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Internship: Simulation and evaluation of logistic systems

DC2016.086

AuthorJ.J.M.J.P Meens - 0766368

InstructorProf.dr.ir. I.J.B.F. Adan

November 28, 2016

Simulation and evaluation of logistic systems

Abstract

In this report it is investigated how different decision options regarding which jobshould be transported by a forklift truck influences the system performance of a job shopmodel. Therefore, a basic job shop model having multiple workstations is modelled inAnylogic [1], which is a simulation tool supporting system dynamics, discreet event, andagent based modelling. After verifying the basic Anylogic model, the model is expandedby adding multiple decision options of which job a forklift truck should transport. Fur-thermore, the influence of an autocorrelated arrival process is investigated. Therefore,the arrival process is changed in an autocorrelated arrival process, which changes thequeue sizes and the throughput of the system significantly.

Additionally, it is investigated how increasing operator qualification and applying analternating release rate of jobs influences the performance of a wafer fab. Because firmsrarely reveal data for investigation, a general used wafer fab Factory Explorer model isused for these investigations. Factory Explorer [2] is a performance analysis simulationtool using excel files as input. The increasing operator qualification is simulated bycombining operator groups which results in a decrease in cycle time and WIP level.Furthermore, depending on the applied alternation period of the release rate of jobs, thewafer fab performance increases or decreases.

TU/e Mechanical Engineering 3

Contents

1 Introduction 1

2 Job shop model in Anylogic 2

2.1 Basic model description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.3 Basic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.4 Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.5 Expansion of basic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 Operator group and release rate influences in a wafer fab 18

3.1 Influence of operator qualification . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.1.1 Experiment plan: Operator group utilization . . . . . . . . . . . . . . 19

3.1.2 Results: Operator group utilization . . . . . . . . . . . . . . . . . . . 21

3.1.3 Experiment plan: Combining operator groups . . . . . . . . . . . . . 24

3.1.4 Results: Combining operator groups . . . . . . . . . . . . . . . . . . . 26

3.1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.2 Effects of an alternating release strategy in a wafer fab . . . . . . . . . . . . . 30

3.2.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

References 34

A Appendix 35

A.1 Data storage Anylogic model . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

A.2 Workstation performance job shop model . . . . . . . . . . . . . . . . . . . . 36

A.3 WIP and cycle time figures operator utilization . . . . . . . . . . . . . . . . . 37

A.4 Issue report Factory Explorer . . . . . . . . . . . . . . . . . . . . . . . . . . . 39


1 Introduction

In this report, two topics are discussed regarding logistic systems. The first topic is abouta job shop system, modelled in Anylogic [1] to investigate how the system changes whenapplying an autocorrelated arrival process. Furthermore, it is investigated how the systemreacts to different decision making options, deciding which product a forklift truck shouldtransport first. This Anylogic model and the results of these experiments are discussed inSection 2. The second topic investigates the influences of operator groups and the arrivalprocess of jobs in a wafer fab. The wafer fab is modified and analysed by using FactoryExplorer [2]. Section 3.1 discusses how both the utilization of operator groups and combiningoperator groups effects the wafer fab performance. The last section discusses the effects ofapplying an alternating product start rate in two different wafer fab systems. The firstsystem has a factory capacity loading of approximatily 90% applying First In First Out asdispatching rule, whereas, the second system has a capacity loading of approximatily 96%applying Critical Ratio as dispatching rule.



2 Job shop model in Anylogic

In this section we describe how AnyLogic [1] is used to build a job shop model, having multipleworkstations and job types. Furthermore, forklift trucks are used to transport the jobs. Givena description of the basic job shop model, an Anylogic model has to be made which is easillyadjustable. Furthermore, the model is used to investigate how the forklift transport influencesthe system performance, by looking at different decision procedures regarding which joba forklift truck should transport. Also, the influence of an autocorrelated arrival processis investigated. Before discussing the Anylogic model and the results, a detailed modeldescription is given in Section 2.1, followed by the assumptions that are made, discussed inSection 2.2. In Section 2.3, the basic Anylogic model is explained and verified in Section 2.4.After discussing the basic model, the Anylogic model is expanded to investigate the influenceof the pickup decision a forklift truck makes and what the influences of an autocorrelated jobarrival process. After explaining the implementation of these additional features, the resultsare discussed. The last part is the conclusion including some recommendations to furtherimprove the model.

2.1 Basic model description

The model represents a system with 5 workstations and a receiving/shipping station ascan be seen in Figure 2.1. This model description is based on the Section An extendedexample in Chapter 13 of the book Simulation Modeling and Analysis [3]. Jobs arrive atthe receiving/shipping station with an exponential arrival process having an average of onejob arriving every four minutes. After arrival the jobs will be transported to a workstationby a forklift truck. The system will process three job types, each having its own route andaverage processing time. Furthermore, the probability of a job arriving being of a type isspecified. All these data regarding the job types are given in Table 2.1.Each workstation has one queue applying First In First Out (FIFO) as dispatch rule and oneor more identical machines. Furthermore, after processing a product, a machine is blockeduntil a forklift truck takes the product. Both the number of machines in a workstation andthe number of forklift trucks are model parameters, which means that these should be easilychangeable. The processing times are Gamma distributed with a shape parameter of 2 anda mean value specified in Table 2.1.

Table 2.1: Job types with additional probability, routing and mean processing times

Job Type Probability Workstations in routing Mean service time for successiveoperations (hours)

1 0.3 3,1,2,5 0.25, 0.15, 0.10, 0.302 0.5 4,1,3 0.15, 0.20, 0.303 0.2 2,5,1,4,3 0.15, 0.10, 0.35, 0.20, 0.20

As explained before, the transportation of jobs between the different stations is done byforklift trucks, moving with a constant speed of 5 feet per second. The distances betweenthe stations are given in Table 2.2, these distances are given in feet.

The transport logistics can be divided in two separate situations. The first situation is theone of multiple products already waiting for transport at the time a forklift truck becomesavailable. In this situation the forklift truck should take the nearest job. If there are oneor more forklift trucks available when a job becomes available for transport, this job is



Figure 2.1: System layout

Table 2.2: Distance table (feet)

Station 1 2 3 4 5 R/S

1 0 150 213 336 300 1502 150 0 150 300 336 2133 213 150 0 150 213 1504 336 300 150 0 150 2135 300 36 213 150 0 150R/S 150 213 150 213 150 0

transported by the nearest available forklift truck. When a forklift truck delivered a productto a workstation and there is no other job available to transport, the forklift truck waits atthe workstation where it delivered its last job until a job becomes available for transport.

