Simulation of Tomato Processing Plant
by
Abasiano Udofa
An Engineering Project Submitted to the Graduate
Faculty of Rensselaer Polytechnic Institute
in Partial Fulfillment of the
Requirements for the degree of
MASTERS OF ENGINEERING IN MECHANICAL ENGINEERING
Approved:
_________________________________________
Ernesto Gutierrez-Miravete, Engineering Project Adviser
Rensselaer Polytechnic Institute
Hartford, CT
May 2014
© Copyright 2014
by
Abasiano Udofa
All Rights Reserved
ii
CONTENTS
Simulation of Tomato Processing Plant ............................................................................. i
LIST OF TABLES ............................................................................................................ iv
LIST OF FIGURES ........................................................................................................... v
ACKNOWLEDGEMENT ................................................................................................ vi
ABSTRACT .................................................................................................................... vii
1. Introduction.................................................................................................................. 1
1.1
Manufacturing Systems ...................................................................................... 1
1.2
Modeling Manufacturing Systems ..................................................................... 1
1.3
Discrete Event Simulation.................................................................................. 2
1.4
Tomato Processing Plant .................................................................................... 2
2. Methodology ................................................................................................................ 5
2.1
Mass Balance ..................................................................................................... 5
2.2
Deterministic vs. Stochastic Simulation ............................................................ 7
2.3
Queuing Networks Theory ................................................................................. 8
3. Model Set Up ............................................................................................................. 12
3.1
Assumptions ..................................................................................................... 12
3.2
Locations .......................................................................................................... 12
3.3
Entities and Arrivals ......................................................................................... 13
3.4
Processing and Routing .................................................................................... 13
4. Results........................................................................................................................ 15
4.1
Simulation Results ........................................................................................... 15
4.2
Recommendations ............................................................................................ 21
4.3
Simulation vs. Hand Calculations .................................................................... 22
5. Conclusions................................................................................................................ 23
6. References.................................................................................................................. 24
7. Appendix.................................................................................................................... 28
iii
LIST OF TABLES
Table 2.1: Distribution Functions ...................................................................................... 8
Table 2.2: Spreadsheet of Hand Calculations .................................................................. 11
iv
LIST OF FIGURES
Figure 1.1: Layout of the Tomato Processing Plant .......................................................... 4
Figure 2.1: Mass Balance for Tomato Processing Plant .................................................... 6
Figure 3.1: Locations ....................................................................................................... 13
Figure 3.2: Processing and Routing ................................................................................. 14
Figure 4.1: Time Plot for deterministic run ½ capacity ................................................... 16
Figure 4.2: Time Plot for deterministic run double capacity ........................................... 16
Figure 4.3: Time Plot for Stochastic Run ........................................................................ 17
Figure 4.4: Entity State for deterministic run ½ capacity ................................................ 18
Figure 4.5: Entity State for deterministic run double capacity ........................................ 18
Figure 4.6: Entity State for stochastic run ....................................................................... 19
Figure 4.7: Location Utilization for deterministic run ½ capacity .................................. 20
Figure 4.8: Location Utilization for deterministic run double capacity .......................... 20
Figure 4.9: Location Utilization for stochastic run .......................................................... 21
v
ACKNOWLEDGEMENT
I would like to first thank God for his grace and strength to study. I also want to thank
all of my teachers and professors I have had throughout my educational journey. A
particular note of thanks is given to my mother who had the idea of studying tomato
processing plants. I would also like to thank all of those who contributions to the field
of simulation I have used in this paper.
The books Modeling and Analysis of
Manufacturing Systems and Simulation Using ProModel provided an understanding of
manufacturing systems and the ProModel computer program. I would like to thank Dr.
Ernesto for his guidance through this process and his helpful insights on manufacturing
systems and ProModel. Lastly I would like to thank Dave Hoeppner for his assistance in
finishing this project.
vi
ABSTRACT
Tomato processing plants are large complex manufacturing systems that can often be
difficult to anticipate, plan, and react to the variability. Simulation is the imitation of a
dynamic system using a computer model. Simulation began to be used in commercial
applications in the 1960s. Now simulation is used by businesses to design, implement,
and optimize complex manufacturing and service systems. Simulation tools can be used
to gain further understanding of interdependencies and effects of variability to the
production of a tomato processing plant. This paper will discuss the build and run of a
tomato processing simulation using Pro Model a discrete event simulation software. The
results of deterministic vs. stochastic simulation will be discussed. The benefits of
simulation vs. hand calculation method will be presented.
