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CHAPTER 8

NCM SIMULATION

8.1 INTRODUCTION

isolated simulation as a means of trying to model the impact of variability on

manufacturing system behavior and to explore various ways of coping with

change and uncertainty. Simulation has provided means to support longer

term decisions involving resource requirements, equipment needs and

sensitivities to a variety of product demand as well as to shorter term

decisions such as shop order releases, and shop floor control decisions by

Felix and Chan (2004). The objective of this chapter is to develop a

simulation methodology and to construct simulation models for small to

medium companies for helping building of manufacturing model. The

important factors to be selected are availability, risk, cost and performance.

The simulation tool will be useful in utilizing the resource availabilities of the

enterprises, analyzing how the new work order opportunities might change

the system workload to determine the time constraints that will be assigned

for the new project. For simulation modeling ARENA simulation tool was

used and its sketch was prepared by using visual basics. The following sub-

sections contain brief definitions of modeling and simulation. A detailed

explanation of the simulation process has been explained in this chapter.

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Briefly, steps involved in developing a simulation model, can be

explained as,

i. Identify the problem,

ii. Determine the objectives and overall project plan,

iii. Collect and process real system data,

iv. Formulate and develop a model,

v. Validate the model,

vi. Select appropriate experimental design,

vii. Establish experimental conditions for runs and perform

simulation runs,

viii. Documentation cum reporting & implementation

Although this is a logical ordering of steps in a simulation study,

additional steps at various sub-stages may be required before the objectives of

a simulation study are achieved.

8.2.1 Simulation Benefits & Demerits

Simulation has many benefits for the users. First of all, it lets users

choose correctly among the possible alternatives, provides time compression

and expansion according to the type of the simulated event, equips the

managers with the tools

real system, allows the user to explore possibilities of new policies, operating

procedures or methods. With simulation, one can diagnose problems of

complex systems that are almost impossible to deal within the real

environment, identify constraints that act as a bottleneck for operations,

visualize the plan using the animation capabilities of the software used that

results in a more presentable design. Simulation is also beneficial to build

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consensus among the members of the decision makers and to prepare for

changes by conside

support creates training environments for production team, it can also be used

to specify requirements for capabilities of equipment and carry out wise

investments using all those properties. In accordance with this definition and

benefits, simulation has been extensively used as an off-line decision making

tool for helping the management with production planning issues such as

efficient capacity utilization, sequencing and scheduling and allocation of

resources in manufacturing and production. As outlined in the previous

section simulation has many benefits and advantages, however despite these

advantages, there are things one should considered carefully on carrying out

simulation studies. It is a probability that simulation may not be the perfect

tool for all types of system analysis. Some researchers underline four main

disadvantages of simulation. The first disadvantage is that model building

requires special training and it is highly unlikely that models generated by

different modelers about the same system will be the same. The second

disadvantage is about t

most simulation outputs are essentially random variables based on random

inputs, it may be hard to determine whether an observation is a result of

system interrelationships or randomness. The third disadvantage is that

simulation modeling and analysis can be time consuming and expensive

especially when enough resource is not allocated for modeling and analysis,

resulting in a simulation model and/or analysis that is not sufficient to the

task. A final disadvantage is that simulation may be used inappropriately,

especially in some cases when an analytical solution is possible or even

preferable.

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8.2.2 Simulation Application

One of the largest application areas for simulation modeling is that

of manufacturing systems, effectively in the design and analysis of

manufacturing systems. The specific issues that simulation is used to address

in manufacturing is identified as follows

(a) The need for the quantity of equipment and personnel are,

i. Number, type, and layout of machines for a particular

objective

ii. Requirements for transporters, conveyors, and other

support equipment (e.g., pallets and fixtures)

iii. Location and size of inventory buffers

iv. Evaluation of a change in product volume or mix

v. Evaluation of the effect of a new piece of equipment on

an existing manufacturing system

vi. Evaluation of capital investments

vii. Labor-requirements planning

viii. Number of shifts

(b) Performance evaluation

i. Throughput analysis

ii. Time-in-system analysis

iii. Bottleneck analysis

(c) Evaluation of operational procedures

i. Production scheduling

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ii. Inventory policies

iii. Control strategies [e.g., for an automated guided vehicle

system (AGVS)]

iv. Reliability analysis (e.g., effect of preventive

maintenance)

v. Quality-control policies

As seen from the above discussion, manufacturing and production

offers a huge number of issues to deal with.

