Simulation of production systems: A case study for stainless steel insulated coffee cup...

SIMULATION OF PRODUCTION SYSTEMS

SIM’S COFFEE CUPS - PROJECT REPORT

Meng Li & Hafez Shurrab

Date

2015-01-12

ABSTRACT

In this report, a production system of a coffee cups factory is used as a case for simulation

modelling and productivity optimization. The study is based on Bank’s simulation project

methodology to systematically approach optimum results, while the theory of constraints (TOC)

is used as the main framework for the iterations of the production system improvements. The

findings prove how simulation modelling and TOC can improve the productivity of the

production system dramatically by surgically aligning investments to even out the system

capabilities.

TABLE OF CONTENT

1. INTRODUCTION _________________________________________________________ 4

1.1. Background __________________________________________________________ 4

1.2. Problem Formulation ___________________________________________________ 4

1.3. Research Questions ____________________________________________________ 4

1.4. Delimitation __________________________________________________________ 4

2. THEORETICAL FRAMEWORK _____________________________________________ 5

2.1. Discrete-Event Simulation - DES _________________________________________ 5

2.2. Simulation Project Methodology __________________________________________ 6

2.3. Theory of Constraints - TOC _____________________________________________ 7

2.4. Detection of Bottlenecks ________________________________________________ 8

2.5. Experimental Plan and Statistical Analysis __________________________________ 9

3. METHODOLOGY ________________________________________________________ 9

4. PROJECT REALISATION: ________________________________________________ 10

4.1. Identify the constraint__________________________________________________ 11

4.2. Exploit the Constraint and Subordinate Everything Else _______________________ 12

4.3. Elevate the constraint __________________________________________________ 13

4.4. Iterate ______________________________________________________________ 14

5. PROJECT RESULTS _____________________________________________________ 15

6. DISCUSSION ___________________________________________________________ 17

6.1. Extra Task A_________________________________________________________ 18

6.2. Extra Task B _________________________________________________________ 19

6.3. Extra Task C _________________________________________________________ 19 7. CONCLUSION __________________________________________________________ 21

REFERENCES ______________________________________________________________ 22

APPENDICES ______________________________________________________________ 23

1. INTRODUCTION

1.1. Background

The majority of manufacturing firms improvises in their strategy of launching new product

development projects and engineering change projects leading to dramatic failure cases of

competition and market acquisition. Many decisions toward costly investments related for

instance to space and layout are difficult to be made with long discussions and many prototypes.

Therefore, the simulation tool turned out to be handy for this era of production systems since

many aspects could be fairly analysed and lots of costly and risky conduction of real production

piloting could be avoided until getting a fairly clear perception on which combinations of

production system components could dynamically fit the current and strategic business needs.

The current situation of the production system at Sim’s Coffee Cups could not be an exception

for how coherently the production capacity is linked with the current market demand. The

problem is described in details in the following section. In this report, we show the benefits of

simulation as a tool for production systems to confront market dynamics through facilitating

solid insights into effective operational and strategic decisions including future investments.

1.2. Problem Formulation

As it is common for many manufacturers, Sim’s production capacity is not properly leveraged,

and the dramatic increase in the demand of the customizable painted coffee cups forged a new

pressure on the business due to the possible negative influence on customers because of poor

deliverability. The marketing department expects even more demand in the near future, while

the current production system regularly experiences many disruptions due to the lack of

balanced resources in the production site. For further accurate investigations on the current

situation of Sim’s production system, we use simulation to dig deeper and deeper in order to

diagnose the system deficits and corresponding changes and investments in which the

throughput can be greatly increased to fulfil the growing demand.

1.3. Research Questions

This simulation project is done to investigate possible solutions to increase Sim’s production

throughput through particular system reconfigurations and possible investments without

exceeding 150 000 € budget.

The main questions that will be answered are:

1. which system constraints are currently limiting or have high restricting potentials toward

the overall production capacity?

2. which changes on the current system configurations and plans can lead to improved

throughput?

3. what effective investments to be realised in both the short and long terms are there to

balance out the overall resource utilization and increase the production throughput as a

result?

1.4. Delimitation

In principle, the simulation study of Sim’s production system is discrete-event oriented whereby

stochastic time driven data are mainly considered when they fit and are available. In other

words, the resource data are expressed as time durations as loads pass by, and only limited

resource characteristics including quality rates and maintenance activities are connected, while

the other specific functionalities are not considered. Furthermore, this simulation study includes

the flow of materials and resource behaviour from the raw materials’ storage point (including

planning and scheduling rules) to the point where coffee cups are assembled and stored to

outgoing logistics activities. The logistics activities after assembly in turn are not simulated or

included in the study. Additionally, there are particular investments for improvements (see

Appendix A) represent the only space of solutions in which different scenarios are drawn and

alternatives are combined together.

Automod software is used to model Sim’s production system with limited graphical

representations, while Autostat is mainly used to analyse system variables in relation to

particular responses, e.g. throughput. Finally, all processes’ data are already condensed into

corresponding distributions expect painting machines, which has raw data of MTTF and MTTR

ready to be fitted to corresponding distributions.

2. THEORETICAL FRAMEWORKIn this project, many concepts and theoretical fundamentals are used as ground and supporting

factors to the related decisions and are detailed in the following sections:

2.1. Discrete-Event Simulation - DES

Simulation is defined by Ingalls (2008) as the process in which a dynamic model of an actual

system is designed and experimented with the aim of understanding the system behavior and/or

acting accordingly in terms of strategy setting and decision making back up by particular

requirement specifications or criteria. As for Discrete-Event Simulation (DES), Robinson

(2004) describes its unique characteristics as a simulation modelling based on discrete sequence

of events in time whereby the state of the system is changed accordingly. Ingalls (2008) in turn

breaks down the main structure of any DES into primary and commonly used components

including entities, events and activities, global and system state variables, resources, a calendar,

a random number generator, and statistics collectors.

The calendar for a simulation is simply the scheduled list of events that will occur when

simulation runs take place. Since the application of DES is directed to the flow of a production

system in this case, entities could be for instance the loads created according to random or

scheduling rules, while events could be the scheduled activities on the calendar that can move or

hold entities for a certain/randomly distributed period of time. As for resources, they are simply

anything included in the system with limited capacity and possibility to do particular activities

(e.g. maintenance) in order to represent realistic behavior of labour and machines over time.

Realistic behavior is mainly derived from the ability to include stochastic formulas in DES,

which is generated by random number generators (Ingalls, 2008).

Global variables are those variable that are dedicated to the entire model at all times in order to

track especially interesting results such reorder point effect. System state variables are in turn

those variables related to the current state of simulation such as simulation time, which means

that they are continuously updated as simulation time goes by. Finally, statistics collectors are

those tracking features used to collect related statistics of the simulated model. A common

variable to track is for instance the utilization of a resource (Ingalls, 2008).

DES has both strengths and weaknesses. The first strength stems from the fact that simulation in

general supports experimentation without disturbing the real system of production. DES in

particular has the ability to process stochastic behaviour, and thus, consider system dynamics,

enabling quicker analysis and solution adapting. DES enables common perception of system

dynamics through its visual and pedagogical features. On the other hand, DES is time

consuming and requires a lot of critical data to be accessed. Above all, the results of DES may

give precise impression although there might have been invalid input data considered. Finally,

human decisions are most of the time difficult to model (Ingalls, 2008).

2.2. Simulation Project Methodology

Banks (2010) introduced the main steps commonly followed in simulation studies as shown in

figure 2.1. Problem formulation is the statement of the problem that clearly declares the main

problem in the current system. Sometime, the problem statement is reformulated as the project

progresses due to the new facts exposed while analysing the system. Then, the objectives of the

project plan are specifically set, which are in fact the questions answered by simulation such as

what alternatives there are and how much the change required will cost to improve the current

system in accordance with the project goal. After that, the model conceptualization phase comes

up whereby the essential features of the problem with affecting factors of relevance to the

problem scope are mapped as a connected network of simple, abstract and understandable

symbols that represent the relationships in the real system to characterize it and level down the

complexity. Simultaneously, relevant data is collected in a planned and organized way since it is

difficult to get acceptance within organizations to gather such data. Both the range and amounts

of data should be sufficient to efficiently represent the corresponding behaviour of the real

system entities. Therefore, this phase could be regarded as the most time-consuming phase

among other phases. After fairly representative data is available, the representative model

should be logically built and translated into relevant computer language/codes. However, it is

possible to model a system for simulation in many cases without actual coding, but the

flexibility will be compromised then. There are two types of programming languages including

general purpose simulation system (GPSS) language and special-purpose simulation software.

Arena® and AutoMod© are examples of the later type for manufacturing (production) and

material handling (logistics). Nonetheless, building models might be manageable but verifying

them require extra work. Therefore, models should be built in a way they could be quickly and

easily verified before they together with their conceptual models could be checked for being

valid representation of the real system (Banks, 2010). Figure 2.1 shows that the validation phase

can ask modellers to go back to the conceptual modelling and data gathering phase if the model

accuracy is not judged to be acceptable.

Once the model is fairly valid from any common perspective, experiments to be conducted

through the model should be properly designed so that they can eventually lead to improved

results in systematic manner (the following section clarifies this point in details) before the

production runs and analysis are implemented. Many simulation software embed statistics

experimentational features such as AutoStat in AutoMod and OptQuest in several simulation

software. The number of runs are stopped based on the degree of satisfaction of the achieved

results, commonly when very limited improvements are achieved over relatively long analysis

and run time. The achieved results are then then documented and reported using two main

types: program and progress. The earlier type is for clarifying the way the program operates in

order to gain support for and confidence in it, i.e. to be convincing for the target document

receivers. Besides, program documentation is expected to support determining the relationships

between performance output measures and input parameters for the earlier aspect to be

optimized. As for the progress report, they are not very different from any project progress

report that aim to avoid any significant delay in the project realisation. In general, project logs

provide a comprehensive record of key decisions, change requests, accomplishments, and other

important aspects. However, the analysis results and steps should be reported concisely and

clearly in a final report so that future model users can effectively review it during the

implementation phase. In principle, ultimate model users should be sufficiently involved along

the simulation project to effectively guide their contributions. Another similar and more

practical simulation project methodology was discussed by Rabi et al. (2008), see figure 2.1.