Observations and
recommendations for the most beneficial use of simulation software in the tomato
processing
industry
have
vii
been
recommended.
1. Introduction
The purpose of this report is to demonstrate and discuss the modeling and analysis of a
tomato processing plant using the discrete event simulation program Pro Model. A brief
overview of manufacturing systems and how they are modeled computationally and
virtually will be addressed. A description of the tomato processing sequence of events
will be given for a better understanding of the system under study. The results of the
computer simulation will be evaluated against hand methods.
1.1 Manufacturing Systems
Manufacturing systems are processing systems where raw materials are transformed into
finished products through a series of operations performed at workstations.
These
systems consist of entities, activities, resources, and controls that define the parameters
for processing. Entities are the items being processed through the system. Activities are
the tasks being performed in the system. Resources are what is being used to perform
the activities. Controls dictate when, where, and how the activities are performed. The
resulting interactions of these elements are what make manufacturing systems complex
and difficult to evaluate. Interdependencies and variability are the two factors that make
up the complexity of manufacturing systems and make the behavior difficult to analyze
and predict. The manufacturing system under study in this paper is a tomato processing
plant.
1.2 Modeling Manufacturing Systems
Computation and simulation are the methods that have been used to try to model
manufacturing systems to understand the complexities and make responsible decisions.
Computation methods are extremely useful, but can be limited and inefficient with
larger, more complex systems. Simulation is a modeling and analysis technique used to
evaluate and improve dynamic systems of all types. Simulation is typically performed
on a computer utilizing various computer programs designed for capturing the behavior
of systems. Accurately predicting the performance of complex systems and having the
ability to test various scenarios before making major decisions that affect the system is
why simulation is important. Proper simulation accounts for interdependencies of a
1
system that cannot be obtained using other analysis techniques. This allows for risk-free
trial with no disruption to the current system and provides objective evidence to
substantiate changes to the system or guide in building a new system.
1.3 Discrete Event Simulation
How simulation works is dependent upon the method of simulation chosen.
The
common ways of characterizing simulation are static vs. dynamic, stochastic vs.
deterministic, and discrete event vs. continuous. Static simulation is one that is not timedependent while dynamic simulation is dependent on time. Dynamic simulations are
well suited for service and manufacturing systems.
Deterministic vs. stochastic
simulation has to do with the nature of the inputs and outputs. Deterministic simulation
has fixed inputs and outputs, while stochastic simulation has random inputs and outputs.
Deterministic simulation should always produce the same outcome no matter the number
of run times. Stochastic simulation requires several runs to get an accurate performance
estimate due to variations of outputs for a given run. Discrete-event simulation is based
on the tracking of events as they occur at distinct times during the simulation, while
continuous simulation is based on the tracking of events as they change continuously
with respect to time. Discrete-event simulations typically reflect many manufacturing
and service systems. The parameters chosen for the simulations used for this project are
dynamic, discrete-event, both stochastic and deterministic.
These parameters best
represent tomato processing plants due to the change of state from solid tomatoes to
tomato paste.
Tomato processing plants are process-oriented systems, which are
represented by discrete-event simulation.
Pro Model was the software chosen to
complete this simulation. Pro Model is a powerful commercial simulation tool that is
designed to effectively model any discrete-event simulation processing system.
1.4 Tomato Processing Plant
A tomato processing plant take fresh tomatoes and turns them into paste by chopping,
heating, and removing the water from the tomatoes. Tomato processing plants are
complex manufacturing systems. The tomato paste manufacturing system is a flow line
also known as a process layout. The tomatoes move along the same sequence shown in
2
Figure 1.1.