2.2 Assumptions

Some additional assumptions made for modelling the job shop system in Anylogic are dis-cussed in this subsection. The first assumption is that the job longest waiting for transportis taken if two or more jobs have the same priority of being transported. Furthermore, be-tween a forklift delivering a job and deciding what job it should transport next, there is nodelay. Also, when a forklift truck is waiting for a job becoming available for transport, itwill react immediately when a job becomes available for transport. The decision making ofthe forklift trucks is static, meaning that when a forklift truck decides which job to take, itwill not change its decision, even when a job becomes available closer to the forklift truck atthe point the forklift is moving towards the job it will take. All the simulations are executedapplying random seed, forcing the simulations to be uncorrelated.

2.3 Basic model

The first step is modelling the basic model as described in the Section 2.1. The description ofthe Anylogic model describes the steps a product will undergo in the system. Furthermore,



it will be discussed how the decision making of a forklift truck is implemented to guaranteethat a forklift truck will take the right product. First the main file will be discussed followedby the receiving/shipping station and the workstations. Furthermore, the visualisation ofthe model will be discussed shortly and the solution to guarantee a forklift taking the correctproduct is discussed.

Main

First the Main file is discussed, in which the five workstations and the receiving/shippingstation are connected which each other. These connections can be seen in Figure 2.2, inwhich also a source, a couple of select outputs and the ForkliftTrucks resource pool arelocated. Jobs enter the system via the source with interarrival times that are exponentialrandom variables with a mean of one job every four minutes. Furthermore, the releasedjob obtains one of the three recipes, in which the routing and processing times are stored.These recipes are stored in a LinkedHashMap and each recipe has an ArrayList containingthe routing and the corresponding processing times.

Figure 2.2: Main file

All jobs released by the source will enter the receiving/shipping station, where they will wait



for a forklift truck to transport them to the first workstation defined in the recipe of thejob. After a forklift truck arrives to transport the job, the job will be transported to oneof the workstations via the outputReceiving shipping. This outputReceiving shipping

block has five exits, making it possible to directly move from the receiving/shipping to one ofthe five workstations. Furthermore, as it can be seen in Figure 2.2, after every workstationthere is a block located, to move a job directly to one of the other workstations or thereceiving/shipping station where the job will exit the system again. Although the forklifttrucks will seize and release jobs in the stations, the forklift trucks are defined in the mainfile, because they will travel between the different stations. The next section will describethe receiving/shipping block.

Receiving/shipping station

The receiving/shipping station is the first station a job enters. The Anylogic model ofthis station is shown in Figure 2.3. Every job entering the receiving/shipping station willfirst enter the SelectOutput called toSink, which will separate jobs leaving the system andentering the system. If a job enters the system, the job is send to the seizeForkliftTruck

block, otherwise a job will be send to the releaseForklift block where the forklift truckwill release the job, and the job will leave the system via the sink. However, if a job enters thereceiving/shipping station for the first time, it will try to seize a forklift truck. When thereis a forklift truck available and it is decided that the job seizes the forklift truck, the forklifttruck starts travelling towards the receiving/shipping station via the recourceTaskStart

block. Furthermore, the waitingForforkliftTruck delay simulates the time it takes beforethe forklift truck arrives at the receiving/shipping station. How the decision is made ofwhich job seizes an available forklift is discussed in Section 2.3. When the forklift truckarrives at the receiving/shipping station, the job is transported to the next workstation.The transportDelay delay simulates the transport to the next workstation, where after thejob leaves the receiving/shipping station.

Figure 2.3: Receiving/shipping station



Workstation

After a job leaves the receiving/shipping station, a forklift truck transports the job to aworkstation. There is one general workstation model, which can be seen in Figure 2.4,since the only difference between the workstations is the number of machines used in theresource pool Machines. Therefore, the number of machines in a workstation is defined bya parameter, which is modifiable in the simulation window before starting a simulation.

Figure 2.4: Workstation

After a job enters a workstation, the forklift truck releases the job and the job tries to seizeone of the machines. If all machines are busy or blocked or there are other jobs waiting to seizea machine, the job waits until it can seize a machine. After seizing, the job will be processed,simulated by the processingdelay. As described in the assignment, the processing timeof a job is Gamma distributed with a fixed shape parameter of 2. Furthermore, the recipeof the job carries the mean value of this processing time. Both the fixed shape parameterand the mean processing time of the job are used to calculate the processing time of thedelay. If a process finishes, the machine is blocked until a forklift truck removes the job.Therefore, the job first seizes a forklift truck before it releases the machine and afterwardsthe transportdelay simulates the transport to another station. Modelling the seizing andreleasing of machines and forklift trucks this way, guarantees that the machine does not seizeanother product before the product it processed before is taken by a forklift truck.



Visualisation

To analyse the behaviour of the system easier, a simple visualisation is added in the Main

file. This visualisation, as can be seen in Figure 2.5, exists of 5 red factories, representingthe workstations, and one grey square which is the receiving/shipping station. Furthermore,the workstations all have two nodes, one inside for products which are in progress or in thequeue, and one at the edge for products or forklift trucks waiting for transport.

Figure 2.5: Visualisation

The receiving/shipping station only has the node at the edge, since there is no process insidethis station. All the nodes located at the edge of a station are connected with each other,making it possible for the forklift trucks to drive from one point to another. The movementof the forklift trucks and jobs is specified by using the following two lines:

• jumpTo(INode node)

• moveToInTime(INode node, double tripTime, TimeUnits units)

The first one is used to place the jobs at the edge of a station when it is waiting for transportand inside a workstation when it is in progress or waiting in the queue. Furthermore, thefirst line is used to initially place the forklift trucks at the receiving/shipping station. Thesecond line is used to define the movement of the forklift trucks, with or without a job,because the forklift truck seizes the job, it will also carry it in the visualisation.

Seizing forklift trucks

This section discusses the seizing step between a forklift truck and a job more in detail byfocussing on how a forklift seizes the right job. Looking to a seize block in Anylogic, itis possible to enter a Resource choice condition in the advanced properties, as can beseen in Figure 2.6. This option makes it possible to seize a specific forklift truck with theseizeForkliftTruck block in a specific workstation by giving the variable seizeVariable

of a forklift truck and the seizeVariable of the job the same value. The determination ofthese two variables can be divided into two different situations:

• A job becomes available for transport and all forklift trucks are busy.



• A job becomes available for transport and one or more forklift trucks are idle.