Figure 2.1: Steps in DES - in the right “source (Banks, 2010)”, in the left “source (Rabi et al., 2008)”

2.3. Theory of Constraints - TOC

Since simulation of production systems projects are done in order to improve the system’s

performance in terms of productivity, safety and ergonomics, cost and economy, and/or

environment impact, analysing the modelled system behaviour should be implemented in a

planned way according to a systematic approach (Banks, 2010). In this regard, Goldratt and Cox

(1992) highlighted a systematic way of approaching and tackling system constraints in a

productive manner. According to them, a constraint is any aspect in the system that undermines

its goal to be achieved, and they could be internal or external constraints. Constraints could be

internal when the system is incapable of delivering its value as the market demands. Therefore,

any improvements in the internal system where these constraints are located will improve the

deliverability of the system in relation to the market demand. Here, Goldratt and Cox (1992)

suggests what is commonly known as the theory of constraints (TOC), which is a management

paradigm whereby systems are perceived as being limited in achieving more of its goals by a

very small number of constraints, which are diagnosed, detected and continuously balanced out

through a series of iterated steps. These steps include:

1. identifying the system’s constraint(s),

2. deciding how to exploit the system’s constraint(s),

3. subordinating other resources to the constraint(s),

4. elevating the system’s constraint(s),

5. and going back to step 1 whenever the current constraint(s) is/are broken.

Goldratt and Cox (1992) believe the types of internal constraints could stem from 3 Ms, Man

(labour), Machine (equipment), and Management (policy). Each type could be a constraint by

preventing the system from delivering more than the current throughput is.

On the other hands, if the system has overcapacity, the constraint is said to be external, and the

organization should establish mechanisms to sell more of its products or services, which is more

of business strategists’ job (Goldratt & Cox, 1992).

2.4. Detection of Bottlenecks

According to Roser et al. (2003), the reason of having the production system

constraints/bottlenecks phenomenon is mainly the production flow losses including balance

losses (different designed cycle times between stations) and system losses (variations in

stations’ actual cycle times; production disturbances of breakdowns and small stoppages; and

interrelations between stations that may result in either blocked or starved status). Therefore,

they go to assume that these causes lead to one or combinations of possible basic indicators for

bottlenecks including:

1. high utilization,

2. shortest overall time of blocked or starved status,

3. high buffer levels upstream,

4. longest overall time of blocked resources upstream,

5. and longest overall time of starved resources downstream.

In DES, these indicators could be identified and gathered using buffer sizes in the animation

mode and buffer as well as utilization statistics and machine states in the reports generated after

the simulation is run. However, there are utilization indicator uncertainties in detecting the right

primary and secondary bottlenecks over time using these combinations of indicators. Moreover,

the buffer indicators have problems since all machines do not have buffers and buffer sizes are

limited to different capacities leading the system to be choked. Therefore, another status is used

to decide the bottleneck that is active and passive states. Active state occurs when resources

limit the product flow, which is the status of a resource when it is working, being repaired, or

under setup, while passive state occurs when the resource do not limit the product flow, which

is when the resource is either blocked (followed by full output buffer) or starving (preceded by

buffer). Long active period refers to the current bottleneck and interrupts other resources, while

short active period is interrupted by other resources, which means that the current bottleneck

could change over time leading to two main static description of the bottleneck position

including shifting and sole bottlenecks. The later position happens when only one resource

(machine) has the longest uninterrupted active period, while shifting bottleneck implies that

more than a resource has/have the longest active period. As a result, to determine the bottleneck

over time, the overall durations of shifting and sloe bottlenecks are calculated and compared for

each resource and the longest duration will be the bottle over time (Roser et al., 2003).

To back up the TOC by DES, the work procedure would be then:

1. bottleneck identifying using simulation,

2. bottleneck exploiting by testing ideas in the model,

3. trying out strategies using the model to subordinate other resources,

4. elevating the bottleneck capacity by conducting investment analysis using simulation,

5. realising the chosen investment in the real-world system,

6. and moving to the next bottleneck by returning to the first step.

2.5. Experimental Plan and Statistical Analysis

According to Carson and John (1996), in the statistical analysis, since Autostat uses the random

samples as the input data for analyses which would cause different behaviours of the stochastic

simulation models, more than a single simulation run are needed here to get a more real model.

Multiple replications instead of a single number are applied in Autostat to calculate the

confidence intervals which generate the steady-state mean value of responses. This statistical

analysis gives us an accurate performance of the real system.

There exist several kinds of analysis which could be applied in this project according to

different aims. Single scenario analysis uses confidence intervals to get various responses. On

the other hand, in multiple scenarios we also can apply same or different input parameters,

factors to find how the system behaves under some predefined conditions. And it is allowed to

define many kinds of responses we are interested in. Mostly the above analysis methods are

used to achieve such like optimal setting, experimental plans or get more complex analysis

result. In this project both of them are used to realise different tasks.

3. METHODOLOGYIn this report, we inductively study the production system of Sim’s Coffee Cups factory using

many consistent established methods, approaches and guidelines. For the main study method,

we used the steps of DES as framed by Banks (2010), which is shown in figure 2.1. The first

step of Bank’s is problem formulation, which is given and well described for Sim case in

section 1.2. The second step is setting of objectives and project plan, and that is also addressed

for great extent in sections 1.3 and 1.4 as well as in Appendix B (the project plan). As for the

conceptual model, we used the drawing of the production flow that is given in the case input as

a conceptual model since we were comfortable with it (refer to Appendix C to see the

conceptual model). As for the data, it was already collected and condensed in the case except

for the painting process where we needed to conduct input analysis for the MTTF and MTTR

values. Therefore, it is obvious that the steps from 1 to 4 were just extracted and organized from

given data and information.

In step 5, we translated the conceptual model together with the available data into computer

language that is readable by Automod, which is the software we rely on for the DES modelling.

Simultaneously, the verification (step 6) was done in accordance with Sargent (2012) whereby

the model is built and tested step by step. The verification in our case included five stages

including:

1. after the coding has been finished from the point of metal sheets delivery to the middle

storage of cup parts,

2. after the coding has been finished from the point of pellets delivery to the middle storage

of both wrappers and lids assemblies,

3. after the aforementioned branches of flow have been combined together forming one

middle storage,

4. after the coding has been finished from the point of middle storage to the conveyor before

assembly,

5. and after the coding of assembly has been included and the whole base model was

complete according to the specifications.

During the verification process, we had to build temporary final output and close some loops so

that we can verify the current progress of the model. Furthermore, we used almost all

verification techniques recommended by Sargent (2012) including cross-checking the code,

letting other groups inspect the code, using the available debugger and message printing feature

of Automod, and studying the model animation.

As the validation step (step 7) is important, there were some aspects of the original system

behavior that was pre-formulated for the case in order to be compared with such as the weekly

throughput of the base model after considering all system losses and limitations. The case

formulator who was also the Automod expert has given support in this step to ensure that the

improvements would not be built on invalid model. According to Sargent (2012), this validation

technique could be called face validation.

After the base model has been judged as fairly valid, we started to strictly follow TOC iterations

as described in section 2.4. For instance, to identify the first bottleneck, we calculated the

longest active time of a resource through state functions of Automod. Also, to subordinate other

resources, we tried to set priorities to the processes of the bottleneck. Another example is that

we picked and tested relevant investments from the list in Appendix A to elevate the bottleneck.

Finally, we continued that over and over again until we just hit the limit of the investment

budget stated in the case description, which is 150,000 Euro. It is worth mentioning that in order

to test some investments, e.g. for determining the optimum buffer levels, we used the Design of

Experiments (DoE) tool enabled by Autostat according to section 2.5. Besides, to take a

shortcut and save significant calculation time, we modelled the economic as well as other

calculations of relevance in a way they are printed directly on the animation screen as labels. In

this way, we could sensitively track the contradicted/passive changes that are limited by the

complex interrelationship of the flow entities.

The following steps of running the simulation of the production system have been

simultaneously done from the point at which the experimental plan is set. Finally, the extra

tasks (A+B) have been modelled, verified, validated, and then added to the final system model.

The whole project work is documented in this report as the last step of the DES project

methodology proposed by Bank (2010).

4. PROJECT REALISATION:By having the conceptual model (shown in Appendix B) and the data given by the case

description of Sim’s Coffee Cups, the coding of the base model could be done, then verified and

validated as mentioned in chapter 3. The complete code of the final model is attached in

Appendix D.

After the base model has been verified and fairly validated, the improvement iterations of TOC

was applied several times according to the aforementioned methodology. The next sections

clearly describe the first iteration of TOC used to detect and break the bottleneck(s) from the

point where the base model is ready to be experimented. This iteration could be also regarded as

our structured experimental plan to improve the productivity of the production system of Sim’s

Coffee Cups until the final setting of the model is reached. Both the number of iterations and the

changes in each iteration following the first iteration will be declared.

4.1. Identify the constraint

Before analysing the output of the base model runs, warm-up analysis has been done to

determine and only consider the steady-state interval, which was done by Autostat through two

responses, the total claim counts of the final buffer and final process. Figure 4.1 shows the

similarity between the patterns of two responses, which gives more credibility in the

determination of the steady-state interval. The warm-up was estimated to be according to the

following formula of Bank (2000):

Warmup Time = Steady State Time – w × Snap Length = 6 – 2 × 1 = 4 hours

Figure 4.1: Warm-up Determination Analysis

After that, by gathering the responses of all machines utilizations as shown in figure 4.2, both

painting equipment and wrapper molding seem to have the highest utilization over time.