The tomatoes are dumped into the receiving stations from the trucks
transporting them. The receiving station has 3 to 5 greater times the amount of water
then the weight of the tomatoes for cleaning purposes. The tomatoes are routed to the
washing/sorting station next. Having been washed under a clean water spray the unfit
tomatoes are picked out and sent to scrap while the rest of the tomatoes are sent to the
chopping station. At the chopping station the tomatoes are chopped by hammer chopper
or a special mono-pump where they are chopped (broken and pulped). The pulp is preheated to 85-98°C for Hot Break processing. The pulp is then sent to the hot break
station consisting of a de-juicing unit (or juice extractors) composed of two centrifugal
stations: a pulper and a refiner, equipped with two sieves having different meshes. The
first sieve treats solid pieces up to 1 mm (or more), while the refiner processes solid
pieces up to 0.6 mm (or more). Two products therefore come out of the de-juicing unit:
refined juice ready for concentration and waste which is sent to scrap. The entire
concentration process (evaporation) takes place under vacuum conditions and at low
temperatures, significantly below 100°C. Product circulation inside the various
concentric tubular exchangers is carried out by special stainless steel pumps which are
designed to ensure that the product is conveyed inside the exchanger tubes at enough
speed to avoid “flash evaporation” (burning the tomato paste). The concentrate is sent
from the evaporator directly inside the aseptic system tank where it is packaged.
3
Figure 1.1: Layout of the Tomato Processing Plant
4
2. Methodology
This section explains methodology used for the hand calculations. A conservation
of mass for the system is completed first. Given the arrival time of the tomatoes and the
assumed service rates of the locations the utilization rate, effective arrival time,
throughput time, and entities in the system are calculated. This information will help to
guide the building of the simulation. The hand calculations will be used as a comparison
to the outputs of the simulation.
2.1 Mass Balance
When building simulation models the first step is model conceptualization.
In
conceptualizing a tomato processing plant a mass balance is necessary to know how
entities are flowing through the plant. Although tomato processing plants change the
state of tomatoes to paste there still must be a conservation of mass. This model was
based on a plant that processes 1000 kg of tomatoes/day. This means 41.660 kg/hr are
being discharged into the system. One can assume that the tomatoes being discharged
were handpicked so we can expect ~2% waste between the receiving station and the
sorting station. The tomatoes next go to the chopping station then from the chopping
station to the hot break station. We can expect ~3% losses between the tomatoes going
from the hot break station to the evaporator. The evaporator station will evaporate ~84%
water at this station and the resulting paste will be sent to the aseptic filler. Figure 1.1
below
shows
a
breakdown
of
the
5
mass
balance
described
above.
Figure 2.1: Mass Balance for Tomato Processing Plant
6
2.2 Deterministic vs. Stochastic Simulation
Both deterministic and stochastic simulations have been used for the modeling of
processing plant. Deterministic simulation contains no input components which are
random while stochastic simulation contains one or more input components which are
random. Due to this fact stochastic simulation produce outputs that are random and may
not be repeatable and deterministic simulation all future states are determined based on
the input data. Stochastic systems have probability distributions to generate random
variables. A random variable is a function that assigns a value to every outcome of a
random experiment. The random variables for our purposes are continuous. The
continuous random variable X is defined by its probability density function f(x). The
probability that the continuous random variable X takes on a value in the interval [a,b] is
defined by equation 1 below.
𝑏
𝑃(𝑎 ≤ 𝑥 ≤ 𝑏) = ∫𝑎 𝑓(𝑥)𝑑𝑥
[1]
The exponential distribution is used for the arrival time of the tomatoes. The probability
density function is defined by equation 2 below.
𝑓(𝑥) = 𝜆𝑒 −𝜆𝑥
[2]
The exponential distribution is commonly used for interarrival times because the
preceding event does not affect the following event. It contains the “memoryless”
property. The normal distribution is used for the processing times the probability density
function is defined by equation 3 below.
1
1 𝑥−𝜇 2
)
𝜎
𝑓(𝑥) = 𝜎√2𝜋 𝑒 2(
[3]
Normal distributions are typical used for time waiting to perform a task. The simulations
scenarios ran will highlight further the advantages and disadvantages of both types of
simulation as it pertains to tomato manufacturing plants. The distributions functions are
built into Pro Model and generate random variables based on the specified distribution.
Table 2.1 below shows the distributions and Pro Model expressions.
7
Table 2.1: Distribution Functions
Pro Model
Expression
Distribution
Exponential
E(mean)
N(mean, std.
dev.)
Normal
2.3 Queuing Networks Theory
A queuing network consists of one or more locations which provide some service or wait
for service at a queue when locations are busy. Queuing networks theory is the basis for
the calculations utilized to model the system. Queuing networks provide good estimates
for the characteristics addressed in simulation. “Queuing theory is the science of waiting
lines” (Harrell, 41). Understanding and correctly utilizing the queuing networks was key
to correctly building the hand calculation and computer models for the tomato factory.