Figure 2.6: Seize properties

In case a product becomes available for transport and all forklift trucks are busy, the jobwaits in the machine until a forklift truck takes it for transport. At the point a forklifttruck becomes idle, it should know what job it should transport. Therefore, jobs availablefor transport are added to the list jobList. This list stores the time point a job entered thesystem and the position a job is waiting as type Job. The time point a job entered the systemis used as an identifier, since it is not possible for jobs to enter the system at exactly the sametime. Furthermore, the location of a job is important to determine which job a forklift truckshould transport. If a job becomes available for transport when all forklift trucks are busy,the seizeVariable of the job is set to the time the job entered the system. The value of theseizeVariable of a forklift truck is determined when it finishes transporting a job. Becausethere are jobs available for transport, the forklift truck should immediately transport a nextjob. To determine which job it should transport, a Java class uses the current location of theforklift truck and the jobList to determine which job has the lowest distance to the forklifttruck. If there are multiple jobs available for transport in the nearest workstation, the job



waiting the longest is returned by the Java class. The Java class returns the Job the forkliftshould transport and the seizeVariable of the forklift truck is set to the system entry timeof the job. Furthermore, the Job returned by the Java class is deleted from the jobList.By setting the seizeVariable of the forklift truck to the system entry time of the job itshould transport, the seize condition holds only for this combination, resulting in the forklifttransporting the correct job.

One or more forklift trucks being idle when a job becomes available for transport is theother situation. When a forklift truck finishes transport and the jobList is empty, it waitsat the workstation where it delivered it’s last job. Simular to storing jobs in the jobList,the forklift is now stored in the forkliftTruckList. This is an integer list containingthe current locations of the forklift trucks (workstation1 = 1, workstation2 = 2 , ... ,receiving/shipping = 6). Furthermore, the seizeVariable of the forklift truck is set tothe current location of the forklift truck. If a job becomes available for transport and oneor more forklift trucks are idle, a Java class simular to the one used to determine the joba forklift should seize, is used to determine the seizeVariable of the job. This Java classuses the current location of the job and the forkliftTruckList to determine which forklifttruck is the closest to the job. The Java class returns the location of the closest forklift truckand the seizeVariable of the job is set to this forklifts location. The job seizes the closestforklift for transportation and this forklift truck is deleted from the forkliftTruckList.

2.4 Verification

To verify the behaviour of the basic model, the results obtained from the basic Anylogicmodel are compared with the results in the book [3]. This comparison is done for both astable and unstable system, where unstable means that the queue sizes and average timea job is in the system is not constant but increasing over the simulation time. The sim-ulations are executed for the same run time as given in the book, the run length is 920hours of which 120 are used for start up. The differences between the stable and unstablesystem are the number of machines in a couple of workstations and the number of forklifttrucks. The results of the Anylogic simulations are the average values of 100 replications,where the results mentioned in the book are obtained by taking the average of 10 replications.

The first results shown below in Table 2.3 are of the unstable system. As can be seen the dailythroughput is approximately the same for both systems. Furthermore, there is a differencein the average time a job is in the system and in the queue which are approximately 6%higher. Looking to the average total wait for transport, the difference between the modelsis approximately 26%. The standard deviations of the Anylogic results are also included inthe table.



Table 2.3: Comparing results of book model and Anylogic model for an unstable system

Number of machines: 4,1,4,2,2Number of forklifts: 1

Book model Anylogic model Standard deviation

Average daily throughput (jobs) 94.94 92.88 0.96Average time in system (hours) 109.2 114.63 5.74Average total time in queues (hours) 107.97 113.51 5.73Average total wait for transport (hours) 0.42 0.31 0.004Proportion forklifts moving loaded (%) 0.77 0.76 0.002Proportion forklifts moving empty (%) 0.22 0.23 0.002

Looking to these results and differences, it cannot be concluded that the Anylogic modelbehaves the same as the model used to get the results shown in the book. Because thebig difference in the wait for transport time, the total transportation time for each job ischecked and also the total average processing time of the job types are checked by takingthe average. However, the values of these times are the same as they should be looking tothe Tables 2.1 and 2.2 and calculating the total mean service time for each job type and thetotal transportation time. Furthermore, the time points of a forklift truck leaving a stationand arriving at another station are plotted during a couple of simulations to check the travelspeed and distances manually, resulting in the conclusion that also these values are correct.It looks like the Anylogic model behaves slightly different, which can be explained by thefollowing points:

• Difference in simulation feeds (high standard deviation)

• FIFO when jobs have same priority of transport

• Number of replications

• There may be more model features which are not given in the book but are implementedin their model.

Besides the overall system performance, also the individual workstation performance is anal-ysed. These results are discussed in Appendix A.1.

While the simulations of the Anylogic model are executed with a random feed without cor-relation, the book uses the common random numbers method which results in the followingbehaviour to suppress the variance between simulations: ”This will guarantee that a par-ticular job will arrive at the same point in time, be of the same job type, and have thesame sequence of service-time values for all system designs on a particular replication. Jobcharacteristics will, of course, be different on different replications.” [3]. Furthermore, theAnylogic model seizes the longest waiting transport job if there are more jobs waiting fortransport having the same distance to the available forklift truck. It is not known whatthe model used to obtain the results of the book does in this case. Moreover, the numberof replications is different, since the Anylogic model is simulated ten times more than themodel used in book. Therefore, it is difficult to say something about the correctness of theAnylogic model, however, all the times regarding transport and processing as well as therouting of the jobs is checked manually and there is no failure found.



As mentioned before, the system is also compared for a stable situation, of which the resultsare shown in table 2.4.

Table 2.4: Comparing results of book model and Anylogic model for a stable system


Book model Anylogic model Standard deviation

Average daily throughput (jobs) 120.29 120.09 0.88Average time in system (hours) 1.76 1.7 0.10Average total time in queues (hours) 0.86 0.81 0.10Average total wait for transport (hours) 0.08 0.07 0.001Proportion forklifts moving loaded (%) 0.44 0.44 0.003Proportion forklifts moving empty (%) 0.27 0.27 0.002

For this system, the differences between the obtained data are slightly smaller, however, notethat there is again a big difference between the values obtained for the Average total waitfor transport, having a low standard deviation value.

Although there are differences between the Anylogic model and the model in the book, it isdecided to use this model to make a more flexible model to investigate autocorrelation onthe arrival process and different decision making of a forklift truck.

2.5 Expansion of basic model

As discussed in the introduction, the basic model is used to implement additional decisionstrategies for the forklift trucks and the effect of an autocorrelated arrival process is inves-tigated. Regarding the forklift truck decision making, only different strategies are added forthe situation that there are jobs waiting for transport at the point a forklift truck becomesavailable again. Furthermore, a positive and negative autocorrelation is applied to the arrivalprocess. Both the decisions making and the autocorrelation are discussed more into detailin the following two subsections.