Figure 4.2: Machines Utilizations in the Base Model

By studying the other bottleneck indicators of buffer status (starved or blocked) before and after

molding and painting machines as shown in figure 4.3, it was determined that painting machines

were the bottleneck since the buffers downstream were more blocked than for molding

machines. In contrary, the utilizations of assemblers were relatively low, meaning that they

were most of the times idle waiting for the painting machines. Besides, the interrelationship of

painting machines with other processes influenced all process up- and downstream more

significantly than the molding process did. Finally, we applied similar improvements in the

molding and painting productivity, respectively, to validate our choice of painting as a

bottleneck.

Figure 4.3: Average Time Per Claim of Buffers in the Base Model Excluding Storage Buffers

4.2. Exploit the Constraint and Subordinate Everything Else

Once the painting machines were identified as the current bottleneck and that wrapper molding

machine is the dependent second bottleneck, we tested how reconfiguring the current flow

without the need to invest could increase the throughput. By giving the priority of the

maintenance activities done by the maintenance operators to the painting then polishing

machines to exploit the constraining aspect of the painting process, we could improve the

throughput. Similarly, by giving the priority to the molding process to be maintained by its

corresponding operator, more improvement in the throughput could be realised. By applying all

these changes to subordinate the system components for the sake of the painting process, we

could realise significant throughput improvement as shown in figure 4.4.

Figure 4.4: Throughput Before and After Exploiting the Bottleneck and Subordinating Everything Else

4.3. Elevate the constraint

After the free improvements have been applied to the system, we highlighted the relevant

investments to the painting process from the list in Appendix A for investigations. Initially, it

was obvious that it was about the wrapper production since painting and wrapper molding the

first and second bottlenecks, respectively, and both machines only process wrappers. Moreover,

figure 4.2 shows significantly long overall polishing downtime and very long stamping idle

time. Therefore, the space of investigations were not limited to the painting. The focus was the

wrapper flow in the beginning, and it was planned that once no further improvements could be

realised in the wrapper flow, polishing downtime and stamping idle time would be the next

target of experiments.

By investigating all painting investments’ influence on the throughput, we choose the

investment with the biggest impact, which was adding a new painting station. It is also worth

mentioning that we used single- and multivariable analysis on Autostat to investigate the effect

of space (wrapper flow buffers), which was also promising but not enough to be our first

investment.

4.4. Iterate

The same steps were followed and repeated using the same approach until the limit of the

investment budget (150,000 Euro) was almost reached. Table 4.1 abstract the iterations. The

current bottleneck after all selected investments was the wrapper molding machine.

Table 4.1: Iterations of Bottlenecks Detecting and Breaking

Iteratio

n # Current Bottleneck Indicators

Considered Free

Reconfiguration Chosen Investment

1 Painting Machines ● Machine

Utilization

● Overall Buffer

Status

● Interrelationshi

p

● Pilot Runs

● Prioritizing

maintenance

activities for

the bottleneck

and next

bottleneck

● Secondly

prioritizing

maintenance

activities to the

machine with

relatively great

downtime

● Changing the

roles of

operators after

assuming that

they are

interchangeabl

e

New Painter

2 Painting Machines Reducing Painting

Ct by 20%

3 Painting Machines Refill Capacity

4 Painting Machines Double Painting

Batch Size

5 Painting Machines 25 Increase in

Wrapper Box Buffer

Size

6 Polishing Machines ● Machine

Utilization

● Overall Buffer

Status

Middle Storage

Expansion and

Removing One

Stamping Machine

7 Wrapper Molding Machine ● Machine

Utilization

● Overall Buffer

Status

● Pilot Runs

40% Decrease in

Molding MTTR


Utilization

● Overall Buffer

Status

● Pilot Runs

20% Decrease in

Polishing MTTF


Utilization

● Overall Buffer

Status

60% Decrease in

Polishing MTTR

10 Cutting Machine ● Machine

Utilization

● Overall Buffer

Status

● Pilot Runs

25 Increase in

Cutting Buffer Size

5. PROJECT RESULTS According to the analysis in last chapter, the investments in Table 5.1 are applied on the base

model.

Table 5.1 Applied investments

After executing all the investments and free reconfigurations, the throughput of the factory

increases by 47.5% and this comparison could be found in Figure 5.1. As shown in Figure 5.2,

the utilization of all equipment in the factory are optimized as the future-state machine

utilization represents. After the improvements, the processes before painting work more

efficient and can manage the production flow even the painting process of downstream has

more demand. Removing one stamping machine has nothing harmful done for the following

work and released a lot space and consumption which fairly contributed for the capacity of the

system.

Figure 5.1 Throughput comparison between base model and improved model

Figure 5.2 Utilization comparison l

The progress of the final model is described in Table 5.2 as the comparative analysis. From this

table the output of the final model is explicitly demonstrated at different points. It is obvious

that the performance increased a lot compared to the original model. In the financial

perspective, the profit of the factory would get much improved even taking into account the

investment cost.

Table 5.2 System performance comparison

To achieve the visualization of final improved model, the related parameters are graphically

described as Figure 5.3. The displayed information gave a good view of the dynamic condition

of the system and direct data for some kind of specific analysis without going through the

reports deeply.

Figure 5.3 System visualization

Since the improvements are restricted by the limit budget, there are still other unimplemented

investments which are able to increase the throughput. The recommendations shown in Table

5.3 could be considered for the future progress.

Table 5.3 Future recommendation

In the article Comparison of Bottleneck Detection Methods for AGV Systems by Roser et al.

(2003), the most common bottleneck detection methods are described which are based on the

utilization, the waiting time and developed shifting bottleneck detection method. In the real

system, most of the time there are more than one bottleneck in the production system and some

of them could vary with time according to the dynamic system property. The primary bottleneck

has fairly highest level of influence on the system, and some less influential machines called

secondary bottlenecks will constrain the system more or less at the same time. As the analysis in

this paper shown, the waiting time method has a couple of additional limitations for bottleneck

detection which are kind of inapplicable in our project. The shifting bottleneck method

apparently has better accuracy and understand ability, but it is more difficult to implement

compared to other methods. The utilization method combined with experimental analysis is

used in this project since they are fairly enough to detect the constraint in this system and easier

to be applied. Moreover the result is verified as quite reasonable according to application of

improvements afterwards. Benefiting from the flexibility and comprehensive function of

simulation system, it is easier to get all desired information and realise different kinds of

experiments to analyze the production system behavior. It also helps to avoid potential mistakes

and unstable results which could be detected from more data and analysis. Combining the

relatively easily implemented bottleneck method and the function of the simulation system

guarantees the reliability and rationality of the result.

6. DISCUSSION

6.1. Extra Task AThis task was done to estimate the total energy consumed by the polishing machines in active

(operating) state and idle (on but not functioning) state. That was calculated after setting states

for polishing machines and then modelling the following formulas to be printed on labels posted

on the animation screen of the model:

set V_PolishingActiveEnergy = (2*(R_Polishing WorkingPolishing total * R_Polishing

WorkingPolishing average time)/3600)

set V_PolishingIdleEnergy = 1800 - (R_Polishing WarmupPolishing total * R_Polishing

WarmupPolishing average time)/3600 - (R_Polishing WorkingPolishing total * R_Polishing

WorkingPolishing average time)/3600

print "Total Energy: "(V_PolishingIdleEnergy(1) + V_PolishingActiveEnergy(1)) to

LBL_TotalPolishingEnergy(1)

From that, the total energy for all the polishing machines is 9818 kW, 3272.7 kW per machine

on average

6.2. Extra Task B

In the original system, the orders are continuously generated every time when the last order is

finished, it has the highest efficiency and continuity according to the completeness of orders. In

the extra task C, the parameter X which represents the time interval of the order arrival decides

the frequency of orders generation. The model without any improvements apparently is

incapable of finishing all orders at some conditions with different choices of X. As Figure 6.1

described, when X is equal to 70 this model can still finish 100% orders which is nearly its full

capacity. It appears that the system starting to fail to finish all orders when X reduces to 60.

This indicates, when X reaches a specific level, the system will utilize its highest ability and is

incapable of managing more orders with respect to the decrease of X.

Figure 6.2 Order completion before improvements

To be able to manage more orders, some improvements need to be implemented to accelerate

the downstream processes. Since the order generation process directly connects with painting

and assembly processed, some improvements in these processes are able to increase the ability

to take care of more orders. Considering related aspects, there are two improvements applied

here. One is adding another assembly station matching with the four existing painting lines. The

other one is reducing the painter cycle time by another 40%. The reduction ratio second

improvement is based on Figure 6.2 which simply compared the impact on the order completion

with different configurations. It is shown that when cycle time is equal to 21.2 second and X is

equal to 35, the system can manage 91.6% of the orders within 8 hours after warm-up. Along

with less X value, this system in unable to keep the 90% completion. Then the lowest X this

system can handle is 35.

Figure 6.2 Order completion with different painting cycle time and scale X

7. CONCLUSION This report shows how a production system can be simulated and optimized in terms of

productivity and line balancing using TOC as a background theory. Sim’s Coffee Cups factory

was studied and its production system was modelled. The results of the base modelled shows

that the system produce wrappers slower than other parts making all in machines along its flow

and the dedicated space recurring bottlenecks including the middle storage, painting and

molding processes. Besides, there were excess capabilities stem from having an unnecessary

additional stamping machines, while the robustness of the stamping machines was not sufficient

in terms of failure and maintenance time. Further, the overall utilization of the labour was rather

small but unevenly distributed.