The tomatoes arrival and service rate are stochastic (as mention in section 1.3 stochastic
has random inputs and outputs). Therefore to appropriately model the system random
numbers were used. Poisson arrivals, exponential distributions, and first come first serve
(FCFS) service provides the proper parameters for the hand calculations.
The
distributions of interarrival and service times are exponential. Exponential and Poisson
distributions are Markovian distributions where the future probability characteristics in a
system which a stochastic process is taking place depends on the state of the system at
the current time.
FCFS service has been assumed for this type of system.
The
commonly used abbreviation for queuing systems has the form A/B/s where “A” is the
type of interarrival distribution, “B” is the type of service time distribution, and “s” is the
number of servers. The tomato processing plant uses an M/M/6 queuing system. The
arrival rate is represented by (the service rate is represented byand the number
of servers represented by (c). The utilization factor () equals the arrival rate divided by
the number of servers times the service rate.
𝜌 = 𝜆/𝑐𝜇
[4]
8
Calculations were also based performed based on Little’s Law. Little’s law states the
expected number of entities in the system (L) is equal to the arrival rate (times the
throughput time (W).
𝐿 = 𝜆𝑊
[5]
where:
𝑊 = 1/𝜇(1 − 𝜌)
[6]
Open networks are systems consisting of multiple interconnected workstations typically
having jobs moving between pairs of stations according to some routing scheme. The
tomato processing plants consists of network workstations and was solved as an open
network. Open networks admit jobs from the outside world, which are then routed along
the network.
The following properties of stochastic systems are applicable to open networks.
1. The sum of independent Poisson RV is Poisson
2. If rates are Poisson inter-arrival times are Exponential
3. Inter-departure time from an infinite capacity M/M/c system is exponential.
The inter-arrival time (𝜆′) is defined by the arrival time multiplied by the probability of
the entity transferring to the next station (p).
𝜆′ = 𝜆𝑝
[7]
The three steps procedure used for analyzing this open queuing network are:
1. Determine effective arrival rates
2. Analyze each station as if it were alone
3. Aggregate the results over the network.
The expected throughput time is found by aggregating the results over the network.
𝑊 = 𝑊𝑗 𝜈𝑗
[8]
where:
𝜈𝑗 =
𝜆′
[9]
𝜆
A spreadsheet was created with the solutions of these steps and can be seen in table 1
below. Using open network theory for the hand calculations of this tomato processing
9
plant it states that the throughput time is 7.76 minutes and there are 77.59 jobs in the
system. The hand calculations also give the utilization rate for each location below.
10
Table 2.2: Spreadsheet of Hand Calculations
Locations
Arrival Rate
(batches/min)
Service Rate
(min)
Probability
Eff. Arrival
Rate
Receiving Sorting Chopping
Hot
Evaporating Packaging
Break
10.00
10.50
10.00
12.00
12.00
12.00
10.00
1.00
0.98
1.00
0.97
0.16
1.00
10.00
9.80
9.80
9.51
1.52
1.52
0.95
0.98
0.82
0.79
0.13
0.15
1.00
0.98
0.98
0.95
0.15
0.15
2.00
5.00
0.45
0.40
0.10
0.12
Expected
Throughput
Time (min)
2.00
4.90
0.45
0.38
0.01
0.02
7.76
L (tomatoes)
20.00
49.00
4.45
3.81
0.15
0.18
77.59
(min)
Utilization
Rate
Aggregate
Throughput
Time (min)
11
3. Model Set Up
3.1 Assumptions
Several assumptions were made in the development of the simulation due to some of the
limits of the simulation software. The entities arriving to the system are assumed to be
in batches. The arrival rate is 10 batches per minute. 1 batch contains 60 tomatoes,
which means that there are 600 tomatoes/min. In reality, there are multiple sorting
stations and people monitoring and adjusting the conveyor speeds for entrance into the
plant. For this simulation there was only one sorting station and the conveyors were not
given a speed. These assumptions are good assumptions because instead of a conveyor
with speed queues were utilized where you could hold the tomatoes until the location is
open. The service rates of the machines were assumed based on the desired per hour
production output. In reality there is more variability with the service times of the
machines and the assumed service times would be validated by data. Tomato processing
plants use water and steam as resources for processing. These resources were not added
to this model because it was outside the scope of this project. There is an added location
called scrap where all the waste is routed within the system. In reality there are flow
lines that will route the scrap out of the system. Pro Model version 8 was used to model
the system.
