitled

A COMPUTER SIMULATION OF A PACKAGING LINE

presented by

JAMES RANDOLPH STON EMAN

has been accepted towards fulfillment
of the requirements for

M.S. degree in PACKAGING

 

 

Dr. Wayne H. Clifford

 

Major professor

Date February 25, I976

 

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DECIBZUOS

 

ABSTRACT

A COMPUTER SIMULATION OF A
PACKAGING LINE

By

- James Randolph Stoneman

This thesis and its computer program are designed
to simulate the operation of a high speed beverage canning
line such as a beer or soft drink line. The packaging
line chosen for this study is composed of five machines
and a connecting conveyor system. Through detailed time
studies, accurate data has been collected on the speeds
and relevant downtimes of these machines and programmed
into the simulation procedure. Treating these as con-
stants, the lengths of the interconnecting conveyor lines
can be altered and their effect on total line production
can be examined. An optimum conveyor length, matchingi
capital investment against production, can be arrived at
without having to spend the money or take the risk of

actually making a physical change in the line.

A COMPUTER SIMULATION OF A
PACKAGING LINE

BY

James Randolph Stoneman

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
' for the degree of

MASTER OF SCIENCE
School of Packaging

1975

ACKNOWLEDGMENTS.

I would like to thank Dr. Wayne Clifford without
whose gracious help and assistance this thesis would never
have been written. Sincere appreciation is also extended
to Mr. Bill Weatherston for his help in completing the
basic research required for this paper. Last, but not
least, I would also like to thank my wife, Marsha, for

her patience and understanding. .

ii

TABLE OF CONTENTS

Page

LIST OF TABLES . . . . . . . . . . . . iv
LIST OF FIGURES . . . . . . . . . . . . v

Chapter-

I. INTRODUCTION . . . . . . . . . . . 1
II. PROBLEM STATEMENT . . . . . . . . . 8
III. STIMULATION MODELING . . . . . . . . 11
IV. MODEL VALIDATION (DOWNTIME SUBROUTINE) . . 18
V. COMPUTER PROGRAMMING . . . . . . . . 31
VI. PROOF OF THE COMPUTER.PROGRAM. . . . . . 40
VII. SUMMARY . . . . . . . . . . . . 69;
APPENDIX . . . . . . . . . . . . . . 71
BIBLIOGRAPHY . . . . . . . . . . . . . 78

Table

> >- D> <m as os‘ox
u: to P4 Ln -D o: to

LIST OF TABLES

Proof Model 1

Proof Model 2

Proof Model 3

Proof Model 4

Visual to Proof MOdel
Main Program, PKGLINE
Subroutine, Down ..

Subroutine, PLINE .

iv

Page
43
50
55
61
65
72
74
75

Figure

O‘C‘O‘O‘LflLfi-P-b-P-I—‘J-‘w
waHHHLfl-I-‘UJNI-‘N

NH

LIST OF FIGURES

Block Diagram for Simulation Model

Flow Chart for the Simulation Model .

Depalletizer Downtime

Filler Downtime

Seamer Downtime

Packer Downtime

Traymaker Downtime

Flow Chart for Program PKGLINE
Flow Chart for Subroutine DOWN
Proof Model 1

Proof Model 2

Proof Model 3

Proof Model 4

Page
12
16
21
24
24
26
26
33
36
42
48
54
6O

CHAPTER I

INTRODUCTION

The purpose of this thesis is to duplicate the
operation of a high speed beer or soft drink canning line.
The typical beverage line is comprised of five machines
and a connecting conveyor system. These machines include:

1. Depalletizer - to remove the bottomless empty

cans from the incoming pallets.

2. Filler - to fill the empty cans with product.

3. Seamer - to attach the bottoms in an air tight

double seam.

4. Packer - to place the containers in convenient

packs of six and eight.

5. Traymaker - to place the packs in corrugated

trays for easy loading and shipment.

The interaction of these machines becomes very-
complex as each operates at different speeds, has differ-
ent capacities, and different patterns of breakdown.
Consequently, direct analytical techniques are inadequate
in reproducing such a system. The computer, then, is
necessary to simulate the operation of various designs of

such a high speed line.

A computer model is simply defined as a mathemati-
cal model expressed or written according to a particular
set of rules so that the model may be processed by the
computer.1

Simulation is defined as dynamic representation
achieved by building a model and moving it through time.2

Simulation models have been used to date for pur-
poses of experimentation or evaluation, that is, in trying
to predict the consequences of changes in conditions, or
methods without having to spendthe money or take the risk

3 Models can-

of actually making the change in real life.
not replace the real world; at best they reduce a complex
system to manageable proportions or serve to crystallize
our thinking and perception.4
Soft drink and beer canning lines lend themselves
well to the modern mass-production system of high speed
assembly lines in meeting the large demands of the con-
suming public. However, mistakes in assembly line design

can be costly in terms of idle time, lost production, and

 

1McMillan and Gonzalez, Systems Analysis - A Come
puter Approach to Decision Models (Homewood, 111.: R. D.
Irwin, 1968), p. 12. .

2William Arthur, "To Simulate or Not to Simulate:
That Is The Question," Educational Data Processing News-
letter, XII, No. 4 (1968), 9

3Dimetris N. Chorafas, Systems and Simulation
(New York: Academic Press, 1965), p. I7.

4

 

 

 

McMillan and Gonzalez, p. 9.

3

excess capital investment. Computer simulation can be
used in predicting the performance of a proposed line
design, and can, therefore, be useful in avoiding costly
mistakes in the installation and operation of an inade-
quate assembly line.5
The packaging line chosen for this study is com-
poSed of five machines and a connecting conveyor system.
Each machine or station contributes to the work of the
previous station, resulting in the production of the
finished product at the end of the assembly line. The
main program PKGLINE simulates the principle of this high
speed line with packages entering and exiting at each
operating station. Through exacting time studies the speeds
and breakdown frequencies of these machines have been recorded
and incorporated into the simulation program.through the use
of the subroutine DOWN.
These processing machines include (1) depalletizer,
(2) filler, (3) seamer, (4) packer, and (5) traymaker.
They will be examined in detail. I

Depalletizer

 

Nine foot high pallet loads of bottomless empty

cans are unloaded from semitrailers. The pallets are

 

5Janis Church, Simulation as a Tool in Assembly
Line Design (Dearborn, Michigan: Sodiety of Manufacturing
Engineers, 1971), p. 10.