Our findings proves that by building in capabilities along the wrapper flow, which could be

done by adding new painter, improving painting speed and refill capacity, adding space to

wrappers in the middle storage, and improving molding robustness. Additionally, further

improvements require investing in machines with low robustness such as polishing. In principle,

the system will reach to a point after many investments are applied in which many

improvements should be applied in the same time to improve the productivity since capabilities

become relatively balanced and single improvement will only sub optimize the system. Finally,

it is worth mentioning that free changes such as setting priorities in planning and resources to

the system constraints improve the system, which was proved in Sim’s case after operators were

given orders to prioritize the first and second bottlenecks for maintenance activities.

References

Banks, J. (2010). Discrete-event system simulation. Upper Saddle River, N.J.: Prentice Hall.

Banks, J. (2000). Getting started with AutoMod. Bountiful, UT: Brooks-PRI Automation.

Brown, S. and Maylor, H. (2005). Strategic resonant firms, mass producers, big fish & flatliners:

a study of policies. practices & performance in innovation. Technovation, 25(4), pp.307-319.

Carson, I. and John, S. (1996). AutoStat: output statistical analysis for AutoMod users. In:

Proceedings of the 28th conference on Winter simulation. IEEE Computer Society, pp. 492-499.

Goldratt, E. and Cox, J. (1992). The goal. Great Barrington, MA: North River Press.

Ingalls, R. (2008). Introduction to simulation. In: Proceedings of the 40th Conference on Winter

Simulation. Stillwater: Oklahoma State University, pp.17-26.

Rabi, M., Johansson, K. H. and Johansson, M. (2008). Optimal stopping for event-triggered

sensing and actuation. In: Proceedings of the 47th Conference on Decision and Control. Cancun,

Mexico: IEEE, pp. 3607-3612.

Robinson, S. (2004). Simulation – The practice of model development and use. Chichester, Eng.:

Wiley.

Roser, C., Nakano, M., and Tanaka, M. (2003). Comparison of bottleneck detection methods for

AGV systems. In: Proceedings of the Winter Simulation Conference 2003. LA: IEEE, pp. 1192-

1198.

Sargent, R. (2012). Verification and validation of simulation models. Journal of Simulation, 7(1),

pp.12-24.

APPENDICES

Appendix A: Possible Investments for Sim’s Production System

Appendix B: The DES Project Plan of Sim’s Coffee Cups

Appendix C: The Conceptual Model of the Production System of Sim’s Coffee Cups

Examination project Simulation of Production Systems

MPR 271 – 2014 - Version 1.6

This document describes a production system in need of improvement. The examination project

is to create a model of the production system in AutoMod, simulate and analyze its behavior, and

suggest proposals for investments able to make the production flow more efficient. Please advice

in the matrix in the course PM to see the specific objectives that you will learn and be assessed

on in this project!

Your work must be reported in five rounds:

1. Create a project plan and upload that on PingPong. The project plan should at least include base tasks scheduled in time e.g. a Gantt chart, and a project aim. A task could be to model the cutting process or plan experiments. It should include who has the main responsibility for the task or if it is shared responsibility. The project plan should align to your grade aim. If you want the highest grade, you need to plan for all extra tasks. Submit maximum two A4 pages. Upload the project plan to PingPong no later than Monday 141117.

2. Build one conceptual and one simulation model of the system as it works today (the base model). Verify the base model by demonstrating how it is running for a supervisor in the laboratory and submit the output data to PingPong. This must be done before the end of scheduled time on Friday 141205.

3. Design a plan for experimentation. Use Theory of Constrains and experimental planning. Answer the questions in the assignment on PingPong. This must be done before the end of scheduled time on 141211, but it is recommend do aim for the beginning of that week.

4. Present the final analyze for 8 minutes to the project commissioner on given times between 141215 and 141219. Upload your presentation on PingPong before your presentation time. Presentations will be scheduled later in the course and further instructions available on PingPong.

5. Deliver your final report and model according to the specification below before midnight on Monday 150112.

o Upload a report pdf file with max 6000 words. o A zipped exported automod file. o All the code in a single text file. (e.g. “model.m” or the code copied to

“allmycode.txt”)

In step 5, you should summarize and present every part of the DES-project you want credit for.

The report must be written in English and consist of maximum 6000 words. The report constitutes

the basis for examination and grading. The report should contain at least:

Output and performance of the base model

Improvement plan based on your experiment design

Output from simulation experiments and the improved final production system

A visualization of the final system must be done and at least one picture should be enclosed

A discussion relating your project to at least one of the scientific papers in the course PM.

Extra tasks to be able to achieve grade 4 or 5. For a chance of a five, you are required to at least start with all extra tasks. Two completed extra

task and one planned for but not fully implemented has a chance of five depending on the quality.

For a good, chance of grade four complete two extra tasks. However, good overall project and only

one extra task is enough for grade 4. A good performed extra task is implemented and fully

analyzed, however a planned and good described extra task that was not finished give a chance

for high grades, depending on the project as a whole.

A. The raw material plastic are produced in a factory around 200 kilometers from your factory. Sim’s Coffee Cups would like to change ordering policy from today’s fixed delivery interval to an order point strategy. In other words, an order of raw material should be placed when there are X sheets or kg plastics (do one of them) left in stock. From the time the order is placed and the order is received it takes between 3 to 9 hours, and the most likely value is 7 hours. Your task is to find a good order point (X) using simulation. Sim’s Coffee Cups expects the service level of raw material for the molding injectors to be 98%. Be aware, while improving the system you may need to change the storage size and order point. You might what to disable this order point strategy while improving the system

B. The manager heard that the polishing machines use lots of energy while they are not producing (idle or blocked). The manager wants to assess how much energy that can be saved if the machines were totally shut down when not operating and how much that strategy would affect the capacity of the plant. During operation each machine use 2 kW, during idle time they use 1 kW each. After a machine shut down, 2 minutes need to be added when the machine is started as a warm-up procedure of the).

C Model the cups to be produced to order from the middle storage. The orders arrives

continuously according to a Weibull function with shape 2 and scale X minutes. The goal

is to make your system finish at least 90 % of the orders within 8 hr after at least one-

week warmup. Decide and optimize (low is better) a safety storage level with cups ready

to be ordered in the middle storage. Apply any other needed improvement. How low X

can you get you system to handle?

The report can be written using following structure (or similar):

- Title - Abstract - Table of content - Introduction: background, purpose, objectives, problem formulation, delimitations etc. - Methodology - Project realization: work description including analysis of bottlenecks, experiment design

and analysis - Project results - Discussion (referring results and/or methodology to at least one scientific paper) - Conclusion

Implemented DES-model code must be included as an appendix – remember that code of good

quality always includes a lot of explaining comments.

Upload your report as a pdf document, all your code in a single text file. (e.g. “model.m” or the

code copied to “allmycode.txt”) and a exported and ziped arc folder to Pingpong.

Questions? Contact: [email protected] or [email protected]

Good Luck!

mailto:[email protected]

mailto:[email protected]

Sim’s Coffee Cups Keeps your coffee hot and tasty

Background You are newly hired to work at the production development department at Sim’s Coffee Cups,

which is a company producing coffee cups. Due to an exploding market in reusable and customized

coffee cups, they built a new production line for this that do not perform as expected. The poor

delivery precision of ordered cups during high demand is only one example that affects the

customers. The management team guesses that the root causes of frequent production

disruptions are to be found in poorly balanced line and old machine reused from a former

production site. The marketing department has indications of an even more increased market

demand for the company’s customized coffee cups. To be able to catch that extra demand the

capacity needs to be increased. You need to perform an investigation and deliver a report that will

serve as decision support for future investments.

Goal The goal is to improve the throughput of products in the factory as much as possible with a given

investment budget. The task is to analyze the dynamics of the product flow within the factory, find

its weaknesses and present possible investments within the frame of the fixed budget. The

investment budget for this task is 150 000 €. Improvement above 150 000 is possible but needs to

be very well motivated. However, all investments must be proven economic profitable with related

risk analysis.

Description of the production system There are three production flows. One produce the stainless steel inner coffee mug. One line

produce the plastic lids and outer plastic wrapper. One line paint the plastic mug wrapper and

assemble the cup together with the lid.

The existing and current schematic production flow is presented in Appendix A.

A list of possible investments and their costs is presented in Appendix B.

If there are any obscurities, use your engineering skills to make assumptions that are relevant to

the described production system.

Factory hours The factory is open for production 40 hours per week, and 1900 hours per year. Operating time is

07:00 to 16:00 including 1-hour lunch.

Manning Six operators and one maintenance personnel operates the production. All operators are allowed

for short breaks besides the lunch. The breaks is individual and occurs approximately according

to a Weibull distribution of shape 2 and scale 4 hours. They last approximately according to a

normal distribution with mean 5 minutes and standard deviation 0.5 minutes.

Indication of production output A production planner at Sim’s Coffee Cups has performed a very informal study of the production

output in the given production system. Her opinion is that the system can produce around 8000

cups per week. However, the production system has very seldom been tested at maximum

capacity though the demand today is approximately 4600 - 4900 cups per week.

Economics The income for the production is 4 € for each cup plus 5 € for each order

The raw materials for one individual cup cost 0,3 €

Painting costs 0.2 € for each cup

Machining cost for each machine is 25 € per hour processing (Injection molding, stamping, cutting, polishing)

When the machines is not processing they cost 5 € per hour, do only apply when the factory is open.

Each operator costs 30 € per hour

The maintenance personal cost 35 € per hour

Running cost for the facility is 10000 € month

Material handling The material handling equipment in the factory is considered very quick and robust. Therefore,

Sim’s Coffee Cups does not want you to spend time and resources on modeling a complete

movement system with forklifts and conveyors in your base model. The only conveyors to include

in the base model are given in the following description of the production system.

If you have time, you can include more conveyor and forklifts to enhance the 3D-graphics after all

important analyses are finished! Feel free to be creative if something is not described in detail.