3.2 Locations
Figure 3.1 below shows the layout with all the locations. The queues, the receiving, and
the scrap locations have infinite capacity. The washing sorting, chopping, hot break,
evaporation, and packaging locations have capacities of 10 each.
12
Figure 3.1: Locations
3.3 Entities and Arrivals
The only entities in the system are tomatoes. The tomatoes arrival time for the two
deterministic runs are 5 batches/min and 20 batches/min. The arrival time for the
stochastic run is an exponential distribution with a mean of 10 batches/min. Each batch
contains 60 tomatoes.
3.4 Processing and Routing
The processing times for the deterministic runs are 9 minutes for every location except
for the queues, scrap, and packaging. The queues do not have a processing time and
entities are routed to exit from the scrap and packaging location. For the stochastic run
the processing times for every location is a normal distribution with a mean of 9 minutes
and a standard deviation of 1. The routing is arranged for a serial flow through the
system with a queue between every location. It is important to note that the routings
account for the scrap during processing through the probability command. From the
receiving station to the washing/sorting station, 100% of the entities were successfully
processed. From the washing/sorting to chopping station, 98% were successfully routed
13
through the station while the remaining 2% sent to scrap. From the chopping station the
hot break station, 100% of the tomatoes were successfully routed through the system.
From the hot break to evaporation station 97% of the entities entering were routed while
3% were sent to scrap. From evaporation to packaging station 16% are routed to be
packaged while 84% were sent to scrap. The 84% consists of all the water that is
removed for the paste to be made. Figure 3.2 shows the routing scheme in Pro Model.
Figure 3.2: Processing and Routing
14
4. Results
This section will discuss the results obtained from the three simulations which had two
deterministic runs and one stochastic run. The deterministic runs varied the amount of
tomatoes arriving in the system. The first run sent half the capacity and the second run
sent double the capacity. The stochastic run used an exponential distribution for the
arrival time and a normal distribution for the processing time of all the locations except
the queues. Data for the locations utilization, entity states, and time plot tracking the
arrival of the tomatoes will be discussed. The section also includes recommendations on
improvements in the simulation of tomato processing plants. The discussion ends with a
comparison of the simulated resulted to the hand calculations for accuracy of
predictability. All of the simulation runs were done based on a twenty four hour day.
4.1 Simulation Results
Figure 4.1-4.3 shows the time plot for the receiving location for the deterministic run
with half and double capacities, then the stochastic run respectively.
For the
deterministic runs the graph shows a constant number of tomatoes are being receiving at
the processing plant.
This is what we expected to see because of the nature of
deterministic simulation. Figure 4.3 shows the variability in the amount of tomatoes due
to the exponential distribution of the arrival time.
15
Figure 4.1: Time Plot for deterministic run ½ capacity
Figure 4.2: Time Plot for deterministic run double capacity
16
Figure 4.3: Time Plot for Stochastic Run
Figure 4.4-4.6 shows the state of the tomatoes in the plant for the deterministic run with
half and double capacities, then the stochastic run respectively. The tomatoes will either
be in operation, waiting in the queue, or blocked from entering because a location is full.
Figure 4.4 shows that all tomatoes are in operation and the tomatoes will not be held in
the queues. This is what was expected since only half the capacity is being received at
the plant. Figure 4.5 shows that ~84% of the tomatoes in the plant are waiting in the
queues.
This is consistant with expectations because double the capacity is being
received at the plant. Figure 4.6 shows that ~82% of the tomatoes are in operation, ~26%
are in the queues, and ~2% of tomatoes were blocked due to an operating location being
full. This means that the plant can access the arrival time or speed up the processing
times of the machines to eliminate the blockages.
17
Figure 4.4: Entity State for deterministic run ½ capacity
Figure 4.5: Entity State for deterministic run double capacity
18
Figure 4.6: Entity State for stochastic run
Figure 4.7-4.9 shows the utilization of each location in the plant for the deterministic run
with half and double capacities, then the stochastic run respectively. The locations will
either be full, empty, or occupied. Figure 4.7 show that none of the locations are full
which is consistent with the other results that have been shown on this deterministic run.