Forklift truck decision making

As mentioned above, the decision making regarding which product a forklift truck takes afterdelivering a job is changed into a system parameter. Note that if there is no job available fortransport after a forklift truck delivering a job, the basic decision making still holds. For thecase of one or more jobs available for transport, five decision making strategies are added,resulting in total six forklift decision options:

• FIFO (First In First Out): the job which entered the job list first will be transported.

• LTS (Longest System Time): job longest in the system will be transported.

• SDF (Shortest Distance First): nearest job is transported.

• TSDF (Total Shortest Distance First): job is transported for which the combinationsof the travel distance from forklift to job and job to destination is the shortest.



• BQS (Biggest Queue Size): job waiting at the workstation with the largest queue willbe transported.

• JT (Job Type(3,2,1)): jobs of type 3 will be transported first, after it jobs of type 2and as last jobs of type 1.

For each decision option, a separate Java class is created, returning the job the forkliftshould take, resulting in the corresponding value for the seizeVariable variable of theforklift truck.

However, the basic jobList carries not enough information to execute all of the additional de-cision options. Therefore, the jobList is modified. The modified jobList lists the datatypeJob, which carries:

• current location of a job

• next destination of a job

• distance from the current location to the destination (automatically calculated)

• the time point a job entered the system

• the priority of a job

Furthermore, the decision making Java classes also use the location of the forklift truck anda list carrying the current queue size of each workstation to return the right job the forklifttruck should take. What information is used by the decision making depends on the optionselected in the window before a simulation is started.

Autocorrelated job arrival process

In this part, the implementation of an autocorrelated arrival process is discussed. To im-plement autocorrelation in Anylogic, JARTA [4] is used. JARTA is a Java library to modeland fit Autoregressive-To-Anything (ARTA) processes and is used to generate an autocorre-lated arrival process. Although most arrival processes for simulations are modelled withouta certain dependency, some of the arrival processes in the industry have a certain depen-dency. Therefore this Anylogic model is used to investigate the influences of such an arrivalprocess on a job shop model. The autocorrelation is used to model such a dependency inthe arrival process, which can have a big impact on the system behaviour and the queue sizes.

Applying autocorrelation introduces dependencies between the interarrival times of the jobs,applying positive autocorrelation will result in more jobs arriving close to eachother, followedby a period of less jobs arriving. Note that the average number of jobs arriving is still one jobevery 4 minutes and that their interarrival times are exponentially distributed. Furthermore,a negative autocorrelation coefficient results in more evenly spread interarrival times. Moredetailed information about the autocorrelation can be found in the papers Jarta- a javalibrary to model and fit autoregressive-to-anything processes [4] and Evaluation of modelingtools for autocorrelated input processes [5].

To investigate the influences of autocorrelation, positive (α = 0.3) and negative (α = −0.3)autocorrelation is applied. The results of these experiments are shown in the section below.



2.6 Results

This section discusses the results obtained by the experiments regarding the forklift decisionmaking and the autocorrelated arrival process. However, first the changability of the modelis discussed.

Easy adjustable model

One of the tasks was modelling an easy adjustable system. As described before, all theforklift decision making options are modelled in separate Java classes, making it easier tochange an option or add new options by making new Java classes. Furthermore, there is oneJava class used to calculate all the distances and travel times. This class is the only part inthe system in which the distance table is defined. So if some distances between workstationsshould be changed, it is only necessary to adjust them in this class.

The system parameters as defined by the basic model can be set in the simulation window,which opens before running a simulation. This simulation window is shown in Figure 2.7.As it can be seen, it is possible to set the amount of machines and the amount of forklifttrucks. Furthermore, it is possible to choose which decision making option should be usedduring the simulations, adjust the speed of a forklift truck and set both the start up timeand the total run time. Furthermore, also the routing and the average process times of jobsare easily adjustable, because they are defined just once in the system.

Figure 2.7: Simulation Window

As discussed above the model is flexible, since it can be adjusted and the parameters can bespecified in the simulation window. However, it is not easy to expand the model to a systemhaving more than 5 workstations. Adding a workstation has to be done by placing anothergeneral workstation model in the main file and connect it with all the other workstations,furthermore, the the workstation parameters, such as the id of the workstation should beset. Also the visualisation of the model has to be changed.



Forklift behaviour

The experiments regarding the forklift truck decision making are simulated with the unstableconfiguration, having just one forklift truck and the number of machines in each workstationare: 4,1,4,2,2. The reason to simulate the unstable system is simply due to the fact that theinfluence of the differences in decision making have a bigger impact. The impact of changingthe decision making of a forklift truck in the stable system is less, because most of the timeit will be the case that a forklift truck is waiting for jobs to be available for transport.

The results of the simulations are shown in Table 2.5. The results are obtained by executing50 replications for each decision making option and taking the average of the results.

Table 2.5: Influences forklift truck decision makings unstable system

FIFO LST SDF TSDF BQS JT

Average daily throughput (jobs) 56.18 77.88 92.88 85.35 78.50 46.19Average time in system (hours) 264.09 182.50 114.63 139.72 180.17 293.83Average total time in queues (hours) 262.49 0.36 113.51 138.64 0.43 292.24Average total wait for transport (hours) 0.81 182.51 0.31 0.28 178.91 0.81Proportion forklifts moving loaded (%) 0.58 0.57 0.76 0.7 0.57 0.58Proportion forklifts moving empty (%) 0.42 0.43 0.23 0.28 0.43 0.41

As can be seen in Table 2.5, in general the option forcing the forklift truck to take thenearest job results in the best results. This result is the best, because the throughput is thehighest and the time a product is in the system is the lowest. However, if it is necessary tohave the lowest queue sizes, the LST option works the best. Note that this results in manyjobs waiting for transport at the receiving/shipping station. Furthermore, if it is necessaryto optimize the waiting for transport time, it can be seen that the TSDF option is slightlybetter than the LST option. The worst results in are obtained by selecting the JT option.

Autocorrelated arrival process

In this part, the results of the autocorrelated interarrival times are shown. Also for theseexperiments, 50 replications are executed for each experiment. Furthermore, the averagequeue sizes are included in the results table, because autocorrelation effects these. Theseexperiments are executed for the stable system as discussed in Section 2.4.