Delivery of raw material Raw material is delivered once a day during the morning before 07:00. Stainless steel sheets comes

in pallets of 200 sheets, plastic pellets comes in pallets of 100 kg. Keep 5000 sheets and 1000 kg

plastic in your base model and optimize this in Extra task A.

Deep drawing stainless steel inner cups The first step in the line, is to stamp sheets using two parallel machines. The only required manual

operation is to fill it with stainless steel sheets and to clean it. Each cup use one sheet.

http://www.youtube.com/watch?v=qol_BGPmek8

Process: There are two identical stamping machines in the factory today.

They have each a buffer capacity of 200 sheets.

Time per part is 10 seconds.

Time for cleaning is uniformly distributed between 3 and 7 minutes (requires operator) and performed every 1000 product.

Loading the machine is done when there is less than 50 sheets left. Loading takes about 2 to 5 minutes.

The same operator serves this machine, the cutting machine, and the lid injection molding machines.

MTTF = Exponential distribution with average value of 12 hours.

MTTR = Triangular distribution: min: 12, mode: 30, max 35 minutes (requires maintenance operator).

Buffer: After the stamping process the mugs is automatically transported to a small buffer with capacity

of 5 cups, when this buffer is full the stamping machines automatically stop.

Cutting This process removes excess material from the stamping. The machine is fully automatic and

robust machine, it only needs cleaning one time each day.

http://www.youtube.com/watch?v=qol_BGPmek8

Cycle time for the process is constant 10 seconds.

Cleaning requires one operator and takes between 5 and 12 minutes.

The same operator serves this machine, the deep drawing stainless, and the lid injection molding machines.

Buffer: After the cutter, the cups are automatically stored in a small buffer with capacity of 10 cups, when

this buffer is full, the cutting machines automatically stop.

Polishing Next process is polishing, the cups needs to be polished, which is done in three polishing

machines. When 9 cups are available in previous buffer and one machine is empty, loads an

operator the cups into the machine. The machine polishes the cups. When they are ready, an

operator unloads the cups onto pallets in the middle storage 100 on each pallet. The loaded

pallets are already in the middle storage and does not need to move.

http://www.youtube.com/watch?v=8LycPssTMjs

Three parallel machines, which process 9 cups each at a time.

Loading is manual process for one operator o Loading all cups takes a Weibull distribution with shape α=4 and scale β=20 sec o Unloading and inspection of all cups takes a Weibull distribution with shape α=4

and scale β=40 sec

Polishing time is constant 510 sec.

MTTF = Exponential distribution with average value of 5 hours.

MTTR = Triangular distribution: min: 50, mode: 60, max 120 minutes (requires maintenance operator).

One operator serving all three machines.

1 % of the cups are scrapped after this process.

Injection molding of lids and plastic wrapper Lids and plastic wrapper are processed in a separate line. The line have two injection molding

machines. One injection-molding machine creates lids in two parts using one die. A following

process assembles the parts using one operator. The other injection-molding machine molds the

plastic wrapper that will surround the cup. The machine produces wrappers into a box to be

transported to the middle storage.

http://www.youtube.com/watch?v=eUthHS3MTdA

Lids die o Each die strike include 4 lid assemblies. o Each lid uses 40 g plastic. 4 lid uses 40 *4 g plastic

Plastic wrapper die o Each die strike include 3 cup wrappers o Each wrapper uses 120 g plastic. 3 wrappers uses 120 * 3 g plastic.

Cycle time is constant 59 seconds

Filling the molding machines takes 5-8 minutes. Commonly this is done when it is 50 kg left in a machine.

Plastic capacity is 300 kg plastic.

The same operator serves this machine, the deep drawing stainless, and the cutting machines.

MTTF is exponential 5 hr

MTTR is normal distributed with mean 45 min and standard deviation 5 min. Repairing is done using the cutting operator.

Buffer: The lids are automatically put in a box that contain max 30 lids. If it is full the process stops

automatically

http://www.youtube.com/watch?v=8LycPssTMjs

http://www.youtube.com/watch?v=eUthHS3MTdA

The wrappers are produced into a box of maximum 100 wrapper. When it is full the machine stops, and the Operator in the lid assembly change the box, which takes between 2 to 3 minutes.

Lid Assembly Lids have been molded in two parts that is assembled by one operator. The operator also remove

excessive material on the lids during the assembly using polishing equipment. This operator also

operates the painting machines and the middle storage.

One operator on this station

Cycle time is normally distributed with mean 18 seconds and standard deviation 3 seconds.

2 % of the lids are scrapped.

MTTF for the equipment is exponential 8 hr

MTTR is normal distributed with mean 6 min and standard deviation 30 sec, the reparation in done by the operator at the station.

Buffer: The lids are put on a pallet that contains 100 lids that is send to the middle storage when

done, which takes between 2 to 3 minutes.

Middle storage The lids and cups are stored before they are ordered in a middle storage. The middle storage has

a capacity of 2 batches stainless steel cups, 2 batches wrappers, and 2 batches lids, each batch is

100 pieces. The customer´s orders arrives to this storage and mainly rage between 1 to 500 in

size. Small orders are common and the big orders are not very common.

The order sizes are distributed as a

Size of 1:30 %, 2-5:20%, 5-20:20%, 20-400:20%, 400-500:10%

Assume a new order starts directly after one order is finished in the painting stations.

Painting Station: Each order have a unique painting that the customers can design themselves on our homepage.

For each batch, the painting machines need to be set up for the particular batch. Ones set up the

process is automatic. There are three painting lines. One operator man the printer, the middle

storage and the lid assembly.

To set up a new batch there are two phases. First, the design is loaded. Then color are

filled if it is needed.

o Load program takes uniformly 2-4 minutes and requires an operator.

o The machine has capacity for 500 cups color. If it is not enough color in the

machine, the operator fills the machine before it is started. Filling time is normally

distributed with mean 7 and standard deviation 1 minute.

The painted cups are transported on a conveyor to the assembly and packaging.

When the painter run out of plastic wrappers a new batch of 100 cups, is loaded which

takes 2-3 minutes

Each painting takes 53 sec.

MTTF and MTTR is provided as real measurements in an excel document. The document

is provided to your project groups file area on PingPong. Use the software Expert Fit to

derive a distribution from the values.

Assembly and packaging In the assembly and packaging area, lids and cups are assembled on three separate lines, one for

each painting machine. One operator operates each painting line. First the painted outside is

assembled into a metal mug and forced together. Secondly, the lid is assembled on top, perform

some last checks, and clean some dust. The operator finally package it for delivery. Each order is

packed into one package from small packages to full pallets. They are then ready for delivery.

Conveyor Conveyor speed 0.4 m/s

The conveyor is 15 meter long in total from painting stations to the end. When it is 5 meters to the assembly station, it divides into three 5 meter conveyor, one for each station

Each cup is 4.5 cm in diameter.

The conveyors are not accumulating

MTTF for conveyor: Exponentially distributed, mean 16 hours

MTTR: Weibull shape α=1.5 and scale β=90 min (requires maintenance operator)

Assembly and packaging: Cycle time for each cup is distributed as a Weibull function shape α=3 and scale β=28 sec.

Extra packaging Cycle time for each batch is distributed as a o Weibull function shape α=1.5 and scale β=120 sec for 1-10 cups orders. o Weibull function shape α=1.5 and scale β=150 sec for 10-100 cups orders. o Weibull function shape α=1.5 and scale β=250 sec for 100-500 cups orders.

Gather more lids and stainless steel cups from the middle storage takes 1-3 minutes every 100 cups and is done by the assembly operator.

Other information If you feel insecure on how to interpret the input information above, please make your own

assumptions and document them in your report. You can also ask a supervisor for guidance.

If you run on a student version of AutoMod, please make sure not to use too many entities. It

should be possible to build this model using less than 100 entities.

The YouTube videos are only there to give you a hint of the kind of process we are simulating. The

processes in the videos is not equal to the process in our project, but could help you understand

the typical environment.

Appendix A, production flow

Stamp

ing 2

(cup

s)

Stamp

ing 1

(cup

s)

Cu

tting

Polishing 1

Polishing 2

Polishing 3

Plastic M

old

ing

1

(wra

pp

er)

Lid A

ssem

bly

Pe

llets

Mid

dle Sto

rage

Plastic M

old

ing

2(lid

s)

She

ets

Appendix B, Possible investments

Conveyors €

Per meter conveyor 1000

Price for any fixture 4000

Increased speed on a conveyor (per 20%) 4000

Make one conveyor accumulating 5000

Buffers

Additional buffer capacity anywhere in the factory (25 cups or lids) 1000

Middle Storage expansion; Remove any machine on the shop floor to

get extra 1000 cups and lids space. -10000

Middle storage expansion 100 cups or lids 5000

Injection molding machines

Identical set of new molding equipment 60000

New die with any setup. (die setups: “4 wrapper”, “3 wrappers

assemblies and 1 lid assemblies”, “2 wrappers assemblies and 2

Assemblies”, or “1 wrapper and 3 assemblies”, or “5 lids assemblies”) 20000

Automatic pellets filler continuously from central storage 30000

Increased MTTF (per 20%, max 60 %) 10000

Decreased MTTR (per 20%, max 60 %) 13000

Stainless steel drawing

Identical set of new molding equipment 30000

Increased loading buffer x2 10000



Stainless steel Cutting

Add functions to polishing machines eliminate this process 30000



Polishing

Identical set of new equipment 40000

Reduced cycle time (per 10%, max 40%) 15000

Fully automatic process, a conveyor has to connect the other

processes. 10 % longer cycle time, no loading time. 45000



Assembly lids

New Line 5000

Reduce all cycle times (per 10%, max 30 %) 10000

Automatic machines, connect with conveyor 40000

Increased MTTF (per 20%, max 40%) 8000

Decreased MTTR (per 20%, max 20%) 3000

Painting

New station, including conveyor 40000

Reduce all cycle times (per 10%, max 20%) 14000

Bigger refill capacity 400 % increase, per printer 5000

Load two batches at a time, takes 3-4 minutes 15000

Increased MTTF (per 20%, max 40%) 13000

Decreased MTTR (per 20%, max 40%) 15000

Assembly

Assembly station 20000

Auto packaging machine, order cycle time decrease 40 % 10000

Project for standardized work, reduce scrap by 50 % 30000

Manning

New employee (Initial cost except salary) 5000

Train anyone to do one additional task 8000

If you find out other improvements than the ones mentioned above, please make realistic

assumptions and use much as needed.