Figure 4.8 shows that the washing sorting location is 100% full and queue 1 is occupied
100% of the time. This is because the plant is receiving double the capacity. The
chopping and hot break locations are full ~77% of the time. The evaporation location is
full ~52% of the time. These locations are not full 100% of the time like the washing
sorting location because portions are being sent to the scrap location as outlined in the
processing and routing section. Figure 4.9 shows that no location is full 100% of the
time which indicates that the plant is not at capacity. The processing locations and
queues show that they are being utilized. This stochastic run provides insight on how
the plant would run if this exact conditions but if there is variability it appears that the
locations still have margin to handle fluctuations.
19
Figure 4.7: Location Utilization for deterministic run ½ capacity
Figure 4.8: Location Utilization for deterministic run double capacity
20
Figure 4.9: Location Utilization for stochastic run
4.2 Recommendations
The simulation of a tomato processing plant can be further refined to provide the
maximum benefit to the industry.
Any tomato processing plant looking to use
simulation should take statistics of their specific plant to minimize uncertainty from
assumptions. Statistics should be taken for the arrival rate of tomatoes to the plant, the
processing times at each location, and the interarrival times of the tomatoes. The
statistics can then be fitted to the most accurate distribution function and whether
deterministic or stochastic simulation should be done. The importance of this has been
seen in section 4.1. Information about the amount and flow of resources can also be
added to the simulation to determine the effects on production. Resources needed for a
tomato processing plant are water, steam, and power.
21
4.3 Simulation vs. Hand Calculations
The open network queuing theory hand calculations performed in section 2.3 provided
some helpful insight but the Pro Model simulation would be the preferred method due to
the complexity of this system. Pro Model simulation allows for good decision in the
shortest time possible. The hand calculations predicted the utilization rate for each
location but the Pro Model simulation showed the rate of utilization as well as how it
was being utilized empty, full, or occupied. The built in distribution functions allowed
for quick changes and evaluations on the effects which would not be possible with the
hand calculations. The ability to model queues in the simulation is insight that the open
network theory would not be able to depict accurately. The throughput time and entities
in the system were provided through the hand calculations to use these results to predict
the results after a twenty four hour day would have questionable accuracy based on the
unaccounted for uncertainties.
The hand calculations are good in providing rough
solutions but would fail to provide the accurate solutions needed for a dynamic system
such as the tomato processing plant.
22
5. Conclusions
Discrete event simulation is valuable and should be utilized in the tomato processing
industry. Discrete event simulation accurately converted the activities performed in a
tomato processing plant to time triggered events and consequent reactions which were
chronologically processed. With the many uncertainties that arise from variability and
interdependencies of a tomato processing plant simulation can be used for production
planning, scheduling, and guide decision to increase productivity. This simulation
showed the effects on choosing the correct parameters when modeling manufacturing
systems. These parameters are static vs. dynamic, stochastic vs. deterministic, and
discrete event vs. continuous. Figure 4.1-4.3 showed how the difference between
deterministic runs and a stochastic run with continuous vs. random distribution. The
effects on the system performance were seen in figures 4.4-4.6 showing the entity states
and in figures 4.7-4.9 showing how the locations were being utilized. Collecting data
statistics about the processing plant and converting that into the proper distribution
functions is important to accurately capturing the system dynamics. Simulation is the
preferred method rather than hand calculation for complex systems such as tomato
processing plants. Although performing hand calculations may be initially cheaper the
potential benefits of simulation can have more financial pay offs for companies than
hand calculations.