 

 

pushed on a set of rollers built into the truck beds
directly onto another set of rollers that feed into the
depalletizing machine. A hoist raises the pallet into
position and a sweep arm automatically pushes each layer
of cans onto an accumulation table. This procedure is
repeated for each layer of cans until the pallet is
emptied. It is then automatically moved into a bin with
other empty pallets and is replaced in the hoist by a
full one. The accumulation table can hold a full pallet
width of cans. These cans are then fed into circular
discs which send them singly onto a cable-driven conveyor
line. The modern day depalletizer used in high speed
beverage lines is capable of handling 1500 cans per
minute (cpm) and costs about $18,000 on today's market.
While moving on the conveyor the cans are turned
upside-down and rinsed with water to free them from any
extraneous matter, such as fragments of fiber or other
loose material.6 This process does not require a machine
but Simply a twist in the conveyor line. Consequently it
has not been included in the operation of the simulation

program.

 

6Morris B. Jacobs, Manufacture and Analysis of
Carbonated Beverages (New York: Chemical Publishing Co;
Inc., 1959).

 

 

Filler

The cans proceed directly to the filler, a large
circular-pyramid Shaped machine. It rotates automatically,
slowing down when there are gaps in the line of cans on
the infeeding conveyor and speeding up to top rotation
velocity when the maximum number of cans are being
received from the depalletizer. Filler speed is approxi-
mately 1300 cpm. This is the slowest machine in a high
speed canning or bottling line. The cost of a large high
speed filler is $150,000. Production and output effi-

ciency of the packaging line is gauged on this machine.

Seamer

The seamer is usually situated very close to the
filler because the loss of carbon dioxide between the time
the cans are filled until they are sealed directly affects
the shelf life of the product. The greater the loss of
C02 the sooner the product loses its drinkability.

An operator hand feeds a tube of 408 closures
(bottoms) at a time into this machine which seals the filled
containers in an airtight double seam created by interlocking
the curl of the lid and the flange of the.can.7 The seamer
.runs at the same speed and capacity as the filler. It

costs about $90,000.

 

7National Canners Association, The Canning Industry
Ewashington, D.C.: Communications Services, 1971), pp. 21-
7.

 

At this point, the filled and closed cans must be
warmed to room temperature and preferably higher to aid
drying and to prevent sweating after casing. When dealing
with beer lines this is not a problem as the cans are
pasteurized, or cooked in steam ovens. As far as soft
drink beverage lines are concerned, however, the product
and metal container must be treated. Cans that are cased
while they are wet, or which sweat after they are cased,
are more subject to external rusting and may damage the
cases. Such warming may be accomplished by warm.water
sprays. This serves the double purpose of washing away
any of the beverage that may have spilled on the outside

of the cans.8

Packer
Cans are brought via conveyor into the packing
and cartoning area. The packer divides the cans into
groups of six or eight and places a set of clear plastic
rings around the top of each pack, separating them perm-
anently into six or eight packs for further processing.
The packer runs at about 1500 cpm and costs in the neigh-

borhood of $150,000 not installed.

 

8Jacobs, p. 6.

Traymaker

 

The six and eight packs are grouped together in
fours and threes respectively and die cut corrugated board
is folded and glued with hot melt adhesive to form'a tray
around the containers. Filled trays are transported on
conveyors to the dock area to be loaded onto pallets and
into semitrailer trucks. A traymaker costs $130,000 and
can handle 1700 cpm. This is the fastest machine on the
line from the standpoint of cans handled per minute.

The costs of these five machines are for the
capital investment only and do not take into account the
charges for installing them in running position on the
line.‘ We can assume at least a 50% increase for installa?
tion.

Conveyors today cost $100.00 per running foot.
These cost figures and capacities are current as of the

first six months of 1975.

CHAPTER II

PROBLEM STATEMENT '

When applying simulation the following steps are
taken. The first is to isolate, define, and quantify the
important features of the system to be simulated. Then
these features are represented in terms of a model which
is programmed for the computer. The model is then tested
to make sure it is functioning correctly by making it
reproduce known conditions. This validation stage enables
the model-builder to check that all relevant factors have
been included in the model and that the laws of behavior
have been correctly represented therein. The last stage
consists of running the model to simulate the effects of
various proposed system configurations, so as to assess
relative costs and benefits.9

The packaging line chosen for this study is a comp
plex system with many components. It involves several
machines and a variety of transfer equipment that is

arranged and laid out to form a complete high speed bever-

age canning line. Because of this complexity and because

 

9John Smith, Computer Simulation Models (London:
Griffin, 1968), p. 3.

 

the elements can have a random or variable nature, direct
analytical techniques are inadequate in selecting or
designing the optimum packaging line.' Simulation is chosen
when the overaIl performance of a system cannot be esti-
mated analytically and observation of the actual system is
not practical. Trial-and-error changes to an operating
packaging line can be expensive. With simulation, the
performance of a proposed line, or changes to an existing
line, are estimated without installing and operating the
line.10

The computer is used to simulate the operation of
various designs of such a high speed line. The machines
interact with one another through their capacities,
expressed in containers per minute (cpm), and downtimes
thus affecting the production of other machines as well as
total line output. By studying an actual high speed line
pertinent data was gathered concerning machine capacities
and relevant downtimes. Treating these as constants, the
lengths of the interconnecting conveyor lines can be
altered and their effect on total line production can be
examined.

For purposes of this paper we assume:

1. A continuous infeed of cans into the first

machine, the depalletizer.

 

10Church, p. 5.

10

2. An infinite amount of storage space capable of
accepting all the output of the last machine,
the traymaker. :

3. No downtime due to conveyor malfunctions. All
downtime is attributed to the machines in the
line.

Without being redundant it should again be men-

tioned that capital costs of the various machines in a
high speed beverage line are substantial. With this com-
puter program, line production efficiency can be set
against conveyor cost to design the most efficient line
at the lowest net cost.

The program, written in FORTRAN, uses variable
names to describe the machines (stations), downtimes, and
conveyors (buffers). It is relatively easy to change the
number and locations of the stations, the number, location,
and Size of the buffers, the distribution of operating
time, and the probabilities of rejects and jams to simu-

late other assembly lines.

CHAPTER III

SIMULATION MODELING

The problem.and an abstract model of the theOry
behind the simulation program have been examined in
previous sections. Now the simulation model will be
examined in detail.

Figure 3.1 isolates a machine and its entering

and exiting conveyor mechanisms. The variables are

defined:

C(I): entering conveyor to machine I,

IM(I): machine I.

C(1+1): exiting conveyor from machine I
carrying units to machine 1+1.

ICM(I): ‘maximum.number of units conveyor I
can hold. This is a holdup on the
line.

ICP(I): actual number on conveyor I from
previous cycle 0.: ICP §,ICM.

NMM(I) maximum number of units machine I can
hold. This is the quantity Q(I)
multiplied by the change in time and
is also a holdup on the packaging line.

NMP(I): actual number in machine I. These
will exit if exit conveyor I+l is open.