8.2.3 Simulation Tools

There are several methods to create simulation models on

computer. General programming languages such as FORTRAN, Basic, or

C/C++ can be used with some routines to be found from the literatures. One

of the several commercially available simulation tools can be utilized.

These tools can be divided into three basic classes as follows:

i. General-purpose simulation languages,

ii. Simulation front-ends and

iii. Simulation packages.

The general-purpose simulation languages require the user to be a

proficient programmer as well as a competent simulator. The simulation

front-ends are essentially interface programs between the user and the

simulation language being used. The most advanced of all, the simulation

packages of today utilize constructs and terminology common to the

manufacturing community, and offer graphical presentation and animation.

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Information about some major simulation software can be found

from the following web addresses is given in Table 8.1, however it should be

noted that there are also other software or simulation languages on the market.

Table 8.1 Simulation software on the market

Name of The Simulation Tool Web Address for Further Information Automod http://www.autosim.com Promodel http://www.promodel.com

Arena http://www.arenasimulation.com AweSim http://www.pritsker.com/ Witness http://www.lanner.com/ Flexsim http://www.flexsim.com/ Extend http://www.imaginethatinc.com/

GoldSim http://www.goldsim.com/ Mast http://www.cmsres.com/

SimCad http://www.createasoft.com/

8.3 VISUAL BASIC SIMULATOR

Visual Basic Simulator (VBS) for applications is an

implementation of microsoft visual basic which is built into all microsoft

office applications, some other microsoft applications such as visio and is at

least partially implemented in some other applications such as AutoCAD and

MSword. It supersedes and expands on the capabilities of earlier application-

basic, and can be

used to control almost all aspects of the host application. Visual Basic for

Applications provides a complete integrated development environment that

features the same elements familiar to developers using Microsoft Visual

Basic, including a project window, a properties window, and debugging tools.

VBS also includes support for Microsoft forms, for creating custom dialog

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boxes, and ActiveX controls, for rapidly building user interfaces. Integrated

directly into a host application, VBS offers the advantages of fast, in-process

performance, tight integration with the host application (code behind

documents, cells, and so forth), and the ability to build solutions without the

use of additional tools. As its name suggests, VBS is closely related to Visual

Basic, but it can normally only run code from within a host application rather

than as a standalone application. It can however be used to control one

application from another.

A visual basic computer simulator with graphical user interface is

developed to investigate experimentally the scheduling algorithm. The

simulator is modular in design, that is heuristic algorithm can be easily ported

to the system. The computing component of the simulator implements a

specific heuristic method of scheduling and products an optimal sequence of

jobs. The graphical user interface together with the visual model execution

engine allows a step wise execution of the visual basic model. The program

works with a VB coding file as an input file the SPT heuristic logical code is

shown in Appendix II. In the following section an example of a data file for

12 machines and ten processes that were discussed and given below.

Each line represents a job and its execution times on different

machines. Upon the startup of the tool the main form will display the given

data and allow the user to run the method and calculate the schedule. The

simulator program will apply the algorithm against the given data. Then it

will display the output. The simulator will allow a deterministic execution of

the visual basic model. A step by step visualized execution can be performed

alternatively an execution can be requested for any given number of units of

time. At the end of execution the tool will display the makespan time, and the

machines utilization ratios for the calculated schedule. The software

development of

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basic 6.0, Enterprise Edition as a full application development and Ms-access

7.0 as database engine. The microsoft windows has emerged as the popular

graphical user interface environment. Windows provide considerable

advantages the features of window are, Since all windows programs have

some fundamental look and feel, users no longer expect to spend long period

of time in mastering a new program. According to the number of jobs waiting

for processing, time taken to process a job varies. A single process cannot be

processed, without any interruption. In order to apply VBS for NCM

scheduling problem, five forms shown as screenshots from Figure 8.2 to

Figure 8.6 are developed in VBS. Out of five forms, two forms are used as

data input forms, one is used as calculation form and remaining two forms are

used as output data forms. The Figure 8.2 shows the data input form, by

clicking the calculation form from package this form will get opened and ask

for number of jobs, number of machines and number of times. After entering

all above details the process button can be clicked.