23
6. References
1. Askin, Ronald and Charles Standridge. Modeling and Analysis of Manufacturing
Systems. New York: John Wiley & Sons, Inc., 1993. Print
2. Harrell, Charles, Biman K. Ghosh, and Royce O. Bowden Jr. Simulation Using
Promodel. New York: McGraw-Hill, 2004. Print
3. Gutierrez-Miravete, Ernesto. [Online] 18, January, 2001. [Cited: 5, November, 2013]
http://www.ewp.rpi.edu/hartford/~ernesto/C_S2001/mams/notes/mams02.html
4. Gutierrez-Miravete, Ernesto. [Online] 26, September, 2002. [Cited: 23, October
2013]
http://www.ewp.rpi.edu/hartford/~ernesto/C_F2002/DES/Notes/s04/s04.pdf
5. Fenco Food Machinery. [Online] [Cited: 25, April, 2013]
http://www.fenco.it/eng/tomato-paste-processing.asp
6. Whitney, Daniel E. [Online] 20, November, 2002. [Cited: 16, December 2013]
http://ocw.mit.edu/courses/mechanical-engineering/2-875-mechanical-assemblyand-its-role-in-product-development-fall-2004/lecture-notes/cls20_smltion04.pdf
24
********************************************************************************
*
*
*
*
*
*
*
*
Formatted Listing of Model:
C:\Users\udofaa\Downloads\deterministic-less.MOD
********************************************************************************
Time Units:
Distance Units:
Minutes
Feet
********************************************************************************
*
Locations
*
********************************************************************************
Name
Cap
Units Stats
Rules
Cost
--------------- -------- ----- ----------- -------------- -----------Recieve
Washing_Sorting
Scrap
Chopping
Infinite
10
10
10
1
1
1
1
Time
Time
Time
Time
Hot_Break
10
1
Time Series Oldest, FIFO,
Evaporation
Packaging
Queue1
10
1
10
1
INFINITE 1
Time Series Oldest, FIFO,
Time Series Oldest, FIFO,
Time Series Oldest, FIFO,
Queue2
Queue3
Queue4
Queue5
INFINITE
INFINITE
INFINITE
INFINITE
Time
Time
Time
Time
1
1
1
1
Series
Series
Series
Series
Series
Series
Series
Series
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
FIFO,
,
,
FIFO,
FIFO,
FIFO,
FIFO,
FIFO,
********************************************************************************
*
Entities
*
********************************************************************************
Name
Speed (fpm) Stats
Cost
---------- ------------ ----------- -----------tomatoes
150
Time Series
********************************************************************************
*
Processing
*
********************************************************************************
Process
Routing
Entity
Location
Operation
-------- --------------- ------------------
Blk Output
Destination
Rule
---- -------- --------------- ----------
tomatoes
tomatoes
tomatoes
tomatoes
1
1
1
tomatoes Queue1
FIRST 1
tomatoes Washing_Sorting FIRST 1
tomatoes EXIT
FIRST 1
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
Queue3
Queue4
Scrap
Queue5
Scrap
EXIT
FIRST 1
0.970000 1
0.030000
0.160000 1
0.840000
FIRST 1
Queue2
Scrap
Hot_Break
Chopping
0.980000 1
0.020000
FIRST 1
FIRST 1
Recieve
Queue1
Scrap
Chopping
Wait 9
Wait 9
Wait 9
tomatoes Hot_Break
Wait 9
1
1
tomatoes Evaporation
Wait 9
1
tomatoes Packaging
1
tomatoes Washing_Sorting Wait 9
1
tomatoes Queue3
tomatoes Queue2
1
1
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes Queue4
tomatoes Queue5
1
1
tomatoes Evaporation
tomatoes Packaging
FIRST 1
FIRST 1
********************************************************************************
*
Arrivals
*
********************************************************************************
Entity
Location Qty Each
First Time Occurrences Frequency Logic
-------- -------- ---------- ---------- ----------- ---------- -----------tomatoes Recieve 5
0
INF
10 min
25
Move Logic
------------
********************************************************************************
*
*
*
*
*
*
*
*
Formatted Listing of Model:
C:\Users\udofaa\Downloads\deterministic-more arrivals (1).MOD
********************************************************************************
Time Units:
Distance Units:
Minutes
Feet
********************************************************************************
*
Locations
*
********************************************************************************
Name
Cap
Units Stats
Rules
Cost
--------------- -------- ----- ----------- -------------- -----------Recieve
Washing_Sorting
Scrap
Chopping
Infinite
10
10
10
1
1
1
1
Time
Time
Time
Time
Hot_Break
10
1
Time Series Oldest, FIFO,
Evaporation
Packaging
Queue1
10
1
10
1
INFINITE 1
Time Series Oldest, FIFO,
Time Series Oldest, FIFO,
Time Series Oldest, FIFO,
Queue2
Queue3
Queue4
Queue5
INFINITE
INFINITE
INFINITE
INFINITE
Time
Time
Time
Time
1
1
1
1
Series
Series
Series