The results are in shown in Table 2.6. As can be seen in the results, autocorrelation has a bigimpact on the average queue seizes as well as the total time a job is in a queue. A positiveautocorrelated arrival process results in bigger queues, resulting in a increase in the time a jobis in the system and a decrease in daily throughput. Furthermore, a negative autocorrelatedarrival process results in smaller queues and a increase in the daily throughput.

To visualise the effect of autocorrelation on queues better, the queue size of workstation 5is plotted during simulations with positive, negative and without applying autocorrelation.First the queue size over time is plotted for a system without applying autocorrelation, ascan be seen in Figure 2.8. This figure shows one big peak around 740 hours, with a queuesize of approximately 48. Furthermore, there are a couple of peaks with a queue size ofapproximately 25 jobs.

Figure 2.9 shows the queue size of a simulation executed applying positive autocorrelation.This figure shows a higher and wider peak at 300 hours. Furthermore, the other peaks are



Table 2.6: Results of autocorrelated arrival process

correlation: none positive negative

Average daily throughput (jobs) 120.09 118.75 120.5Average time in system (hours) 1.7 1.9 1.6Average total time in queues (hours) 0.81 1 0.7Average total wait for transport (hours) 0.07 0.07 0.07Proportion forklifts moving loaded (%) 0.44 0.44 0.44Proportion forklifts moving empty (%) 0.27 0.26 0.27Queue workstation 1 (jobs) 3.72 4.72 2.96Queue workstation 2 (jobs) 0.24 0.28 0.23Queue workstation 3 (jobs) 2.13 2.66 1.8Queue workstation 4 (jobs) 0.46 0.6 0.4Queue workstation 5 (jobs) 5.78 6.73 5.19

Figure 2.8: Queue size workstation 5 with uncorrelated job arrivals

higher and a little bit wider spread. between these peaks, it looks like the queue is a littlebit smaller compared to the uncorrelated figure. As it can be seen, positive autocorrelationresults in higher maximal values in queues and the time a queue is filled is longer due to thehigher arrival rate of jobs in a short period of time.



Figure 2.9: Queue size workstation 5 with positive autocorrelated job arrivals

Negative autocorrelation should results in more evenly spread job arrivals. As can be seenin Figure 2.10, there are no big peaks compared to the other two arrival rates. The max-imum queue size does not exceed 30 jobs, which is low even compared to the system withuncorrelated job arrivals.

Figure 2.10: Queue size workstation 5 with negative autocorrelated job arrivals



2.7 Conclusion

Looking at the results it can be concluded that the forklift truck decision making could havea big impact on the system. However, the system should run on the edge of stability to seethe difference, because the decision making in the Anylogic model is only adjustable for thecase there are already jobs available for transport when a forklift truck becomes available.For this job shop model, the shortest distance to job option results in the best results.

Applying an autocorrelated arrival process mostly effects the average queue sizes, resulting inbigger average queue sizes for positive autocorrelation. Furthermore, the queue sizes decreasewhen applying negative autocorrelation. The results prove that it is important to investigatethe correlation in an arrival rate because the impact on a simple job shop model used hereis significant. In this model, the queue sizes are unlimited, however, in real systems this willnot be the case. Modelling a system without correlated arrivals to analyse what buffer sizesshould be used in a real world system could lead to full buffers or even a blocking systemwhen it turns out that the arrival rate is positively correlated. One can improve the systemperformance by applying negative autocorrelation, however, in most cases the probability ofhaving a positive correlation is higher and in reality one can rarely choose the correlation ofthe arrival process.

To further improve the job shop model, it is an idea to implement also decision makingoptions for the case a forklift truck is idle and no jobs are available for transport. In thismodel, the forklift truck just waits at its last position, however, it could be interesting to seehow the system performance changes when the forklift truck waits for instance in the middleof the system, or predicts where the next job becomes available for transport. Furthermore,if there are more than one forklift truck available at the point a job becomes available fortransport, the nearest forklift truck will seize the job. To increase the flexibility of the model,it could be interesting to also add some other decision making options for this situation.



3 Operator group and release rate influences in a wafer fab

This section discusses how increasing the operator qualification and an alternating releaserate of jobs influences the WIP level and cycle time of a wafer fab. The software usedfor the experiments is Factory explorer [2], using models specified in Excel files as input.Because of the high complexity of a wafer fab, a simplified general wafer fab model namedmimac6 is used. The mimac6 model is a model based on the Siemens HL Fab in Regensburg(Germany) in the mid-nineties. The mimac6 model is often used as a reference model toinvestigate logistic problems regarding semiconductor fabs because firms rarely reveal datafor investigation. The mimac6 model exists of 9 products having different release rates andproduction recipes. Furthermore, 108 tool groups and 7 operator groups are specified inthe model. The production recipes describe typical semiconductor production processes,containing approximately 250 upto 350 production steps dependant on the product type.Each production step contains the following information:

• Step name

• Used tool group

• Used operator group

• Load time

• Whether it is a single-wafer, lot or a batch process

• Processing time

• Unload time

• % of units scrapped

Besides these data, the model also contains set-up processes, waiting times and failures. Theoperators needed for the processing steps are divided into 7 operator groups, each having 1or more operators. As mentioned before, this model is used to investigate the influence ofincreasing the operator qualification in a real wafer fab. However, the amount of differentoperator qualifications is very high and not definable in a simple model as a mimac6 model.Therefore, increasing operator qualification is modelled by combining the operator groupsin the mimac6 model. Besides looking to the operator groups, the influence of alternatingrelease rates of jobs is investigated by alternating the release rate each day, 2 days, 4 days, 1week, and 2 weeks. However, to compare the results to a constant release rate, the averagerelease rate is kept constant.

3.1 Influence of operator qualification

To investigate whether increasing the qualification of operators increases the plant perfor-mance, an increase in operator qualification is modelled by combining operator groups. Theexperiments focus on both the operator group utilization and combining operator groups.



3.1.1 Experiment plan: Operator group utilization

The first step is to search for a point where changing the operator groups significantly effectsthe WIP and cycle time. This point can be found iteratively and it turns out that thebiggest changes are at the point where the system is at the edge of stability. Therefore,the influence of the operator utilization on the WIP and cycle time is investigated. Thisinvestigation results in five experiment cases, where the utilization of the operator groupsdiffers, by changing the amount of operators in an operator group. The first configurationcan be seen in Figure 3.1. This configuration is named the 95% utilization case, becausethe highest utilization is approximately 95%. As it can be seen in this figure, some operatorgroups have a lower utilization. These operator groups would have a operator utilization ofabove 100% when one of the operators would be removed.