Figure 8.2 Data input form 1

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Figure 8.3 Data input form 2

Next input form 2 as in Figure 8.3 will opens and ask for the jobs

processing times as input. After entering all its processing times and clicking

calculation button the SPT algorithm logical calculation will be carried out

and calculation form will appear with makespan and idle time results table as

in Figure 8.4. Then by clicking the next button output result form 1 as in

Figure 8.5 will open.

Figure 8.4 SPT algorithm logical calculation form

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Figure 8.5 Output result form 1

Figure 8.5 can display the results about the scheduling outputs like

customer demand per day, machine sequence, makespan value, idle time of

machine, idle time of product etc. then by clicking the next button output

result form 2 as in Figure 8.6 will open and display the results of Nagare cell

output like product cycle time, output/cell/shift, TAKT time, number of

operators/cell, total parts produced etc.

Figure 8.6 Output result form 2

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8.3.1 Experimentation

A sample of one job with 8 machines problem can be taken and the

VBS tool is applied. The processing time for the above problem is given in

Table 8.2. As the first step the input of number of jobs as 1, number of

machines as 8 and the number of times as 100 are entered in form 1 as like in

Figure 8.7.

Table 8.2 Product A with its processing times

Machine m1 m2 m3 m4 m5 m6 m7 m8

Job A 1 2 4 3 1 5 2 2

The following screen shots demonstrate the work of VB model.

One can find a graphical user interface of the VB tool for the heuristic

method. From the Figures 8.7 to 8.11 an example of 1 job and 8 machines

Figure 8.7 Input for 1X8 problem

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Figure 8.8 Processing time for 1X8 problem

Then the processing times from Table 8.2 are entered in the second

input form as like in Figure 8.8, by clicking the calculation button the

calculation table can be obtained with the details of M1[Ti], M1[To] i.e.

machine 1 in time and out time values for all 8 machines as shown in Figure

8.9. then by clicking the next button the first output form will displays the

scheduling outputs as like Figure 8.10, i.e. the number of jobs is 1, customer

demand per day is 100, machine sequence is m1-m5-m2-m7-m8-m4-m3-m6,

the makespan period is 515 minutes, idle time of machine is zero and idle

time of product is 4 minutes.

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Figure 8.9 Makespan and idle time determination for 1X8 problem

Figure 8.10 Scheduling output for 1X8 problem

Then by clicking the next button, the second output form as shown

in Figure 8.11 displays the NCM output, i.e. for making 100 numbers of job A

the required product cycle time is 309 minutes, output per cell is 87.37

minutes, TAKT time is 13.5 minutes. Similarly the above steps can be applied

for 2 jobs (A and B) 8 machines problem. It s processing times are given in

Table 8.3. The VBS forms are shown in Figure 8.12 to 8.16.

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Figure 8.11 NCM output for 1X8 problem

Table 8.3 Product A & B with its processing times.

Machine m1 m2 m3 m4 m5 m6 m7 m8

Job A 1 2 4 3 1 5 2 2

Job B 1 2 0 3 1 0 2 2

Figure 8.12 Input for 2X8 problem

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Figure 8.13 Processing time for 2X8 problem

Figure 8.14 Makespan and idle time determination for 2X8 problem

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Figure 8.15 Scheduling output for 2X8 problem

Figure 8.16 NCM output for 2X8 problem

The final output for 2 jobs and 8 machines problem is shown in

Figure 8.16. It displays the output for making 100 number of job A and B, the

required product cycle time will be 190.2 minutes, output per cell will be 142

minutes, TAKT time will be 27 minutes.

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The above procedure can be applied for 10 jobs with 12 machines

problem with a total of 100 products in each job with its processing time as

given in Table 8.4.