Series
Series
Series
Series
Series
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
FIFO,
,
,
FIFO,
FIFO,
FIFO,
FIFO,
FIFO,
********************************************************************************
*
Entities
*
********************************************************************************
Name
Speed (fpm) Stats
Cost
---------- ------------ ----------- -----------tomatoes
150
Time Series
********************************************************************************
*
Processing
*
********************************************************************************
Process
Routing
Entity
Location
Operation
-------- --------------- ------------------
Blk Output
Destination
Rule
---- -------- --------------- ----------
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
Queue1
Washing_Sorting
EXIT
Queue3
FIRST
FIRST
FIRST
FIRST
Queue4
Scrap
Queue5
Scrap
EXIT
Queue2
0.970000 1
0.030000
0.160000 1
0.840000
FIRST 1
0.980000 1
Scrap
Hot_Break
Chopping
Evaporation
0.020000
FIRST 1
FIRST 1
FIRST 1
Recieve
Queue1
Scrap
Chopping
Wait 9
Wait 9
Wait 9
1
1
1
1
tomatoes Hot_Break
Wait 9
1
tomatoes Evaporation
Wait 9
1
tomatoes Packaging
tomatoes Washing_Sorting Wait 9
1
1
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes Queue3
tomatoes Queue2
tomatoes Queue4
1
1
1
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes Queue5
1
tomatoes Packaging
FIRST 1
********************************************************************************
*
Arrivals
*
********************************************************************************
Entity
Location Qty Each
First Time Occurrences Frequency
Logic
-------- -------- ---------- ---------- ----------- ---------- -----------tomatoes Recieve 20
0
INF
10 min
26
1
1
1
1
Move Logic
------------
********************************************************************************
*
*
*
*
*
*
*
*
Formatted Listing of Model:
C:\Users\udofaa\Downloads\stochastic.MOD
********************************************************************************
Time Units:
Distance Units:
Minutes
Feet
********************************************************************************
*
Locations
*
********************************************************************************
Name
Cap
Units Stats
Rules
Cost
--------------- -------- ----- ----------- -------------- -----------Recieve
Washing_Sorting
Scrap
Chopping
Infinite
10
10
10
1
1
1
1
Time
Time
Time
Time
Hot_Break
10
1
Time Series Oldest, FIFO,
Evaporation
Packaging
Queue1
10
1
10
1
INFINITE 1
Time Series Oldest, FIFO,
Time Series Oldest, FIFO,
Time Series Oldest, FIFO,
Queue2
Queue3
Queue4
Queue5
INFINITE
INFINITE
INFINITE
INFINITE
Time
Time
Time
Time
1
1
1
1
Series
Series
Series
Series
Series
Series
Series
Series
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
Oldest,
FIFO,
,
,
FIFO,
FIFO,
FIFO,
FIFO,
FIFO,
********************************************************************************
*
Entities
*
********************************************************************************
Name
Speed (fpm) Stats
Cost
---------- ------------ ----------- -----------tomatoes
150
Time Series
********************************************************************************
*
Processing
*
********************************************************************************
Process
Routing
Entity
Location
Operation
-------- --------------- ------------------
Blk Output
Destination
Rule
---- -------- --------------- ----------
tomatoes
tomatoes
tomatoes
tomatoes
1
1
1
tomatoes Queue1
FIRST 1
tomatoes Washing_Sorting FIRST 1
tomatoes EXIT
FIRST 1
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes
Queue3
Queue4
Scrap
Queue5
Scrap
EXIT
FIRST 1
0.970000 1
0.030000
0.160000 1
0.840000
FIRST 1
Queue2
Scrap
Hot_Break
Chopping
0.980000 1
0.020000
FIRST 1
FIRST 1
Recieve
Queue1
Scrap
Chopping
Wait N(9,1)
Wait N(9,1)
Wait N(9, 1)
tomatoes Hot_Break
Wait N(9,1)
1
1
tomatoes Evaporation
Wait N(9,1)
1
tomatoes Packaging
1
tomatoes Washing_Sorting Wait N(9,1)
1
tomatoes Queue3
tomatoes Queue2
1
1
tomatoes
tomatoes
tomatoes
tomatoes
tomatoes Queue4
tomatoes Queue5
1
1
tomatoes Evaporation
tomatoes Packaging
FIRST 1
FIRST 1
********************************************************************************
*
Arrivals
*
********************************************************************************
Entity
Location Qty Each
First Time Occurrences Frequency Logic
-------- -------- ---------- ---------- ----------- ---------- -----------tomatoes Recieve 10
0
INF
E(10) min
27
Move Logic
------------
7. Appendix
Below is the mass flow chart that the simulation was based on. It is from Fenco Food
Machinery.
28