0 :_NMPl§_NMMZ'
MD(I): machine downtime
if MD 3.1 machine I is down
if MD = 0 machine I is operating.

11

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13

KLP(I): the production for machine I.

NH(I): the number of packages to leave
conveyor I to enter machine I. It is
the smaller of the machine capacity
and the conveyor content.

NC(I): the number of packages to leave

machine I to enter conveyor I+l.
It is the smaller of the conveyor
capacity and the machine content.

The simulation program can be divided into two
parts: (Figure 3.2):
I. the exiting packages from M(I)

II. the entering packages to M(I)

I. The exiting packages

A. A check is run on the station machine M(I) to
see if it is down.

1. If yes: no units will exit, output = O

2. If no: the machine is Operating. The
number of packages (units) in this machine
from the previous cycle will exit on this
cycle if the exit conveyor C<I+1> is open.

B.- A check is made to see if the empty space on
the conveyor C(I+l) is capable of holding all

the packages exiting from the maching M(I)'

1. If no: the number on the conveyor is
incremented by the number of packages it
can accept (denoted by NC) and the number
in the machine is lessened by a similar
amount, or

ICP<I+1> = ICP<I+1> + NC

NMP = NMP - NC

(I) (I)

14

2. If yes: the number on the conveyor is
incremented by the entire quantity in the
machine and the machine is left empty.
Equations =

ICP<I+1> = ICP<I+1> + NMP(I)
NC = NMP<I>

NMP(I) = 0

Show this operation.

Machine I can now accept packages from conveyor 1.

”II. The entering packages

A. A check is run to see if the empty Space in the
machine M(I) is capable of holding all the

packages exiting from the conveyor C(I)‘

1. If no: the number in the machine is incre-
mented by the number of packages it can
accept (denoted by NH) and the number on
the conveyor is lessened by a similar amount,
or,

ICP( ICP<I> - NH

I)

NMP NMP(I) + NH

(I)

2. If yes: the number in the machine is
incremented by the entire quantity on the
conveyor and the conveyor is left empty.
Equations

NMP(I) =(I) + ICP(I)
NH = ICP<I>

ICP<I) = 0

show this operation.

15

The following are mathematical equations for the
simulation model.

1. O < ICP < ICM

2. O < NMP < NMM

3. MD

0 if the machine is operating.

= l,2,3,...7 if the machine is down.
The whole number indicates the length
of time in minutes the machine is not
running.

16

 

I - N I
I , _ N a Number of Machines - 5

 

 

NC=ICM(I+1) -
ICP(I+1)

‘19 (“0) ) number of pack-

ages conveyor

I+1 can accept

 

 

 

 

 

    

 

Output - 0
Nc-O

 

  

NC greater

no
than NMP(I)

.‘7

  

 

 

 

 

r { NC-N'MP(I) 1

Transfer containers from Machine I to
Conveyor 1+1

ICP(I+1) - ICP(I+1) +~NC

NMP(I) - NMP(I) - NC

 

 

 

 

 

Number of packages that Machine I can
now hold from Conveyor I

NH I NMM(I) - NMP(I)

 

 

 

 

Figure 3.2.-Flow Chart for the Simulation Model. Part I

 

l7

 

NB - ICP(I)

 

 

 

ICP(I) I ICP(I) - NH
NMP(I) I NMP(I) + NH
transfer containers from Conveyor I. """”
to Machine I

 

 

 

 

 

Change I for next machin
I - I - 1

 

no

 

 

C m... 9

Figure 3.2.e-Flow Chart for the Simulation Mbdel, Part II

CHAPTER IV

DOWNTIME VALIDATION

Data for the subroutine evaluating the fre-
quency and size of the downtimes occurring in the packag-
ing line was gathered from detailed time studies made in
a high speed beverage canning plant over a period of
three weeks. This entailed 90 hours of actual observation.
The Operation times were observed and for each station the
distributions of those times were determined from.a stop-
watch study. The actual data and the appropriate bar
graphs are presented for the depalletizer, filler, and
seamer in the line. Downtime data and bar graphs for the
packer and traymaker were estimated by Bill weatherston,

Superintendent of Canning for a large Great Lakes brewery.

Depalletizer Downtime Data

lies. Beam
7" trouble with pallet infeed
1' 21" broken pallet
2' pallet caught on edge of hoist
2' 22" something caught under pallet at hoist
3' 54" broken pallet
18

17"
54"
1' 6"
36"
14"
s"
1' 15"
3' 2"
1' 41"
1' 23"
2' 29"
14"
54"
8..
22"
56"
47"
54"
29"
so"
1' 55"
10"
42"
.14"
12"
21"

19

slow infeed

hoist not raise pallet

edge of pallet caught on heist
hoist not raise pallet

pallet change

pallet change

inspection of equipment
switch lines

personnel switch

personnel switch

personnel switch

pallet not lock into place
personnel switch

personnel switch

personnel switch

personnel switch

pallet change

pallet change

pallet change

pallet change

chip board jam

jammed caps

grip fingers missed chipboard
grip fingers missed chipboard
grip fingers missed chipboard
grip fingers missed chipboard

10"
>10"
43"
12"
12"

I' 4"
s"

18"
55"
54"

2' 54"
55"

1.

48"
36"
32"
13"

1' 3"
2' l6"
1' 14"
1' 49"
3' s"
34"
44"
28"

grip
grip
grip
grip
grip
grip
grip

fingers
fingers
fingers
fingers
fingers
fingers

fingers

20

missed
missed
missed
missed
missed
missed

missed

chipboard jam

chipboard jam

chipboard jam

chipboard jam

chipboard jam

chipboard jam

sriP'

grip
grip
grip
grip
grip
grip
grip
grip
grip
grip
grip

fingers
fingers
fingers
fingers
fingers
fingers
fingers
fingers
fingers
fingers
fingers

fingers

missed
missed
missed
missed
missed
misSed
missed
missed
missed
missed
missed

missed

chipboard
chipboard
chipboard
chipboard
chipboard
chipboard
chipboard

chipboard
chipboard
chipboard
chipboard
chipboard
chipboard
chipboard
chipboard
chipboard'
chipboard
chipboard
chipboard

21

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7. .6 .5 .4 1. o.
ooeauauuo o>uuo~o¢

Length of Downtime (Minutes)

Figure‘4.l.-Depa11etizer Downtime.

z. '45"
4..
26"
30"
so"
1.

40"

3.
15"
41"
12"

4..

7' 24"
10"
24"

2.

19"
41"

2' 24"
7..