Table 8.4 Product A to J with its processing times

Machine m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11 m12 Job A 3 0 3 2 0 11 11 3 0 7 0 5 Job B 5 0 3 2 4 0 8 0 11 0 2 3 Job C 8 4 4 0 3 14 0 5 6 9 3 4 Job D 0 3 0 6 0 0 8 0 0 0 4 0 Job E 0 3 4 0 0 6 5 7 7 0 0 7 Job F 4 3 0 5 0 0 0 5 0 11 3 0 Job G 6 0 6 0 6 0 11 0 2 6 0 8 Job H 0 5 0 6 0 6 0 0 0 0 1 5 Job I 0 6 8 0 0 0 0 3 4 0 2 0 Job J 7 4 0 8 0 4 5 0 0 2 0 6

Figure 8.17 Processing time for 10X12 problem

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Figure 8.18 Makespan and idle time determination for 10X12 problem

Figure 8.19 Total ready time, idle time of machine and product

determination screen shot.

Figure 8.20 screen shot of processing the 100th product of job J

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Figure 8.21 Scheduling output for 10X12 problem

Figure 8.22 NCM output for 10X12 problem

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8.3.2 An Industrial Case Study

The complexity of the scheduling problem has been reduced by decomposing all parts into nk part family and the corresponding machines into mk machines for the kth NCM centre. Thus the first step is forming a number of NCM centres. A program is written to read the input data (k,n and m) for each NCM centres. To demonstrate how the proposed method works, some practical problems from an industrial partner had been used. The unit consists of 50 different components, out of which 31 components are being manufactured using 12 different workstations centres. Each part considered as a job order, which includes many operations where each operation takes a certain processing time. The processing time for each operation in the corresponding workstation is given in Table 8.5. Using the processing times a case study was conducted at NCM centre, to implement the proposed method; the close to optimum machine sequence generated by VBS is shown in Table 8.6

Table 8.5 Processing times of 31 different jobs by 12 machines

Machine m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11 m12 Job A 3 0 3 2 0 11 11 3 0 7 0 5 Job B 5 0 3 2 4 0 8 0 11 0 2 3 Job C 8 4 4 0 3 14 0 5 6 9 3 4 Job D 0 3 0 6 0 0 8 0 0 0 4 0 Job E 0 3 4 0 0 6 5 7 7 0 0 7 Job F 4 3 0 5 0 0 0 5 0 11 3 0 Job G 6 0 6 0 6 0 11 0 2 6 0 8 Job H 0 5 0 6 0 6 0 0 0 0 1 5 Job I 0 6 8 0 0 0 0 3 4 0 2 0 Job J 7 4 0 8 0 4 5 0 0 2 0 6 Job K 0 0 7 0 7 6 0 0 0 2 0 0 Job L 0 3 0 7 0 0 7 0 14 0 3 9 Job M 3 0 8 1 0 8 0 5 0 9 0 0 Job N 4 0 8 0 6 0 0 7 0 0 3 12 Job O 5 2 4 0 1 9 9 0 11 0 5 0 Job P 6 2 1 12 0 0 0 7 0 13 3 0 Job Q 0 2 3 0 0 11 0 0 8 0 0 9 Job R 2 2 0 0 9 0 9 0 0 0 2 0 Job S 7 0 6 6 0 8 0 7 9 0 5 11

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Table 8.5 (Continued)

Job T 8 2 1 0 4 4 0 3 8 12 1 0 Job U 0 7 0 12 12 11 7 0 0 0 0 7 Job V 14 0 12 5 0 5 0 7 13 0 8 0 Job W 3 4 0 7 0 7 0 5 0 11 0 6 Job X 4 0 0 0 11 0 0 0 6 0 4 0 Job Y 3 3 9 5 0 7 7 11 0 0 0 4 Job Z 0 0 5 7 0 7 0 8 0 8 6 0

Job AA 0 3 0 0 8 0 0 7 8 0 7 0 Job AB 3 6 0 9 0 9 0 0 6 6 0 9 Job AC 2 3 5 0 7 0 6 9 0 0 5 0 Job AD 3 0 4 3 0 4 2 0 4 0 0 7 Job AE 7 5 0 3 8 6 11 5 0 6 0 3

8.3.3 Results and Graph

To demonstrate how effective the proposed VBS as a stochastic, the

above case study practical problem from an industrial partner was used. The

processing time for each operation in the corresponding workstation are

taken. Using that processing time, for the given case study of NCM centers,

the close to optimum machine sequence were generated through the

implementation of the VB simulator. The simulator output results are

calculated and summarized in Table 8.6. By keeping the customer demand per

day as 100, a variety of jobs of range 1 job to 31 jobs were processed through

the simulator in a same 12 machines workstation the processing results of

machine sequence for arranging the machines in a U shaped product layout

has been calculated, then the minimal makespan is observed. Similarly the

performance parameters of idle time of machine, idle time of product, product

cycle time, output per cell, TAKT time are all calculated and given in Table

8.6. From the table it is observed that by grouping the parts into a part family

the makespan can be reduced, product cycle time can be reduced and idle time

of machine shows zero time.