Figure 3.1: 95% utilization of operator groups

Besides the 95% case, also 90%, 80%, 70% and 60% cases are configured and the utilizationlevels are shown in Figures 3.2, 3.3, 3.4, 3.5. All the simulations have a run period of 5 yearsof which 2 years are used as startup time. Furthermore, 20 replications are executed for eachdesign point. The release times of jobs as well as the processing times are kept constantduring these experiments, only the number of operators in an operator group is modified.





3.1.2 Results: Operator group utilization

The first discussed case is the one with the high utilization of 95%, which WIP and cycletime can be seen in Figure 3.6.



Figure 3.6: WIP and cycle time over time chart of the 95% operator utilization configuration

As can be seen in this figure, the WIP levels and cycle times are rapidly increasing over thefull 5 years. Therefore, it can be concluded that the system is not stable for this operatorutilization level. The second operator utilization level discussed is the 90% configurationand the results are shown in Figure 3.7. Although the WIP levels and cycle times are lessincreasing as the 95% case, the system is still unstable.





Looking to Figure 3.8, which shows the result of the 80% operator utilization case, it canbe seen that this system behaves stable after period of approximately one year. Because the80% utilization design point is stable, the 70% and 60% design points will also be stable.The WIP and cycle time over the 5 year period figures for these two design points can befound in appendix A.3.

The difference in average WIP and average cycle time over the last 3 years of the threestable configurations can be seen in Figure 3.9. It can be seen that a decrease in operatorutilization results in a decrease in WIP level and cycle time.

Figure 3.9: Average WIP and cycle time for the 80, 70 and 60% operator utilization configurations



Figure 3.10: Total amount of operators and average cycle time for the 80, 70 and 60% operatorutilization configurations

To make the influence of adding operators more clear, Figure 3.10 shows both the changein cycle time and the change of the amount of operators in one graph. Adding 6 operatorsspread over the the most utilized operator groups of the 80% design point leads to a decreaseof approximately 2 days in cycle time.

3.1.3 Experiment plan: Combining operator groups

This experiment is used to investigate how increasing operator qualification influences thewafer fab performance. As discussed before, a simplified model applying operator groups isused, modelling an increase in operator qualification by combining operator groups in themimac6 model. The main point to investigate is: how combining operators effects the cycletime and WIP level. Also for these simulations, the release times of jobs as well as theprocessing times are kept constant during these experiments, only the operator groups aremodified. Furthermore, to investigate if there is a significant difference between which oper-ator groups are combined, different combination strategies are carried out. The combinationstrategies are based either on the size of the operator group, or the utilization.

The following experiment is based on the 80% utilization case, which can be seen in Figure3.3. The experiments described below are also executed for the 70% and 60% utilizationpart, the details are discussed later.

First, let’s discuss the size based combining strategy, starting by combining the two smallestoperator groups, which results in a combined group existing of the PROBE and the MISCoperators. For the second simulation, the third smallest operator group is added to thecombined group. In this case, the FURNACE operator group is added to the combinedgroup. For the next experiments, the combined group grows, by adding the next operatorgroup having the smallest size, resulting in the following experiments: (only the combinedgroup is showed)

• PROBE + MISC

• PROBE + MISC + FURNACE

• PROBE + MISC + FURNACE + IMPL



• PROBE + MISC + FURNACE + IMPL + WETETCH

• PROBE + MISC + FURNACE + IMPL + WETETCH + DRYETCH

• PROBE + MISC + FURNACE + IMPL + WETETCH + DRYETCH + PHOTO

The same approach is used for the utilization based strategy. However, now a scenarioof combining the groups with the highest utilization is executed, as well as combining thegroups with the lowest utilization. This results in the following simulations:

High utilization combining

• PROBE + MISC

• PROBE + MISC + DRYETCH

• PROBE + MISC + DRYETCH + WETETCH

• PROBE + MISC + DRYETCH + WETETCH + FURNACE

• PROBE + MISC + DRYETCH + WETETCH + FURNACE + IMPL

• PROBE + MISC + DRYETCH + WETETCH + FURNACE + IMPL + PROBE

Low utilization combining

• PROBE + IMPL

• PROBE + IMPL + FURNACE

• PROBE + IMPL + FURNACE + WETETCH

• PROBE + IMPL + FURNACE + WETETCH + DRYETCH

• PROBE + IMPL + FURNACE + WETETCH + DRYETCH + MISC

• PROBE + IMPL + FURNACE + WETETCH + DRYETCH + MISC + PHOTO

Note, that some of the simulations are combining the same groups as the size based strategy,so these do not have to be executed again. Until now, the combined group consists of twoor more operator groups. However, in practice, there will be a limitation on the amount ofgroups which are combined to one group. Therefore, it is decided to add one experiment,combining just 2 operator groups to one new group. The group with the biggest utilizationis combined with the operator group having the lowest utilization. Resulting in the followingexperiments (groups separated by comma):

• PHOTO + PROBE

• PHOTO + PROBE, MISC + IMPL

• PHOTO + PROBE, MISC + IMPL, DRYETCH + FURNACE



All the simulations have a run period of 5 years of which 2 years are used as start up time.Furthermore, 20 replications are executed for each design point. The obtained results arecompared with the situation where no operator groups are combined. The experimentsdescribed before are carried out for the 80% utilization system. For the 70% and 60%operator utilization cases, exactly the same simulations are executed, so the described groupcombinations are exactly the same as the combinations used for the experiments of the 80%utilization case. However the strategy of combining the groups with the highest utilizationis the only exception to this rule. For this experiment, the combinations are really based onthe height of the operator group utilization, resulting in different combinations for both the70% and 60% scenarios. The other experiments for the 70% and 60% utilization case arenot configured separately due to the short period of time available to obtain results.

3.1.4 Results: Combining operator groups

This section shows the results of the experiments defined in experiment plan above. First,the results of the 80% utilization case are discussed. Figure 3.11, shows the average WIPlevel and the average cycle time of the experiments carried out for the 80% utilization case.The horizontal axis shows the amount of operator groups combined into one new groups, asdiscussed in the experiment plan.

Before discussing the figures showing the results, a short explanation about these figures isprovided, starting with the horizontal axis. The horizontal axis represents the number oforiginal operators which are combined. The without value on the horizontal axis representsthe initial design point without combined operator groups. Therefore, the results have allthe same value for the without case. The left vertical axis represents the WIP level and theright horizontal axis represents the cycle time in days. The WIP levels are plotted by thefollowing block charts:

• Blue: Combined group based on combining the operator groups having the smallestsize.

• Green: Combined operator groups based on combined operator groups based on com-bining the lowest utilized operator groups.