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Model

(without extra tasks)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%*/

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% The code for extra tasks follows at the

end of this code independently

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

begin model initialization function

//Molding

set Q_Molding_wait(1) capacity to 300000/160 //buffer size in grams

set Q_Molding_wait(2) capacity to 300000/360 //buffer size in grams

set Q_Molding(1) capacity to 1

set Q_Molding(2) capacity to 1

//Box size for wrappers and lids

set Q_Wrapper_Box capacity to 125

set Q_Lid_Box capacity to 30

//Lid Assembly

set Q_LidAssembly capacity to 1

//Middle Storage

set Q_LidMS_Wait capacity to 1200

set Q_WrapperMS_Wait capacity to 1200

set Q_CupMS_Wait capacity to 1200

//Stamping

set Q_Stamping_wait capacity to 200

set Q_Stamping capacity to 1

//Cutting

set Q_Cutting_wait capacity to 30

set Q_Cutting capacity to 1

//Polishing

set Q_Polishing_wait capacity to 1

set Q_BatchP capacity to 9

set Q_Polishing(1) capacity to 1



//Painting

set Q_Painting(1) capacity to 1




//Assembly

set Q_Assembly(1) capacity to 1



//Plastic Ordering

create 1 load of load type L_Plastic to P_InitialMolding

create 1 load of load type L_LoadingPlastic to P_LoadingPlastic(1)

create 1 load of load type L_LoadingPlastic to P_LoadingPlastic(2)

//Sheet Ordering

set Q_SheetStorage capacity to 5000

create 1 load of load type L_Sheet to P_InitialStamping

//Order Generation

create 1 load of load type L_order to P_Order(1)




//Maintenance

create 1 loads of load type L_clean to P_Cutter_cleaning

create 1 loads of load type L_Breakdown_Cups to P_DownTime_Stamping1

create 1 loads of load type L_Breakdown_Cups to P_DownTime_Stamping2

create 1 loads of load type L_Breakdown_Cups to P_DownTime_Polishing1



create 1 load of load type L_breakdown_LidMolding to P_DownTime_LidMolding

create 1 load of load type L_Breakdown_WrapperMolding to P_DownTime_WrapperMolding

create 1 load of load type L_breakdown_LidAssembly to P_DownTime_LidAssembly

create 1 load of load type L_breakdown_Painting1 to P_DownTime_Painting1




create 1 load of load type L_breakdown_Conveyor to P_DownTime_Conveyor

create 1 load of load type L_ShortBreak to P_Operator1ShortBreak



create 1 load of load type L_ShortBreak to P_Assembler1ShortBreak



create 1 load of load type L_Print to P_Print

return 0

end

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

// System Visualization

begin P_Print arriving procedure

while 1 = 1

begin

//Labor Utilization

print "Util Maintenance (%): " (100*(R_Maintenance relative average)) to UtilMaint

print "Util Operator 1 (%): " (100*(R_Operator1 relative average)) to UtilOp1



print "Util Station 1 (%): " (100*(R_Assembly(1) relative average)) to UtilSta1



//Machine Utilization

print "Util Stamping 1 (%): " (100*(R_Stamping WorkingStamping total* R_Stamping

WorkingStamping average time/ac)) to UtilStam1

print "Util Cutting (%): " (100*(R_Cutting WorkingCutting total * R_Cutting

WorkingCutting average time/ac)) to UtilCut

print "Util Polishing 1 (%): " (100*(R_Polishing(1) WorkingPolishing total *

R_Polishing(1) WorkingPolishing average time/ac)) to UtilPol ish1


R_Polishing(2) WorkingPolishing average time/ac)) to UtilPolish2


R_Polishing(3) WorkingPolishing average time/ac)) to UtilPolish3

print "Util Lid Molding (%): " (100*(R_Molding(1) WorkingMolding total *

R_Molding(1) WorkingMolding average time/ac)) to UtilMold1

print "Util Wrapper Molding (%): " (100*(R_Molding(2) WorkingMolding total *

R_Molding(2) WorkingMolding average time/ac)) to UtilMold2

print "Util Lid Assembly (%): " (100*(R_LidAssembly WorkingLidPolishing total *

R_LidAssembly WorkingLidPolishing average time/ac)) to UtilLidAs

print "Util Painting 1 (%): " (100*(R_Painting(1) WorkingPainting total * R_Painting(1)

WorkingPainting average time/ac)) to UtilPaint1







//Machine Downtime

print "Down time Stamping 1 (%): " (100*(R_Stamping average down)) to DownStam1

print "Down time Cutting (%): " (100*(R_Cutting average down)) to DownCut

print "Down time Polishing 1 (%): " (100*(R_Polishing(1) average down)) to

DownPolish1


DownPolish2


DownPolish3

print "Down time Lid Molding (%): " (100*(R_Molding(1) average down)) to

DownMold1

print "Down time Wrapper Molding (%): " (100*(R_Molding(2) average down)) to

DownMold2

print "Down time Lid Assembly (%): " (100*(R_LidAssembly average down)) to

DownLidAs

print "Down time Painting 1 (%): " (100*(R_Painting(1) average down)) to DownPaint1




/*%%%%%%%%%%%%%%%%%% Economic calculations and display

%%%%%%%%%%%%%%%%*/

//The idel cost of machines

set V_IdelCost = 1900*7 - ((R_Stamping WorkingStamping total* R_Stamping

WorkingStamping average time)/3600+

(R_Cutting WorkingCutting total * R_Cutting

WorkingCutting average time)/3600+

(R_Polishing(1) WorkingPolishing total *

R_Polishing(1) WorkingPolishing average time)/3600+





(R_Molding(1) WorkingMolding total *

R_Molding(1) WorkingMolding average time)/3600+

(R_Molding(2) WorkingMolding total *

R_Molding(2) WorkingMolding average time)/3600)

//The total cost of machines

set V_TotalCost = (0.5*(Q_CoffeeCup total))+

(25*((R_Stamping WorkingStamping total* R_Stamping WorkingStamping

average time)/3600+

(R_Cutting WorkingCutting total * R_Cutting WorkingCutting

average time)/3600+

(R_Polishing(1) WorkingPolishing total * R_Polishing(1)

WorkingPolishing average time)/3600+





(R_Molding(1) WorkingMolding total * R_Molding(1)

WorkingMolding average time)/3600+

(R_Molding(2) WorkingMolding total * R_Molding(2)

WorkingMolding average time)/3600))+

V_IdelCost + (1900*((6*30)+35))+(10000*12)

print "Total Cost (€): "V_TotalCost to LBL_TotalCost

print "Total Income (€): "(4*(Q_CoffeeCup total))+(V_NumOfOrders*5) to

LBL_TotalIncome

print "Inflow Year 1 (€):"(4*(Q_CoffeeCup total))+(V_NumOfOrders*5) - V_TotalCost

to Inflow

//The calculation of WIP

set V_Capital = ((Q_LidMS_Wait current + Q_WrapperMS_Wait current +

Q_Painting(1) current + Q_Painting(2) current +

Q_Painting(3) current + Q_Painting(4) current)* 0.3) + ((Q_Assembly(1)

current + Q_Assembly(2) current +

Q_Assembly(3) current)* 0.5)

print "Current WIP (€): " V_Capital " €" to WIPCapital

wait for 2 min

end

end

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Cup Making Processes

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

/*Stamping Initialization*/

begin P_InitialStamping arriving procedure

while 1 = 1 do begin

set V_DeliverySheet = Q_SheetStorage remaining space // Keep 5000 sheets in the

model

create V_DeliverySheet loads of load type L_Sheet to P_Stamping_Filling

wait for 8 hr //Delivering at the begining of each work day

end

end

begin P_Stamping_Filling arriving procedure

move into Q_SheetStorage

create 1 load of load type L_Sheet to P_LoadingSheet

wait to be ordered on OL_Sheets

end

begin P_LoadingSheet arriving procedure

set priority to 1 //Priority setting for related processed

set V_Sheetnumber = Q_Stamping_wait remaining space //Every filling is the volume of the

remaning space in stamping buffer

order V_Sheetnumber loads from OL_Sheets to P_NewStamping

if V_loading = 150 then

begin

use R_Operator1 for uniform 3.5, 1.5 min //Loading Stamping machine when there is

50 sheets left

set V_loading = 0

end

end

/*Stamping*/

begin P_NewStamping arriving procedure

set priority to 1

move into Q_Stamping_wait

move into Q_Stamping

get R_Stamping

set R_Stamping state to WorkingStamping

wait for 10 sec //Processing time per part

free R_Stamping

inc V_Stamp1 by 1

if V_Stamp1 = 1000 then //Cleaning is executed every 1000 products

begin

take down R_Stamping

set R_Stamping state to InCleaningStamping

get R_Operator1

wait for uniform 5, 2 min // Cleaning Stamping Machine

bring up R_Stamping

free R_Operator1

set V_Stamp1 = 0

end

send to P_Cutting

end

/*Cutting*/

begin P_Cutting arriving procedure

set priority to 0

move into Q_Cutting_wait

move into Q_Cutting

get R_Cutting

set R_Cutting state to WorkingCutting

wait for 10 sec //Cycle time for cutting

free R_Cutting

// Batching 9 cups to polishing

move into Q_Polishing_wait

move into Q_BatchP

if OL_PolishBatch current < 8

begin

wait to be ordered on OL_PolishBatch

move into Q_BatchWait

wait to be ordered on OL_PolishBatchWait

end

//If there is no space in polshing buffer, cutting process will stop

if Q_Polishing(1) remaining space = 0 and Q_Polishing(2) remaining space = 0 and

Q_Polishing(3) remaining space = 0 then

wait to be ordered on OL_Polishing

order 8 loads from OL_PolishBatch to continue

//Send to polishing process

choose a queue from among Q_Polishing(1), Q_Polishing(2), Q_Polishing(3) // Choose a