57"
16"
28"
30"

22

chip jam in hopper
suction cup malfunction
jam at disc outfeed
loose chip - not lower
jam.at disc outfeed
inspector

jam at disc output

Filler Downtime Data

 

jam in exiting tunnel

jam in exiting tunnel

jam in exiting tunnel

high can switch malfunction
high can switch malfunction
low level oil

tipped can at filler output
jam at filler infeed

jam in tunnel

bent can

jam.in tunnel

bent can in worm gear
output-one can gap

jam in tunnel

can partially filled

can partially filled

can partially filled

1' 12"
1' 2"
22"
2' 34"
1' 10"
6' 20"
4..
1.
1' 2"
s"
7' z."
2.
2' 12"
1' so"
54"
1' 35"
so"
42"
1.
1' 26"
56"
so"
40"
2.
1.
1.

23

can partially filled
can partially filled
no grease - one hour-light

no C0 light

2
clean filler with.water
can partially filled
jam.in tunnel

bent can at high can limit
jam in tunnel

no C02 gas

low oil level

jam in tunnel

jam in tunnel

jam.in tunnel

jam in tunnel

jam in tunnel

jam.in tunnel

jam in tunnel

jam in tunnel

jam.in tunnel

jam.in tunnel

no grease one hour-light
no grease one hour-light
can jam.at infeed

no grease one minute-light

bent can at high can limit

24

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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m
1 - 7
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1 4
X
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3 . {3
X X
l‘l""'l'-llx—z
X X
2
5 X X
|u~
X
'llll'lll'l'- I4":
X X
.ib
X X X X
- - 1
_ x _ x — x _ x x x x x x
3...
— x x x x x x x
5
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X X X X X
- .is
—X X X X X X X
0
o. no a: .w .3 .n a. 9‘ i.

eucouauuo o>uuono¢

Tine (Minutes)

Figure 4.2.-Filler Downtime.

 

 

 

 

 

 

6
l||||'al‘|ll"llulr
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X
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2 X
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X
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24

 

 

TTTT .

 

 

__._.

 

 

 

 

Id 32

_.._._._.._.._H_._.

ooceuaooc o>uuonoz

Tine (Minutes)

rigure 4.3.o-Seaner Downtime.

1.
4..
1.
1.
1.
3.

32"
3' 33"
1' 32"
1.

3..
30"
11"

1' 17"
2' 42"
4" .
4..
so"
1.

1.

2.

5.

1.

a"

25

no grease one minute-light
jam in tunnel

no grease one minute-light;
bent can

bent can

jam at rinser

Seamer Downtime Data

 

jam at discharge rail
bent lid in seamer head
bent can in seamer

can jam.in seamer output
seamer missed lid
seamer missed lid
seamer missed lid
jam.seamer output
seamer outputdmachinist
missed lid

missed lid

seamer output jam
seamer input jam
seamer output jam

can jam

transfer chain.jammed
seamer-output jam

seamer missed lid

26

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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ooceusouo o>uun~os

Time (Minutes)

Figure 6.4.-Pscker Downtime.

 

-............i.:.-fl

 

 

...... .. II ....F

 

 

 

 

 

 

 

 

20

 

 

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0

 

 

 

 

 

 

 

 

n. .1 .6 t, .u .4 or
euceuaooa o>uue~o¢

Time (Minutes)

Figure 4.S.-Trsymeker Downtime.

27

22" jam seamer output

5" seamer missed lid

10" seamer missed lid

4" seamer missed lid

24" seamer missed lid

1' 34" jam at output

4' 6" output conveyor - air nozzle
1' 10" output conveyor - air nozzle
24" seamer input jam

15" seamer input jam

1' 30" seamer input jam

l' 10" seamer input jam

1' bent can at seamer

In order to use this data in.a subroutine and
make it applicable to this simulation program.the
number of occurrences of downtime in each minute time
frame were counted and rounded to the nearest whole
minute. The size of the breakdowns range from.ane to
seven minutes. Each minute for all five machines is
represented by one data card for a total of seven cards

in all. The card representing the first minute is shown:

DATA (NX(1,K), K=1,5) / 49,35,24,30,20/

This translates to mean:

49 - one
35 - one
24 - one
30 - one

20 - one

The depalletizer

down as follows:

minute
minute
minute
minute
minute

minute

\lmUI-L‘UJNH

minute

28

minute breakdowns
minute breakdowns
minute breakdowns
minute breakdowns

minute breakdowns

for the
for the
for the
for the

for the

depalletizer

filler

seamer

packer '

traymaker

data for all seven minutes is broken

- 49 occurrences
occurrences
occurrences
occurrence
occurrences

occurrences

I
O O N H «P 0‘

occurrences

total time 49

12
12
4
10
0
0

minutes
minutes
minutes
minutes
minutes
minutes

minutes

 

87

minutes

It was decided to operate this line at approxi-

mately 80% efficiency, therefore 87 minutes or 20% of

'the total running time, the depalletizer is down. This

I would signify a total simulation time of 87 e .20 = 435

minutes. The total Simulation run to insure 80% effi-

ciency for all five machines is shown in an eighth data

card:

29

DATA NTOT / 435,355,220,325,440 /

435 minutes is the total simulation run for the depalletizer
355 minutes is the total simulation run for the filler
220 minutes is the total simulation run for the seamer
325 minutes is the total simulation run for the packer

440 minutes is the total simulation run for the traymaker

At what point in time a machine breaks down and
how long it remains down is determined by a random number
generator (RANF(X)) subroutine in the computer that
generates a number sequence between 0.0 and 0.99...9.
This sequence is found to be randomly and uniformly
distributed based on chi-squared testing.

An example of this downtime generation is shown

for the depalletizer.

Equation: M = NTOT(I() * RANF(0.5) + 1.0
= 435 * (0.130...) + 1.0
= 57.6

A different random number value for M is determined for
the depalletizer as well as for each of the other
machines at the beginning of each one minute time period.
This value is then run through the first column of the
seven data cards. Referring to the Subroutine DOWN in
Appendix A, it is seen that 57.6 is greater than 55 but.

smaller than 59, thus denoting the depalletizer will go

30

down for a period of three minutes. Should M be larger
than 62, as it will be 80% of the time, the machine would

remain in operation.

”CHAPTER VI
COMPUTER PROGRAMMING

5.1 Flow Chart

 

The computation process consists of a main
program and two subroutines.

The main program, PKGLINE, is concerned with
simulating the movement of a high speed assembly line.
Each station (machine) is handled individually with its
entering and exiting conveyor systems. Checks are run
on each station to see if it is operating and to see how
many packages each machine is capable of accepting from
the entering conveyor. Likewise a check is made to see
how many packages the exiting conveyor is capable of
accepting from its machine.

The subroutine DOWN analyzes and delegates down-
time to the various line machines in accordance with
observed data gathered from actual high speed assembly
line observations and estimates.

The subroutine PLINE used in Proof Model 4 uses
fields of symbols to present a unique visual "picture"
of the units as they are processed through the line. It

was written by Dr. wayne Clifford, Assistant Professor

31

32

at the School of Packaging for presentation at a
brewery packaging technology seminar at Kellogg Center.