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Table 8.6 Simulator output results for 31 different jobs processed in 12 machines

Job CDPD Machine Schedule Makespan Idle

Time of machine

Idle Time of

Product

Product Cycle Time

Output /cell

TAKT Time

No. of operator

s/ cell

Total parts

produced 1 100 2-5-9-11-4-1`-3-8-12-10-6-7 1134 0 11 680.4 39.68 13.5 1 100 2 100 2-11-8-4-5-3-10-1-12-6-9-7 984 0 38 590.4 45.73 27 1 99.99 3 100 2-4-11-5-8-3-12-1-10-9-7-6 859 0 -630 515.40 52.38 40.5 1 100 4 100 2-5-8-11-3-4-12-1-10-9-6-7 709 0 80 425.4 63.46 54 1 99.99 5 100 5-11-2-4-3-8-1-10-12-9-6-7 674 0 125 404.4 66.76 67.5 1 100 6 100 5-11-2-3-4-12-1-8-9-10-6-7 573 0 150 343.79 78.53 80.99 1 99.99 7 100 11-2-5-4-3-8-1-9-12-6-10-7 655 0 217 393 68.70 94.49 1 99.99 8 100 5-11-2-3-8-4-1-9-12-10-6-7 577 0 240 346.2 77.98 108 1 99.99 9 100 5-11-4-8-2-1-3-9-12-10-6-7 507 0 -60 304.2 88.75 121.5 1 100

10 100 5-11-8-2-3-4-9-1-10-12-6-7 514 0 350 308.4 87.54 135 1 100 11 100 11-5-8-2-4-9-1-3-10-12-6-7 466 0 66 279.6 96.56 148.5 1 100 12 100 11-5-8-2-1-3-4-10-9-6-12-7 493 0 111 295.8 91.27 162 1 100 13 100 11-5-8-2-1-4-3-9-10-12-6-7 462 0 174 277.2 97.40 175.5 1 99.99 14 100 11-5-2-8-4-1-9-10-3-6-7-12 458 0 256 274.8 98.25 189 1 99.99 15 100 11-5-2-8-4-1-10-3-9-12-6-7 466 0 570 279.6 96.56 202.5 1 99.99 16 100 5-11-2-8-4-1-9-3-10-12-6-7 445 0 582 267 101.1 216 1 100 17 100 5-11-2-8-4-1-3-10-9-7-12-6 464 0 544 278.4 96.98 229.5 1 100

Table 8.6 (Continued)

Job CDPD Machine Schedule Makespan Idle

Time of machine

Idle Time of

Product

Product Cycle Time

Output /cell

18 100 11-5-2-8-4-1-3-10-9-12-7-6 450 0 792 270 100 19 100 5-11-2-8-4-10-1-3-9-7-12-6 474 0 635 284.4 94.93 20 100 11-5-2-8-4-3-1-10-7-12-9-6 469 0 984 281.4 95.94 21 100 11-2-5-8-3-4-1-10-7-9-12-6 490 0 924 294 91.83 22 100 11-2-5-8-10-4-3-7-1-12-9-6 493 0 1226 295.8 91.27 23 100 11-2-5-8-3-4-7-10-1-12-9-6 505 0 1163 303 89.10 24 100 11-2-5-8-3-4-7-10-1-12-9-6 499 0 1437 299.4 90.18 25 100 11-2-5-8-10-4-3-7-1-12-9-6 502 0 1660 301.2 89.64 26 100 2-11-5-8-7-10-4-1-3-12-9-6 509 0 1761 305.4 88.40 27 100 2-11-5-7-8-10-4-1-3-12-9-6 481 0 1428 288.6 93.55 28 100 11-2-5-7-8-3-1-10-4-12-9-6 497 0 1607 298.2 90.54 29 100 2-11-5-7-10-1-3-8-4-12-9-6 488 0 1854 292.8 92.21 30 100 2-11-5-7-10-8-1-3-4-12-9-6 482 0 1739 289.2 93.36 31 100 11-2-5-3-10-8-4-7-1-9-6-12 533 0 2451 319.8 84.42