• Brown: Combined operator groups based on combining the highest utilized operatorgroups.

• Red: Combining the highest and lowest utilized operator groups into new combinedgroups existing of 2 operator groups. (therefore only plotted for the design pointshaving 2, 4 and 6 combined operator groups)

Furthermore, for each of these design points listed above, also the corresponding cycle timesare plotted by the lines.



Figure 3.11: WIP and cycle time over the amount of combined operator groups for the 80% operatorutilization configuration

As can be seen in Figure 3.11, combining the two operator groups with the highest utilizationresults in the lowest WIP level and cycle time, compared to the other combining strategiesand the initial system. However, looking to the results of combining three operator groups,combining the groups with the lowest utilization results in the steepest decrease in WIPlevel and cycle time. Looking to the figure, there there is no obvious best strategy, however,combining more operator groups results in a decrease in WIP level and cycle time.

Figure 3.12, shows the results for the 70% utilization, also in this case, combining the twooperator groups with the highest utilization results in the steepest decrease of the WIP leveland cycle time. Furthermore, this combining strategy is results in the lowest WIP level andcycle time for combining four, five and six operator groups. However, as mentioned in theexperiment plan, the other combine strategies are based on the combinations of the 80%utilization case, due to the limited amount of time.




For the 60% utilization case, the strategy of combining the operator groups with the highestutilization results in the lowest WIP and cycle time for all the number of combined operatorgroups. Probably, these are one of the most effective combinations of the operator group.These combinations could possibly be more effective due to the better combinations of toolgroups usage in the production processes. However, it is not proven that combining thesegroups always leads to a good result.




3.1.5 Conclusion

Looking to the results, it can be concluded that combining operator groups decreases theWIP level and cycle time of a system. However, it is not clear what strategy for thiscombining is the best. In reality, the amount of operator groups which can be combinedto one new group will be limited, due to the fact that, in most cases, it is not possible toteach the operators in a plant all the processes and there is also just a limited amount oftime for education. Moreover, it is likely that some operator groups can be combined moreeasily than others. However, for this study, all these factors are not known, so the conclusionwhich can be drawn is that combining the two operator groups with the highest utilizationresults in the best wafer fab performance. Furthermore, combining more operator groupsleads to a decrease in WIP and cycle time. Increasing operator qualification results in adecrease in WIP level and cycle time. However, dependent on the possibilities of increasingthe qualification, it is recommended to simulate these possibilities in a more complex waferfab model to obtain the best result.



3.2 Effects of an alternating release strategy in a wafer fab

To compare the results of alternating starting rate with the results of the system having aconstant starting rate, the fluctuating starting rate is applied by increasing and decreasingthe constant arrival rate of 25% to keep the total number of jobs started constant. Forexample, simulating a scenario of switching the starting rate every day, the constant startingrate is multiplied with 1.25 for the first day and multiplied with 0.75 the second day. Besideschanging the starting rate every day, four other alternation periods are modelled, resultingin the following design points:

• constant

• 1 day

• 2 days

• 4 days

• 1 week

• 2 weeks

For every design point listed above, a run period of 50 weeks of which 20 weeks startup time,and 10 replications are executed. These simulations are done for a adjusted mimac6 modelusing FIFO and having approximately 90% factory capacity loading. Also an optimizedmimac6 model using critical ratio having a factory capacity loading of approximately 96%is simulated. These simulations have a runtime of 8 years of which 2 years are used as startup time, and 8 replications are executed. Furthermore, a fluctuation of 50% is simulated forthis second model, to see how the system responds to a larger difference between the arrivalrates.

Problem: The results of the first simulations show a big difference in the total numberof lots started. Therefore, the start rate of each configuration is iteratively changed untilthe total number of lots started is approximately the same as the initial system withoutfluctuation. More about this bug can be found in appendix A.4. Because fixing this bugiteratively takes a lot of time, the number of experiments executed for the second mimac6model are reduced as mentioned before.

3.2.1 Results

The results of the experiments executed for the first mimac6 model are shown in Figure3.14. At the horizontal axis, first the system with a constant arrival rate is shown, afterwhich the results of the alternation periods are plotted in an increasing. Furthermore, foreach case, the average WIP level, cycle time, and cycle time variance are plotted. Pleasenote that there is also a line representing the cycle time variance multiplied by 10, to makethe differences in the cycle time variance more visible.



Figure 3.14: WIP, Cycle time and Cycle time variance over alternation changing period

As can be seen in Figure 3.14, alternating the release rate every day or 2 days decreases theWIP level and cycle time, however, increasing the switching period further leads to a smallincrease of the cycle time. Furthermore, the cycle time variance increases significantly at aalternation period of 2 weeks.

Figure 3.15 shows the results of the second system applying a 25% alternation. Similar tothe results discussed before, the alternation period of 1 day results in a lower cycle time andWIP level, however, the WIP and cycle time for the other alternation periods are highercompared to the constant arrival rate. Furthermore, it can be seen that the cycle time andWIP lever increases applying longer alternation periods.



Figure 3.15: WIP, Cycle time and Cycle time variance over alternation changing period of thesecond system applying 25% alternation

Figure 3.16: WIP, Cycle time and Cycle time variance over alternation changing period of thesecond system applying 50% alternation

Figure 3.16 shows the results for the same system applying a 50% alternation. As it canbe seen, a alternation period of 2 weeks results in an unstable system, resulting in a bigincrease in WIP, cycle time and cycle time variance. Furthermore, the system has the lowestWIP level and cycle time when a 1 day switching period is applied. Remarkable is the result



of the 4 day switching period, for which the average WIP level and cycle time are smallercompared to the constant arrival process. However, the differences in WIP level and cycletime are very small.

3.2.2 Conclusion

Based on the results, it can be concluded that applying a fluctuation in combination witha fast switching period of one day increases the performance of the system, assuming thesimulations with factory explorer are reliable after iteratively fixing the starting rate issue.However, note that these results not necessarily hold for other systems, since the resultsprobably depend on the dynamics of the system.Furthermore, applying longer alternation periods results in an increase in WIP level andcycle time. However, in the second system, the cycle time variance does not increase for the25% alternation due to the critical ratio dispatch rule. This rule tries to suppress the cycletime and its variance until it is not possible any more, as can be seen for the 50% alternationfigure. Therefore, there is a big jump in cycle time and its variance for an alternation periodof 2 weeks.



References

[1] “Anylogic.” [Online]. Available: http://www.anylogic.com/

[2] “Factory explorer R©details.” [Online]. Available: http://www.wwk.com/fxdetail.html

[3] A. M. Law and D. M. Kelton, Simulation Modeling and Analysis, 3rd ed. McGraw-HillHigher Education, 1999.