A complete simulation run is eight hours. The
time increments are one minute each, totaling 480.. The
program computes the line status each six seconds and
prints out the current standing of each machine and
corresponding entering conveyor at the end of each
minute. The subroutine PLINE prints its visual every
six seconds.

Flow charts have been drawn to show both the
main program.PKGLINE, and the subroutine DOWN:

Figure 5.1.1 for PKGLINE and Figure 5.1.2 for
DOWN.

The variable names used in the main program:

ICP: number of packages on each conveyor.

ICM: maximum capacity of each conveyor.

NMP: number of packages being processed at
each station.

NMM: maximum capacity of each station.

MD: machine down = 0 if machine is running.

8 1,2,...7 if machine is
down.

KLP: production of each machine.

KPT: distinguishes the individual machines
(depalletizer,'filler, seamer, packer
traymaker)

UPD: indicates if machine is operating or

not by printing UP or DN.

33

{Dimension ICP(10). ICM(11), NMPUO), mum, 100.0), I

 

KLP(10), KPT(20), UPP(10), NHXUO)

f

 

 

 

 

[ Data NMP, ICP, ICM, .. , MD, RPT, UP, ON I
Initialization
Subroutine Ranset Random Number Generator

 

 

 

l Subroutine in Computer

DELT . 1.0 T-O N35 LP-Ol

\
Initial ization l

l Subroutine Pline Call to Set Up

— Subroutine
r---—---—--——— JI-lour-l,2 >
I
I Print Titles,
I
I
I

 

 

 

 

JTime-1,60>

.. —- —— -— - - -—-[5—ubroutine Down j

 

 

 

 

    

KLPU) II 0

T-‘l' + Delt I

I
I
I
I
I
I
® Figure 6.1.l.-Program PKGLINE. Part I

 

 

 

 

3li

 

 

" ________ 1 Time - 1,10::>.

LP I LP + ICP(N+1)

ICP(N+1) - 0
rcrtr) - Icn(I)

 

 

    

 

I

I I N+1 - 12
NC I ICH(I+1) - ICP(I+1)

 

 

>NMP(T) ayes

 

KLP(I) - KLP(I) + NC
ICP(I+1) - ICP(I+1) + NC
mu) - NMP“) — NC
NH - NHH(I) - NMP(I)

 

 

.L. . mm

 

 

 

yes

 

yes

-—--—-——-————-—-——-fi

 

NHX(I) I NH

 

941“" - rcp(r)l

 

NHP(I) I NMP(I) + NH
ICP(I) I ICP(I) - NH

 

 

 

I
I
I
I
I
I
I
I
I
|
l
I
I
l
I
I
l
I
l
I
I
I
I
I
I
I
II L _______ Figure 6.1.1.-Program PKGLINE, Part II

 

 

135

 

 

 

I

 

 

 

 

 

UPD(J) - as

L -------- Q

[Itint ‘1', ICP“), NHPU), UPD(I). 210(1). ‘1'?qu

 

 

 

 

I

I

I

I

I I

, I

I I

II

I I

: I

' I

| I
I

I

I

I

I

I

I

I

 

Figure 6.1.1.—Progran PKGLINE, Part III

r—----I

I
I
I
I
I
I
I
I
I
I
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I
I

sun

36

 

[Dimension HD(5) , MDOWINS) ,NTO‘NS) .NXfl'é

 

 

Eat. N'X(1,K),(2,K) ...(7,K), NTOT]

  

 

 

 

 

 

MIMIC) - MDIK) + 1

 

 

 

 

Q
MINTOTGQ *
Ranf(0.5) + 1.0

 

 

 

 

 

 

Figure 6.1.2.-—Subroutin¢ Donn. Part I

    

’°’ II> sumo

 

 

 

37

 

 

 

 

”Figure 5.1.2.-—-Flow Chart for Subroutine DOW, Part II

38

NHX: array necessary for PLINE subroutine
DELT & T: time in minutes

N: number of machines in the line (5)

LP: total line production
NC: number of packages to exit machine and

enter conveyor

NH: number of packages to exit conveyor
and enter machine

The variable names used in DOWN:

DOWN: stores the time in minutes the machine
is down
NTOT: running time from which random.se1ector

picks downtime
NX: two dimensional array used in matching
length of downtime to machine
In the computer numerous computations occur in a
simulation run, the number of which depends on the length
of the run. In this case when simulating a beverage line
for a greater period of time than one eight hour shift,
20 minutes of downtime must be allowed for the entire line
for lubrication.
Assumptions used in the program:

1. Perpetual infeed to the conveyor entering
the depalletizer. . '

2. Infinite storage space available to accept
all the output from the traymaker.

3. All downtime is attributable to the five
stations. The conveyors are trouble free.

39

In the actual downtime data studies, large
downtimes not characteristic with normal
downtime problems were ignored.

The incremental revenue on a single unit is

assumed to be $0.03. To simplify matters

in this paper, the entire amount will be ap-
plied to the capital investment in the con-

veying mechanisms.

CHAPTER VI

PROOF OF THE COMPUTER PROGRAM

Four models are examined:

1. A model with infitied (500,000 units)
conveyor lengths between the stations.

2. A model with very small (140,140,120,140,
160,160) conveyor lengths between the
stations. These lengths correspond to
the larger machine capacities at either
end of each conveyor.

3. A model with different size conveyor
lengths between the stations. It will
try to optimize output production at the

‘lowest cost of conveying mechanisms.

4. Similar to model three but with the
addition of the subroutine PLINE.

In each model the cost versus production aspect
will be examined.

The optimum.conveyor capacities could be found
with a saphisticated trail and error subroutine, such as

Rosenbrock's HCLMB.11

However, the routine requires
numerous multiple runs and its cost makes it prohibitive

for this exercise. If one were designing a real assembly

 

11H.H. Rosenbrock and C. Storey, Computational
Techniques for Chemical Engineers (New York: Pergamon
Press, 19667, pp. 64-63}

 

40

41

line for a company this subroutine would be vital to pin-

pointing the optimum conveyor lengths.

6.1 Proof Model 1

 

This model forms a coupling of the five machines
in a series as shown in Figure 6.1. It is designed with
Very large (500,000 unit) conveyor lengths or reservoirs
between the machines. Each reservoir is half full of
units at the beginning of the simulation run.

Because of the very large reservoirs each machine
operates independently of line capacities and downtimes
of the others. 'There is no idling time due to the in-
ability of the entering conveyor to transport units (cans)
or the existing conveyor to accept them. Therefore, each
machine is expected to yield its full catalog efficiency
of operation and the line efficiency will equal that of
the last machine.