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In addition a comparative chart is prepared for makespan and product cycle

time which is shown in the Figure 8.23. It says while product group is

increased the makespan value and product cycle time value gets reduced. And

the Figure 8.24 shows the comparative charts of all jobs between the idle time

and the TAKT time. It was observed that the idle time of machine is zero for

all 31 jobs and the idle time of product & TAKT time gets increased by

increasing the product group.

Figure 8.23 Average makespan and cycle time

Figure 8.24 Average idle time and TAKT time

-1000

-500

0

500

1000

1500

2000

2500

3000

0 20 40

Tim

e in

min

utes

Number of jobs

Makespan

Product CycleTime

-1000

-500

0

500

1000

1500

2000

2500

3000

0 20 40

Tim

e in

min

utes

Number of jobs

Idle Time ofmachine

Idle Time ofProduct

TAKT Time

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8.4 ARENA SIMULATION

The ARENA modeling system from Systems Modeling Corporation

is a flexible and powerful tool that allows analysts to create animated

simulation models that accurately represent virtually any system. ARENA

employs an object-oriented design for entirely graphical model development.

Simulation analysts place graphical objects, called modules, on a layout in

order to define system components such as machines, operators, and material

handling devices. ARENA is built on the SIMAN simulation language. After

creating a simulation model graphically, ARENA automatically generates the

underlying SIMAN model used to perform simulation runs. ARENA has

many unique properties which are, ARENA has a natural and consistent

modeling methodology due to its flowchart style model building regardless of

detail or complexity. Even the flowcharts of systems created by Microsoft

Visio can be imported and used directly. It is extendable and customizable,

which results in a re-creatable, reusable and distributable templates tailored to

specific applications. The scalable architecture of ARENA provides a

modeling medium that is easy enough to suit the needs of the beginner, and

powerful enough to satisfy the demands of the most advanced users. This

makes it a perfect tool for continuously improving modeling studies as the

other advantage of ARENA is that it is open to interaction with many

applications such as Microsoft Access and Excel with its built-in spreadsheet

data interface. Arena Packaging is a simulation system for the performance

analysis of high-speed, high-volume manufacturing systems.

8.4.1 Template Overview

Arena packaging is one of a family of application solution

templates (ASTs) built on the Arena simulation system. It is designed

specifically for performing accurate and efficient simulations of high-speed,

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high-volume manufacturing systems, where the processing rates take place at

hundreds, even thousands, of entities per minute. The Packaging template

enables users to build and run simulation models of high-speed processing

lines quickly and easily, and to analyze the results that these models produce.

8.4.2 ARENA Tools and Features

ARENA has three main tools they are,

Input Analyzer - can be used to process and classify the obtained data for

input data analysis. Appropriate probability distributions can be obtained for

being used in the models.

Output Analyzer - made the user carry out statistical analysis on the results

obtained.

Process Analyzer - helps to examine the selected outcomes of several

different alternatives dependent on selected controls on the system.

The most attractive feature of a simulation study is the animation

that accompanies the model. Most people are interested in watching animated

actions and graphs rather than straight numbers and texts. ARENA has a

powerful animation tool to help the user to pass his/her ideas, studies and

results to the audience easily. ARENA animations can be run concurrently

with the executing simulation model.

For any manufacturing environment, the processing and analyzing

can be done from simulation by following five easy steps with Arena:

Step 1 Create a basic model.

Step 2 Refine the model.

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Step 3 Simulate the model.

Step 4 Analyze simulation results

Step 5 Select the best alternative.

8.4.3 Simulation Concepts

(i) Entities and Attributes

In every simulation model, entities represent the objects moving

through the system. Each entity has its own characteristics, refer to as

attributes. It can define as many attributes as need for the entities in this

system. Each individual entity in the system has its own values of these

attributes; these may be assigned at the various processes it encounters.