[4] T. Uhlig, O. Rose, and S. Rank, “Jarta - a java library to model and fit autoregressive-to-anything processes,” in 2013 Winter Simulations Conference (WSC), Dec 2013, pp.1203–1211.

[5] T. Uglih, O. Rose, and S. Rank, “Evaluation of modeling tools for autocorrelated inputprocesses,” in 2016 Winter Simulations Conference (WSC), 2016.


http://www.anylogic.com/

http://www.wwk.com/fxdetail.html


A Appendix

A.1 Data storage Anylogic model

The data stored for analyses is divided into three groups: job data, workstation data, andforklift truck data. The data for each of these three groups is stored in three differentLinkedHashMaps. Starting with the job data, for each job leaving the system after the startup time, the following data is stored:

• time the job was in the system

• at what time the job leaved the system

• the total time the job spend in a queue

• the total time the job spend waiting for transport

• the total time the job travelled

• the total process time of the job

This information is all stored in variables attached to the job and updated when necessary.To give an example: every time a job finishes travelling, this travel time is added to thevalue of the variable carrying the travel time information. The data is separately stored inthe HashMap for each type of job, meaning that the recipe of a job is used as a the key ofthe HashMap. This storing of data per recipe makes it possible to separately analyse thedata for each job type. The agent data is used to calculate the daily throughput, averagetime in system, average total time in queues, and average total wait for transport.

The workstation data is also stored in a LinkedHashMap, which is called workstationDataMap.The keys of these HashMaps are of type String, each referring to a station in the system.For example the key for workstation 1 is "workstation1". In the workstationDataMap thefollowing data is stored:

• array list containing queue size and time of change

• current queue size (also before start up time is finished)

• total time a machine is busy

• total time a machine is blocked

After the start up time of the system, the queue size of a workstation is stored as well as thetime point the queue size changes. Both these values are added in an array list every timethe queue size in a workstation changes. The time of change is important for calculating theaverage queue size at the end of a simulation, since the period the queue has a specific sizecounts for the mean. Furthermore, the variable currentQueueSize is defined, which alsostores the current queue size at a workstation. This current queue size information is used incase the forklift decision making is set to longest workstation queue, resulting in the forklifttruck taking the job at the workstation with the biggest queue.

The last LinkedHashMap storing used to store data is the forkliftDataMap. This mapstores:



• the total time a forklift truck is moving loaded

• the total time a forklift is moving empty

These values are used to calculate the proportion moving loaded and proportion movingempty at the end of a simulation.

A.2 Workstation performance job shop model

Besides the general system information discussed in the results in section 2.4. Also themachine and queue data of each workstation is analysed. As can be seen in the tables below,the machine performance for both the stable and unstable system configuration are close tothe performance obtained by the reference model. However, looking to the queue sizes, thereare some remarkable differences between the models. The biggest difference is the average andmaximum queue size for workstation 5 running the unstable configuration. If the problem isin the workstation itself, there should also be a big difference in the other workstation data,since all workstations are defined by the same general workstation model. The reason forthe difference could be in the transport towards this workstation, or a recipe error. However,analysing the overall average transportation time and process time separately for each jobtype, it could be concluded that there is no error in the process or transport times of thejobs.

Table A.1: Results unstable system of the book model [3]


Station 1 2 3 4 5Performance measure

Proportion macines busy 0.72 0.74 0.83 0.73 0.66Proportion macines blocked 0.21 0.26 0.17 0.27 0.33Average number in queue 3.68 524.53 519.63 569.23 32.54Maximum number in queue 32.00 1072.00 1026.00 1152.00 137.00

Table A.2: Results unstable system of the Anylogic model






Table A.3: Results stable system of the book model [3]




Table A.4: Results stable system of the Anylogic model



Proportion macines busy 0.81 0.45 0.80 0.58 0.83bbbb Proportion macines blocked 0.06 0.06 0.04 0.06 0.07Average number in queue 3.69 0.25 2.10 0.47 5.64Maximum number in queue 69.00 13.00 42.00 19.00 61.00

A.3 WIP and cycle time figures operator utilization

The WIP and cycle time over the 5 year period figures for the 70% and 60% operatorutilization configurations are shown below:



Figure A.1: WIP and cycle time over time chart of the 70% operator utilization configuration

Figure A.2: WIP and cycle time over time chart of the 60% operator utilization configuration



A.4 Issue report Factory Explorer

Using Factory Explorer to investigate the difference between a system having a constantrelease rate and having an alternating release rate, a mimac6 model is used. To compare thetwo start rate strategies, the constant release rate is increased and decreased by multiplyingthe constant arrival rate value by 1.25 and 0.75, to achieve a 25% alternation in the releaserate process and remaining a constant total release rate.

After which the factory worksheet will be inspected to compare the 2 models. Althoughthe total release rate is set the same for both the models, the results show a big differencein the amount of lots started. This is the first issue we encountered. Furthermore, lookingto the amount of lot started each week, the amount of lots started in the first week of themodel with fluctuation is significantly higher than the following weeks. Which seems odd tous and is the second issue we would like to report. To still be able to get some results forour analysis, we tried to iteratively change the arrival rates of the model with fluctuation toget approximately the same amount of lots started as the model having a constant releaserate. It turns out that a small increase in the release rate not necessarily results in more lotsstarted. Moreover, sometimes even less lots are started. This is for instance the case for thefollowing models:

The release rate of the second model is set a little bit higher than the first model, however,after simulation with the following settings:

It turns out that the total number of jobs started decreased. This phenomenon is also dis-covered for reducing the lots started the first period, to compensate for the higher amountof lots started the first weeks. Sometimes, a small reduction in the first value does not nec-essarily results in less started jobs.

Here are some explanations on the model files we attached so you can easier reproduce theseissues. All important changes are found in the Products worksheet. Cell G8 gives the arrivalrate for a constant arrival rate, furthermore, cell G5 is used to adjust the starting rate asfraction of itself, trying to find a release rate for which the same amount of lots is started.The value to reduce the release rate of the first period is located in I3, which’s value is alsomultiplied with the value of all the first periods (so setting this value to 0.9 results in a 10%decrease of the release rate during the first period).

Issues overview:

• Total number of lots released with and without fluctuation is significantly differenteven if numbers according to the model file should result in the same

• When using release intervals the first interval has a significantly higher amount ofreleased wafer compared to all other intervals

• Increasing the number of wafers to be released per interval sometime leads to less lotsbeing released in total


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