The simulation result of the proof model is shown
in Table 6.1. Each machine turns over its stock of units
every six seconds or ten times every minute. At the end
of each minute there is a summary of the current status

of each entering conveyor and corresponding machine.

Example:
TIME Cl Ml D1 Pl ........ C5 M5 D5 P5 TOTAL
TIME: the time in minutes.
C1...CS: indicates the number of units on the

conveyor as it is about to enter the
station (depalletizer, filler, seamer,
packer, traymaker).

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TABLE 7.l.--Proof MOdel 1, Part I

44

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TABLE 7.1.--Proof Model 1, Part II

45
M1...M5: indicates the number of units currently
in the machine.
Dl...D5: shows whether the machine is operating
or not. Example: ‘
UP 0 indicates the machine is
operating.
DN 1,2,...7 signifies the number of
minutes the machine has been down.
P1...P5: indicates the total production of the
station at the end of that particular
minute.

TOTAL: total line production.

Only the first two pages of the simulation are
presented here. It is felt these will show the results
equally as well as the entire printout and much less
- space will be required.

Comments about Table 6.1:

1. The efficiency of the line is assumed to be
approximately 80%.

2. The efficiency of each machine, excluding
idling time can be found by adding the downtimes of each
over the eight hour simulation run, subtracting each from
eight hours and figuring the result as a percentage of

the total shift.

 

Examples:
A. Depalletizer Total Downtime = 85 minutes
480 min - 85 min _
480 min _ X 100 _' 82.29270
B. Filler Total Downtime = 101 minutes
480 - 101

 

_180 x 100 = 78.9581

46

C. Seamer Total Downtime 94 minutes

 

480a5094 x 100 = 81 875%
D. Packer Total Downtime = 87 minutes
48045087 x 100 = 81.875%

 

E. Traymaker Total Downtime 118 minutes

480 - 118
480

x 100 = 75.417%
The overall line efficiency, based on machine
downtimes is

82.222 j 78.958 + .5 .......... . = 79.792%,

 

This figure is very close to the expected 80%.
' Another way of showing line efficiency is based
on the number of units produced over time. For this first
'model, efficiency is gauged on the last machine, the

traymaker.

Total Production:
Length of Run:
Last Machine:

185,280 units
150 minutes

1600 cpm (cans per minute)

185,280 units
150 min x 1600 cpm

 

x 10 % = 77.2%

3. Filler idling time is as small as possible.
Idle time as a percent of total running time is:

19 min
150 min

 

x 100% - 12.7%

This idle time is due to the 6 second start-up time step

of the filler while it is being loaded with cans.

47

4. Conveyor cost versus production:

Conveyor Capacity: 500,000 units/conveyor
Each Unit (can): 2.5 inches in diameter
Cost of Conveyor: $100.00 per running foot

Capital Cost:
SGIIDOIIfits

 

2. ° .
conveyor x 5 conveyors x {71% x EsmxflgE-QQ = $52,083,333

Total Output: 185,280 units (cans)
Incremental Revenue
per unit of

Output: $0.03
Length of Run: ' 150 minutes
Incremental

Profit:

480 min $.03

185,280 units X m1n X Ell—1?

= $16,786.88/eight hour shift

Necessary time to return investment with 3, eight hour

shifts per day working five days per week:

$52,083,333 x 1 shift x l_year
$17,786.88 3 shifts/day 250working days

 

 

= 3.9 N 4 yrs.

6.2 Proof Model 2
This proof model is the same as Mbdel 1 except for
the length of the transfer mechanisms between the machines.
These very small conveyor lengths (140,140,120,l40,160)
correspond to the larger machine capacities at either end
of each conveyor (Figure 6.2). The model run is started

with each conveyor half full.

48

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huflouauo vac: ova no

Announce use: osfl so
suaoaauo ans: oNH no
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ouaaamau . ouaawmau

. 6 ueaeaeaoao.

Il- .I‘II I III 1 II" I. III

49

Interaction between the machines occurs almost
100% of the time which means every breakdown of a machine
affects the operation of all the others. It is expected
that the efficiency of each machine and the entire line
will decrease markedly. The line efficiency for this
model is based on the slowest machine, the filler.

Table 7.2 shows the computer printout. Some
comments follow:

1. The efficiency of each machine, excluding
idling time is the same as that discussed in comment (2)
of Proof Model 1. The line efficiency based on the
. number of units produced is very low.
Total Production: 65,760 cans
Length of Run: 150 minutes
Slowest Machine: M(2)-Filler 1200 cpm

65,760 cans _
150 min x 1200 cpm x 100 " 35-53%

 

2. Machine idling time is large. Idle time as a

percent of total running time for the filler is

W x 1007. = 45.337.

This compares to zero idle time for Proof Model 1. Idle
time is defined as the number of minutes the filler did
not produce its full machine capacity during the simula-

tion run. Downtimes are ignored.

“II I ll

50

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TABLE 7.2.--Proof Model 2, Part I

51

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TABLE 7.2.--Proof Model 2, Part II

52

3. .The assumption that no units will enter or
exit a station ifits corresponding entering or exiting
conveyors are full is in evidence in this model. It can
be seen that the capacities and downtimes of each machine
materially affect the production of the machines before
and after them. A maximUm of 1400 and 1600 units per
minute can be carried on the entering conveyors to
machines 4 and 5, respectively. However, this quantity
never rises this high when both machines arerunning
because of the line bottleneck, the filler and seamer
machines. Although the packer and traymaker are capable
. of producing 1400 and 1600 units respectively, it is of

no consequence if the cans are not available.
4. From a cost versus production point of view:

Conveyor Capacity: 140+l40+120+l40+l60 = 700 units
Each Unit (Can): 2.5 inches diameter
Cost of Conveyor: $100.00 per running foot

Capital Cost:

2.5 in. 1 ft. x $100.0

700 units x ————T—

1 unit x m. “fir.— = “4583-33

Incremental Revenue:

Total Output: ‘ 65,760
Incremental Revenue per

Unit of Output: $0.03
Length of Run: 150 minutes

. 480 ’ 0.03 . .
65,760 units x ISU—gi%'x §GHIE'= $6,312.96/e1ght hour Shlft

53

Necessary time to recoup investment working 3, eight hour
shifts per day, five days per week:

$14,583.33 1 shift = , .
$6.3I2756'x 3 shifts/day 77 “Orklng daYS-

 

Mbdels l and 2 were examined more to prove that
the simulation model functions realistically as a high
speed assembly line, given the stated assumptions in
section six, rather than as a practical cost versus
production example. However, there are important aspects
to be noticed in this area. Mbdel I produced more units
but lost its profit in excessive capital investment in.

conveyor equipment. Mbdel 2 spent too little on capital
equipment and suffered from an inefficient line, loss of

production and lost market position.