Polishing machine

whose current loads is minimum

save choice as A_qpolishing

set A_polishingindex = A_qpolishing index

send to P_Polishing(A_polishingindex)

end

/*Polishing*/

begin P_Polishing arriving procedure

move into Q_Polishing(procindex)

use R_Operator3 for w 4, 20 sec // Loading cups by the operator

get R_Polishing(procindex)

set R_Polishing(procindex) state to WorkingPolishing

wait for 510 sec //Cycle time for polishing

free R_Polishing(procindex)

use R_Operator3 for w 4, 40 sec // Unloading and inspecting cups

wait until Q_CupMS_Wait remaining space > 9 // 9 cups are polished each time

order 8 loads from OL_PolishBatchWait to P_CupMiddleStorage

move into Q_AfterP

order 1 loads from OL_Polishing to P_CupMiddleStorage

send to P_CupMiddleStorage

end

/*Steel Cup Middle Storage*/

begin P_CupMiddleStorage arriving procedure

set V_PolishScrap to oneof(99:1,1:0) //1% of the cups from polishing are scrapped

if V_PolishScrap = 0 then

begin

move into Q_Scrap

print "Scraped: " Q_Scrap total " products" to Scrap

end

move into Q_CupMS_Wait

order 1 load from OL_WaitingforCups to continue

wait to be ordered on OL_CuptoAssembly // wait to be ordered by assembly process

end

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Lids and Wrappers Making

Processes %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

/*Molding Initialization */

begin P_InitialMolding arriving procedure


set V_DeliveryPlastic(1) = 300000/160 //Every delivery is the volume of the remaining

space in Lid Molding Buffer

set V_DeliveryPlastic(2) = 300000/360 //Every delivery is the volume of the remaining

space in Wrapper Molding Buffer

order 1 load from OL_Waitformaterial to continue

wait for 8 hr

end

end

begin P_LoadingPlastic arriving procedure

while 1=1 do begin

wait for uniform 6.5, 1.5 min //Filling the molding machine

set A_Moldnumber(procindex) = Q_Molding_wait(procindex) remaining space

create A_Moldnumber(procindex) loads of load type L_Molding(procindex) to

P_Molding(procindex)

wait to be ordered on OL_Waitforload(procindex)

end

end

/*Molding*/

begin P_Molding arriving procedure

set priority to 2

set A_Moldingindex = procindex

move into Q_Molding_wait(procindex)

move into Q_Molding(procindex)

//Filling the molding machine

if procindex = 1 then

if Q_Molding_wait(1) remaining space > 1563 then // check if there is less than 50kg plastic in

the machine

order 1 load from OL_Waitforload(1) to continue


if Q_Molding_wait(2) remaining space > 694 then // check if there is less than 50kg

plastic in the machine

order 1 load from OL_Waitforload(2) to continue

//Check if the box is full

if A_Moldingindex = 1 then

begin

if Q_Lid_Box remaining space = 0 then

wait to be ordered on OL_LidtoBoxFull //wait until there is space in the box

create 4 loads of load type L_Lid to P_LidBoxing // Put 4 Lids in the box

end

else

begin

if Q_Wrapper_Box remaining space = 0 then

wait to be ordered on OL_WrappertoBoxFull //wait until there is space in the box

create 3 loads of load type L_Wrapper to P_WrapperBoxing // Put 3 Wrappers in the

box

end

//Molding

get R_Molding(procindex)

set R_Molding(procindex)state to WorkingMolding

wait for 59 sec

free R_Molding(procindex)

send to die

end

/*Lid Box*/

begin P_LidBoxing arriving procedure

move into Q_Lid_Box

send to P_LidAssembly

end

/*Wrapper Box*/

begin P_WrapperBoxing arriving procedure

move into Q_Wrapper_Box

send to P_WrapperMiddleStorage

end

/*Lid Assembly*/

begin P_LidAssembly arriving procedure

set priority to 0

move into Q_LidAssembly

//One box of lids from molding process

if Q_Lid_Box remaining space = 4 then

order 1 load from OL_LidtoBoxFull to continue

//Assembling lid

get R_LidAssembly

set R_LidAssembly state to WorkingLidPolishing

wait for normal 18, 3 sec

free R_LidAssembly

//2% of the cups from polishing are scrapped

set V_LidScrap to oneof(98:1,2:0)

if V_LidScrap = 0 then

begin

move into Q_Scrap

print "Scraped: " Q_Scrap total " products" to Scrap

end

// Move to the middle storage every 100 lid assemblies on a pallet

inc V_LidtoMS by 1

if V_LidtoMS = 100 then

begin

use R_Operator2 for uniform 2.5, 0.5 min // Takes 2-3 mins to Middle Storage

set V_LidtoMS = 0

end

send to P_LidMiddleStorage

end

/*Lid Middle Storage*/

begin P_LidMiddleStorage arriving procedure

move into Q_LidMS_Wait

order 1 load from OL_WaitingforLids to continue

wait to be ordered on OL_LidtoAssembly // wait to be ordered by assembly process

send to die

end

/*Wrapper Middle Storage*/

begin P_WrapperMiddleStorage arriving procedure

move into Q_WrapperMS_Wait

if Q_Wrapper_Box remaining space = 3 then

order 1 load from OL_WrappertoBoxFull to continue //One box of wrappers from

molding process

wait to be ordered on OL_WrappertoPainting // wait to be ordered by painting process

end

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Painting and Assembly

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

/*Order Generation*/

begin P_Order arriving procedure


wait until V_PaintingNum(procindex) = V_OrderSize(procindex)

begin

//Order size distribution

set V_OrderSize1 to uniform 4,2


set V_OrderSize3 to uniform 190.5,170.5


set V_OrderSize(procindex) to oneof(30:1, 20:V_OrderSize1,

20:V_OrderSize2, 20:V_OrderSize3, 10:V_OrderSize4)

//Set the related variables in downstream processes

set V_PaintingNum(procindex) to 0

set V_AssemblyNum(procindex) to V_OrderSize(procindex)

order V_OrderSize(procindex) loads from OL_WrappertoPainting to

P_Painting(procindex)

in case order not filled backorder on OL_WrappertoPainting

inc V_NumOfOrders by 1 // Counting order numbers

use R_Operator2 for uniform 3, 1 min // Loading program for Painting

machine

end

end

end

/*Painting*/

begin P_Painting arriving procedure

set priority to 1

move into Q_Painting(procindex)

// Filling colors for Painting Machine

inc V_Paint(procindex) by 1

if V_Paint(procindex) = 2000 then

begin

use R_Operator2 for normal 7, 1 min // Filling color

set V_Paint(procindex) = 0

end

// Check if wrappers run out

inc V_Runout(procindex) by 1

if V_Runout(procindex) = 200 then

begin

use R_Operator2 for uniform 3.5, 0.5 min //Fetching the Wrapper Boxes takes

2-3 minutes

set V_Runout(procindex) = 0

end

//Cycle time of Painting

get R_Painting(procindex)

set R_Painting(procindex) state to WorkingPainting

wait for 42.4 sec

free R_Painting(procindex)

inc V_PaintingNum(procindex) by 1 // Counting the finished painting numbers which is used

to compare with order size

send to P_AssemblyLoading(procindex)

end

/*Conveyor*/

begin P_AssemblyLoading arriving procedure

move into Assembly.ConvLoading


begin

travel to Assembly.AssemblySt1

send to P_Assembly(1)

end

else if procindex = 2 then

begin



end

else if procindex = 3 then

begin



end

else

begin

travel to Assembly.AssemblySt1 // The products from painter 4 go to

assembly station 1


end

end

/*Final Assembly*/

begin P_Assembly arriving procedure

move into Q_Assembly(procindex)

//Check Lids and Cups which are waiting in the middle storage

if OL_LidtoAssembly current = 0 then

wait to be ordered on OL_WaitingforLids

order 1 load from OL_LidtoAssembly to continue

if OL_CuptoAssembly current = 0 then

wait to be ordered on OL_WaitingforCups

order 1 load from OL_CuptoAssembly to die

//Assembly Coffee Cup

use R_Assembly(procindex) for weibull 3, 28 sec

//Extra Packaging Time

if V_AssemblyNum(procindex) > 100 then

begin

use R_Assembly(procindex) for weibull 1.5, 250 sec

set V_AssemblyNum(procindex) = 0

end

else if V_AssemblyNum(procindex) > 10 then

begin



end


begin



end

// Taking lids from middle storage when it runs out

inc V_Assembly(procindex) by 1

if V_Assembly(procindex) = 100 then

begin

set V_Assembly(procindex) = 0

use R_Assembly(procindex) for uniform 2, 1 min

end

// Printing the good coffee cups and average speed

print "Ok Products: " Q_CoffeeCup total " products" to FinishedProducts

set V_Speed = Q_CoffeeCup total /(ac/3600)