6.3 Proof MOdel 3

 

Proof Models 3 and 4 are designed in an effort to.,

optimize production at the lowest capital investment in
conveyor systems. Mbdel 3 utilizes conveyor lengths of
10,000, 20,000, 15,000, 15,000, and 30,000 units (Figure
6.3). All four models are similar in that they are
arranged in a coupling of five machines in a series and
each simulation run is started with all conveyors half
full.

Some comments follow concerning Table 6.3:

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TABLE 7.3.--Proof Mbdel 3, Part I

 

56

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TABLE 7.3.--Proof’Mode1 3, Part II

57

l. The efficiency of each machine, excluding
idling time is the same as that discussed in comment (2)
of Proof Mbdel 1. The line efficiency based on the
filler is

Total Production: 160,200 cans

Length of Run: 150 minutes
Slowest Machine: M(2)-Filler 1200 cpm
160,200 cans x 100% _ 89%

 

I50 min x 1200 cpm

2. Filler idling time is as small as possible.

Idle time as a percent of total running time is:

19 min 100% = 12.7%
m.n x

This idle time is due to the 6 second start-up time step

of the filler while it is being loaded with cans.

3. In analyzing the operation of this model it
is found that the 20,000 can capacity of the conveyor
entering the filler never falls below 5,000 cans. This
section of conveyor forms a 'permanent' storage area for
cans. If the size of this conveyor is reduced by
g 5,000 cans it is logical to expect no loss in efficiency
from this machine. It was noted previously that the filler
is the heart of the high speed beverage line. It is of
paramount importance to keep this machine at optimum

efficiency.

58

The conveyor entering the seamer can handle 15,000
units per minute. The seamer itself produces 1200 cpm.
The only time its conveyor runs below this 1200 can
machine capacity is the 77.0 to 83.0 minute range. 'This
is due to a six minute downtime in the filler. The con-
veyor never accumulates more than 11,000 cans at any time
in the run, therefore a decrease in capacity to :_ll,000
cans is warranted.

The packer can handle 1400 cpm. Its conveyor in
this particular model can accommodate 15,000. Only in
the 83.0 to 86.0 minute time frame does it fill up. This
is due to a six minute jam in the packer. In the 123.0 to
130.0 minute range the machine shows idle capacity due to
lack of cans. A larger conveyor seems warranted in this
case. p

The traymaker is the fastest machine on the line,
handling 1600 cpm. Its feeding conveyor was set at a
30,000 unit capacity. The most cans it accumulates (except
at the startup) is 9,220 in the 141.0 minute of operation.
Although it has idle capacity for a total of 43 minutes
because of no cans, little can be done other than to in-

crease the size of conveyor C4 entering the packer.

59

4. Conveyor Cost and production figures:

Total Conveyor Capacity: 90,000 cans

Each Unit: 2.5 inches

Cost of Conveyor: $100.00 per running
foot ' '

Capital Cost:

90,000 cans x 24—549- x 1 ft x Egg-€99 = $1,875,000

can I2 in
Total Output: 160,200 cans
Incremental Revenue per
Unit of Output: $0.03
Length of Run: 150 minutes

Incremental Revenue:

480 .
160,200 cans x ISO—31173 x figzgi -= $15,379.20/eight hour shift

Necessary time to return investment working 3, eight hour
shifts per day, 5 days a week:

$1,875,000 1 shift 8 '
$I5737gTZU-x 3 shifts/day 40.7 m 41 days

6.4 Proof Mbdel 4
Proof MOdel 4 utilizes conveyor lengths of 10,000,
17,000, 11,000, 17,000 and 15,000 units (Figure 6.4).
An analysis of Table 6.4 follows:
1. The efficiency of each machine according to
accumulated downtime over an eight hour simulation run is
again the same as comment (2) of Proof Mbdel l. The line

efficiency based on the filler is:

Total Production: 152,700 cans
Length of Run: 150 minutes
Slowest Machine: 1200 cpm

60

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3. The conveyor CZ. entering the filler has been
reduced to a 17,000 can capacity. The filler still pro-
duces at its peak output but the cost of its conveyor has
been reduced by $62,500.

Conveyor C entering the seamer has been lowered

to 11,000 cans as :ecommended in.Mode1 3. The six minute
idletime in Mbdel 3 has increased to 20 minutes because
the simulation run started with only 4500 cans versus
7500 in Model 3. However, the most units C3 ever accumu-
lates is 8,980. A judgment is needed by management at
this point concerning conveyor investment as opposed to
machine idle time.

The packer has an entering conveyor capacity of
17,000 as compared to 15,000 in the last model. Only in
the 86.0 minute does it completely fill up. It shows
idle capacity in the 123.0 to 131.0 and 145.0 to 150.0
time frames. A.sma11 increase in this conveyor based on
changes in conveyor C3 should be experimented with.

Conveyor CS accumulates 53 minutes of idle time
as opposed to 43 in the previous model but its length

has been cut by one half.

64

4. Conveyor cost and production figures:

Total Conveyor Capacity: 70,000 cans

Each Unit: 2.5 inches

Cost of Conveyor: $100.00 per running foot

Capital Cost:

 

70,000 cans x 2'5 in 1 ft $100&00

_E§fi__.x 12 in x "—f__— = $1,458,333
This is a savings of $416,667 over Mbdel 3.
Total Output: 152,700 cans

Incremental Revenue per
Unit of Output: . $0.03
Length of Run: 150 minutes

Incremental Revenue:

480 min $0.03

152,700 cans x min x can

= $14,659.20/eight hour shift

Necessary time to return investment working 3, eight hour

shifts per day, 5 days a week:

$1,458,333.00 1 shift =
. x 3 Shifts/day 33°2'”33 days

5. Table 6.5 shows the simulation program.with
the addition of the subroutine PLINE. The regular program
calculates the position of the assembly line each six
seconds or ten times each minute and prints a numeric
summary of the status of the line at the end of each
minute of operation. With the addition of the subroutine
PLINE the line status is not only calculated each six
seconds but is also printed out in a unique visual 'picture',
one line for each six seconds. Due to the space needed by

this printout only six minutes can be shown per page. Two

(55

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TABLE 7.5._--Visual to Proof Model 4, Part II

67

pages are included for a total of 12 minutes. The print-
out for an eight hour shift would be lengthy but trends
in the line can be spotted very easily by virtually anyone.

Four symbols are used in this picture:
1. $
2. O (alphabetic symbol)
3. S
4. 0 (numeric symbol)

1. Each $ represents 1300 units moving on a
conveyor to a station. '

2. Each 0 (alphabetic symbol) represents 1300
units on a conveyor that are stationary. This is due to
a breakdown in one or more of the succeeding machines.