print "Average Speed: " V_Speed " products per hr" to Speed

move into Q_CoffeeCup

end

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Maintenance

Processes %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

//Stamping Maintenance

begin P_DownTime_Stamping1 arriving procedure

set priority to 1

while 1=1 do begin

wait for e 12 hr /* MTTF */

take down R_Stamping

set R_Stamping state to InFailureStamping

get R_Maintenance

wait for triangular 12, 30, 35 min /* MTTR */

bring up R_Stamping

free R_Maintenance

end

end

// Cleaning of Cutting machine

begin P_Cutter_cleaning arriving procedure

while 1=1 do begin

wait for 8 hr

take down R_Cutting

set R_Cutting state to InCleaningCutting

get R_Operator1

wait for uniform 8.5, 3.5 min /* Cleaning Cutting Machine */

bring up R_Cutting

free R_Operator1

end

end

// Polishing Maintenance

begin P_DownTime_Polishing1 arriving procedure

set priority to 2

while 1=1 do begin


take down R_Polishing(1)

set R_Polishing(1) state to InFailurePolishing

get R_Maintenance


bring up R_Polishing(1)

free R_Maintenance

end

end


set priority to 2

while 1=1 do begin




get R_Maintenance



free R_Maintenance

end

end


set priority to 2

while 1=1 do begin




get R_Maintenance



free R_Maintenance

end

end

// Molding Maintenance

begin P_DownTime_LidMolding arriving procedure

while 1=1 do begin


take down R_Molding(1)

set R_Molding(1)state to InFailureMolding

get R_Operator1

wait for normal 18, 2 min /* MTTR */

bring up R_Molding(1)

free R_Operator1

end

end

begin P_DownTime_WrapperMolding arriving procedure

while 1=1 do begin


take down R_Molding(2)

set R_Molding(2)state to InFailureMolding

get R_Operator1


bring up R_Molding(2)

free R_Operator1

end

end

//Lid assembling Maintenance

begin P_DownTime_LidAssembly arriving procedure

while 1=1 do begin


take down R_LidAssembly

set R_LidAssembly state to InFailureLidPolishing

get R_Operator2


bring up R_LidAssembly

free R_Operator2

end

end

// Painting Maintenance

begin P_DownTime_Painting1 arriving procedure

while 1=1 do begin

wait for weibull 1.011, 14.4 hr /* MTTF */

/*wait for e 15 hr*/

take down R_Painting(1)

set R_Painting(1) state to InFailurePainting

get R_Maintenance

wait for normal 9, 1 min

bring up R_Painting(1)

free R_Maintenance

end

end


while 1=1 do begin




get R_Maintenance



free R_Maintenance

end

end


while 1=1 do begin




get R_Maintenance



free R_Maintenance

end

end


while 1=1 do begin




get R_Maintenance



free R_Maintenance

end

end

// Conveyor Maintenance

begin P_DownTime_Conveyor arriving procedure

while 1=1 do begin


take down Assembly.M_sec1 /* Decelerate to a stop */

wait for weibull 1.5, 90 min /* MTTR */

bring up Assembly.M_sec1 /* Return to previous velocity */

end

end

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Short Breaks by Operators

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

begin P_Operator1ShortBreak arriving procedure

while 1=1 do begin

wait for weibull 2, 4 hr /* Breaks occuring interval */

get R_Operator1

wait for normal 5, 0.5 min /* Break time */

free R_Operator1

end

end


while 1=1 do begin


get R_Operator2


free R_Operator2

end

end


while 1=1 do begin


get R_Operator3


free R_Operator3

end

end

begin P_Assembler1ShortBreak arriving procedure

while 1=1 do begin


get R_Assembly(1)


free R_Assembly(1)

end

end


while 1=1 do begin


get R_Assembly(2)


free R_Assembly(2)

end

end


while 1=1 do begin


get R_Assembly(3)


free R_Assembly(3)

end

end

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% End of

the basic tasks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

/* %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Extra Taks

A %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

/* Change in StampingInitialization*/

begin P_InitialStamping arriving procedure


create V_OrderPoint_Sheet loads of load type L_Sheet to P_Stamping_Filling

wait to be ordered on OL_SheetStock

wait for t 3, 7, 9 hr

end

end

begin P_Stamping_Filling arriving procedure

move into Q_SheetStorage

create 1 load of load type L_Sheet to P_LoadingSheet

wait to be ordered on OL_Sheets

end

begin P_CheckSheet arriving procedure


if Q_SheetStorage remaining space =< V_OrderPoint_Sheet then

order 1 load from OL_SheetStock to continue

end

end

/* Change in Molding Initialization*/

begin P_InitialMolding arriving procedure


wait to be ordered on OL_PlasticStock

wait for t 3, 7, 9 hr

clone V_OrderPoint loads to P_PlasticStock

end

end

begin P_PlasticStock arriving procedure

move into Q_Plastic

wait to be ordered on OL_LoadingPlastic

if V_OrderPoint =< Q_Plastic remaining space then

order 1 load from OL_PlasticStock to continue

send to die

end

/* %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% End of

Taks A %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

/* %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Extra Taks

B %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

//Change in begin P_Print process

set V_PolishingIdleEnergy(1) = 1800 -(R_Polishing(1) WarmupPolishing total * R_Polishing(1)

WarmupPolishing average time)/3600 -


R_Polishing(1) WorkingPolishing average time)/3600









set V_PolishingActiveEnergy(1) = (2*(R_Polishing(1) WorkingPolishing total * R_Polishing(1)






print "Idle Energy: "V_PolishingIdleEnergy(1) to LBL_IdlePoli shingEnergy(1)

print "Idle Energy: "V_PolishingIdleEnergy(2) to LBL_IdlePolishingEnergy(2)

print "Idle Energy: "V_PolishingIdleEnergy(3) to LBL_IdlePolishingEnergy(3)

print "Active Energy: "V_PolishingActiveEnergy(1) to LBL_ActivePolishingEnergy(1)







print "Total Energy: "(V_PolishingIdleEnergy(3)+ V_PolishingActiveEnergy(3)) to


set V_Total Energy = V_PolishingIdleEnergy(1) +

V_PolishingActiveEnergy(1)+V_PolishingIdleEnergy(2) + V_PolishingActiveEnergy(2)+

V_PolishingIdleEnergy(3)+ V_PolishingActiveEnergy(3)

/* %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% End of

Task B %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

/* %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Extra Taks

C %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/

/*Change of middle storage size */

set Q_LidMS_Wait capacity to V_M

set Q_WrapperMS_Wait capacity to V_M

set Q_CupMS_Wait capacity to V_M

/*Change of Order Generation*/

begin P_OrderArrival arriving procedure

while 1=1 do begin

create 1 load of load type L_order_arrive to

nextof(P_Order(1),P_Order(2),P_Order(3),P_Order(4))

inc C_TotalOrders by 1 // Number of total orders

wait for weibull 2, V_X min /* order arriving interval */

end

end

begin P_Order arriving procedure

wait until V_PaintingNum(procindex) = V_OrderSize(procindex)





set V_OrderSize(procindex) to oneof(30:1, 20:V_OrderSize1, 20:V_OrderSize2,

20:V_OrderSize3, 10:V_OrderSize4)

set V_PaintingNum(procindex) to 0

order V_OrderSize(procindex) loads from OL_WrappertoPainting to P_Painting(procindex)

in case order not filled backorder on OL_WrappertoPainting

wait until V_Finished(procindex) = 0 // Set the variables in assembly process when the it

finished the order size, V_Finished(procindex) is checked in assembly process

set V_AssemblyOld(procindex) to V_OrderSize(procindex)

set V_AssemblyNum(procindex) to V_OrderSize(procindex)

use R_Operator2 for uniform 3, 1 min // Loading program for Painting machine

end

/*Change of Painting*/

begin P_Painting arriving procedure

set priority to 1

move into Q_Painting(procindex)

// Filling colors for Painting Machine

inc V_Paint(procindex) by 1

if V_Paint(procindex) = 2000 then

begin

use R_Operator2 for normal 7, 1 min // Filling color

set V_Paint(procindex) = 0

end

// Check if wrappers run out

inc V_Runout(procindex) by 1

if V_Runout(procindex) = 200 then

begin

use R_Operator2 for uniform 3.5, 0.5 min //Assumption for moving the

Wrapper Boxes takes 2-3 minutes

set V_Runout(procindex) = 0

end

//Painting

get R_Painting(procindex)

set R_Painting(procindex) state to WorkingPainting

wait for V_PaintingCycleTime sec // Change cycle time of painting to a variable

free R_Painting(procindex)

inc V_PaintingNum(procindex) by 1

send to P_AssemblyLoading(procindex)

end

/*Change of Conveyor*/

begin P_AssemblyLoading arriving procedure

move into Assembly.ConvLoading


begin



end


begin



end


begin



end


begin

travel to Assembly.AssemblySt4 // Another assembly station is added which connects

with painter 4


end

end

/*Change of Final Assembly*/

begin P_Assembly arriving procedure

move into Q_Assembly(procindex)

if OL_LidtoAssembly current = 0 then

wait to be ordered on OL_WaitingforLids

order 1 load from OL_LidtoAssembly to continue

if OL_CuptoAssembly current = 0 then

wait to be ordered on OL_WaitingforCups

order 1 load from OL_CuptoAssembly to die

//Assembly Coffee Cup

use R_Assembly(procindex) for weibull 3, 28 sec

//Extra Packaging Time

if V_AssemblyNum(procindex) > 100 then

begin



end


begin



end


begin



end

// Take lids from middle storage when it runs out

inc V_AssemblyLids(procindex) by 1

if V_AssemblyLids(procindex) = 100 then

begin

set V_AssemblyLids(procindex) = 0

use R_Assembly(procindex) for uniform 2, 1 min

end

//Check if the order is finished

inc V_Finished(procindex) by 1

if V_Finished(procindex) = V_AssemblyOld(procindex)

begin

set V_Finished(procindex) to 0

inc C_TotalFinished by 1 // Counting the finished orders

end

move into Q_CoffeeCup

end

/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%% End of Task C

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%*/

Simulation of production systems: A case study for stainless steel insulated coffee cup...

Engineering

Transcript of Simulation of production systems: A case study for stainless steel insulated coffee cup...