3. Each 5 represents 20 units (cans) that are
moving through the machine.

4. Each 0 (numeric symbol) represents 20 units
that are stationary in the machine. This machine is run-
ning but its exiting conveyor is full. The packages are
not moving due to_a breakdown in one or more of the suc-
ceeding machines. When a machine goes down all the units
it contains are emptied into the exit conveyor if this
conveyor has the space available to accept them.

In this model 1300 and 20 units are represented.
It is very easy to change the amounts to suit the user.

Each machine is distinguishable because of the

letter that follows its symbols:

 

 

68

A - depalletizer
B - filler

C - seamer

D - packer

E - traymaker

In analyzing the line during the-first minute of
operation machines M1, M2, ananz have gone down.
fMachine‘Ms is producing cans that move to conveyor 04
fand machine M5 produces units that are counted in the
total line production. Looking over the entire 12
minutes conveyor C2 entering the filler tends to decrease
its can supply. Conveyor C4 has become well stocked with
cans due to the six minutes of downtime in the packer and

the traymaker-(M5) soon exhausts its supply which never

rises above 140 cans in the 12 minutes.

CHAPTER VII

SUMMARY

A high speed beverage line is a complex system
.with many interacting components. It involves a variety
of expensive line machines and transfer equipment.
Because of this complexity and the variable nature of
the machines in terms of breakdowns and idle activity,
analytic techniques are inadequate in selecting or
designing the optimum.assembly line. Simulation models
then, are necessary in trying to predict the conse-
quences of changes in conditions, or methods without
having to spend the money or take the risk of actually
making the change in real life. MOdels are not perfect,
they cannot replace the real world, but they can reduce
a complex system, in this case a high speed beverage
line, to manageable proportions.

It can be seen from the Proof Models that there
is a trade off between conveyor equipment investment and
efficient line production. A larger capital investment,
however, does not necessarily mean a more efficient line
and vice versa. This balance is different for each comr
pany depending on the objectives and attitudes of its

higher‘management.
69

70

It was found, while talking to managers of two
different high speed brewing lines, that there is a
ready market for as much product as can be produced.

In this case then, the manufacturer would want to make
as efficient use of his packaging line as possible,
meaning increased conveyor investment. ‘Without the use
of a simulation program, such as this, a company could
easily invest more money than is necessary for effi-
cient production, thus tying up capital needlessly

which could be used to purchase other assets that would
better serve its goals in the competitive business arena.
‘Working closely with higher management and running several
test models similar to those shown in Proof Models 3 and
4, it is relatively easy to narrow down the ranges of
optimum.conveyor length to get the most for the capital
invested.

A computer program.for the simulation model has
been developed and, given the assumptions in section
six, has been proven to work successfully. If a high
speed packaging line can be described by Figure 3.1 or
any combined form of it, this simulation program will be

found useful in designing a more cost efficient line.

APPENDIX

71

in I' ‘Hi‘ ‘I. .

ll I!!! III. I lrk l I I l I I]. ll!‘ ll I'll

 

“ .Il'lilli'llll

 

APPENDIX

Figures Al, A2 and A3 show the actual computer
program" The main program, PKGLINE is a general repre-
sentation of a packaging line. As mentioned previously
the substitution of different numbers, sppeds, and
capacities of the machines as well as the conveyors is
relatively simple. The subroutine DOWN is more specific
and is accurate only for a high speed beverage packaging
line. The subroutine PLINE is a visual aid to under-

standing the workings of PKGLINE and DOWN,

72

 

f l! A."
ml! I!

 

73

PROGRAM PKGLINE(INPUT.OUTPUT’

ccccc ccccccccccccc

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3

TABLE Al.--Main Program.PKGLINE, Part I

74

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TABLE A2.--Main Program PKGLINE, Part II

75

SUBROUTINE DOWN (HD9T.N9

OCCnuCCE

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TABLE A2.--Subroutine DOWN.

 

 

r—a

all .’ Ill! .‘ Ila-Ill ‘III!

76

ONVEYOR IN rHE"ourPut

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T BNNELSOEOC 100502 A (..(..U9 50 (1 I. SUTZ
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8 TT)RTTPTH H... ZAIRAG MIHINI‘O (N =LT.( H(NTL
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S HRS 9T)NANN DDINRPFTHDHLHLCIL DSC DJHLIIUHSCLH
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2 1 1 1 11

CCCCCCCCCCCCC C C 6

TABLE A3.--Subrout1ne PLINE, Part I

77

yrs:

9
L
s
F
a 0

.0
1 N
U
0 R
9| 99R
1E
o “v
SCH L50
’A J , 82‘
SH) 9 T 1 9
9N0... E 9 9X
II o: L 5X0

fiNJ1-Hrr NNHU

99:9: "(NM IRRTD
LHFO=L00 ROOEN
SJIOHSCC PFFRE

on Dr...

55 12

11 22
3

TABLE A3.--Subroutine PLINE, Part II

BIBLIOGRAPHY

78

BIBLIOGRAPHY

American Can Co. The Canned Food Reference Manual.
New York: Rogers-Kellogg-Stillson,'lnc.,_l947.
Chapters 1, 2 and 6.

 

Arthur, William. "To Simulate or Not to Simulate: That
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Belshaw, Fran. "Computers Keep Products Mbving at
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p. 52. .

Bloedorn, William.H. "New Series: Improving Conveyor

Performance, Part I: Downtime--What Are the
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. "Part II: Chain Wear Strip Compatibility."
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Chorafas, Dimitris N. Systems and Simulation. New York:
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Church, Janis. Simulation as a Tool in Assembly Line
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"Equipment for Speed and Accuracy." PIRA Packaging
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Francis, Richard L., and John A. White. Facility Layout
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79

80

“Glass Bottling at New Speeds." PIRA Packaging
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- -Hall, J. R., and_R. L. Swaine. "Trends in the Carbonated
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Howard, A. J. Canning Technology. Washington, D.C.:
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Jacobs, Morris B. Manufacture and Analysis of Carbonated
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Jones, Gwendolyn. Packaging--A Guide to Information
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Judge, Edward E., and Son. The Directory of the Canning,
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Lamble, P. M. "High-Speed Filling of Carbonated Soft-
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Manetsch, Thomas J., and Gerald L. Park. System Analysis
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McMillan and Gonzalez. Systems Analysis~-A Computer
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Mullins, Paul E. "Two Packaging Breakthroughs from
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. ‘7' ‘4‘, .'

 

. ”Brewery Packaging Technology at M.S.U. in
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. "Continuous Mbtion Bottle Cleaner Operates
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. Heineken's New Brewery." Brewers Digest
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. "Preventing Materials Handling Bottlenecks."
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"Shrinkwrap Line Tray-Packs 6000 Cans a Minute."
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