(ii) Queues

The primary purpose of a queue is to provide a waiting space for

entities whose movement through the model has been suspended due to the

system status (e.g., a busy resource). Queues are passive in nature; entities

enter the queue and are removed from it based upon the change in state of the

system element associated with the queue. There are two types of queues used

in Arena.

Individual queues

Internal queues

(ii) Resources

Resources are stationary elements of a system that can be allocated

to entities. They have a specified capacity (at any point in time) and a set of

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states (e.g., busy, idle, inactive, or failed) that they transition between during

a simulation run. Resources may be used to represent people, machines, or

even space in a storage area.

Resource terminologies

Seizes,

Releases,

Unit,

Schedule,

Downtimes,

Failures

Resources are depicted in the animation by a stationary set of

pictures representing the states of the resource (idle, busy, etc.) The default

pictures can be customized to better represent the resources in this system

from more information on animating resources.

(iv) Modeling environment

The Arena modeling environment will open with a new model

window as shown in Figure 8.25. To model the process it would be work with

three main regions of the application window. The Project Bar hosts panels

with the primary types of objects that will work

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Figure 8.25 Arena model window with Basic processes

(v) Basic Process panel

Contain the modeling shapes, called modules, that uses to define

the process.

(vi) Reports panel

Contains the reports that are available for displaying results of

simulation runs.

(vii) Navigate panel

Allows to display different views of the model, including

navigating through hierarchical sub models and displaying a model

thumbnail. In the model window, there are two main regions. The flowchart

view will contain all the model graphics, including the process flowchart,

animation, and other drawing elements. The lower, spreadsheet view displays

model data, such as times, costs, and other parameters.

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(viii) Exhibit Task

Create module, w Create module,

from the Basic Process panel. This is the starting point for the flow of entities

through the model.

1. Drag the Create module from the Basic Process panel into the

a more meaningful description as well as

some data to support the simulation.

Figure 8.26 Process flow chart model

(ix) Process flowchart

Build a flowchart the word itself flowchart suggests two of the

main concepts behind modeling and simulation i.e. a chart refer to as a

process map or a model that describes a flow. Flow refers to as entities that

will move through the process steps in the model which is shown in Figure

8.26.

(x) Process module

Next in our flowchart is a Process module that represents the

Review Application step.

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1. Be sure that the Create module is selected so that Arena will

automatically connect the Process to the Create module.

2. Drag a Process module from the Basic Process panel into the

model window, placing it to the right of the Create. Arena will

automatically connect the two modules. As with the Create,

(xi) Decide module

After the Process, we have a Decide module which determines

whether the mortgage application is complete.

1. -

the Object > Auto- Connect menu), be sure that the Process

module is selected so that the Decide module will be

connected to it.

2. Drag a Decide module to the right of the Process module. If

the mortgage application has a complete set of information, it

will leave the Decide module from the right side of the

diamond shape, representing the True condition. Incomplete

applications (False result to the Decide test) will leave via the

bottom connection.

(xii) Dispose module

Dispose module that represents accepted

applications, connecting to the True (right) output from the Decide shape.

applications.

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1. Select the Decide shape so that our first Dispose will be

connected automatically.

2. Drag a Dispose module to the right of the Decide module.

Arena will connect it to the primary (True) exit point of the

-and-drop sequence.)

3. To add the second Dispose module, once again select the

Decide module, so that Arena will automatically connect its

False exit point to the new Dispose module, and drag another

Dispose module below and to the right of the Decide module.

4. Drag and drop another Dispose module, placing it below and

to the right of the Decide shape, completing the process

flowchart.

8.4.4 Module Creation

In Arena, modules are the flowchart and data objects that define the

process to be simulated. All information required to simulate a process is

stored in modules. Those are placed in the model window to describe the

process. In the basic process panel, these are the first eight shapes used to

construct the flow chart:

Create: The start of process flow. Entities enter the simulation here.

Dispose: The end of process flow. Entities are removed from the simulation

here.

Process: An activity, usually performed by one or more resources and

requiring some time to complete.

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Decide: A branch in process flow. Only one branch is taken.

Batch: Collect a number of entities before they can continue processing.

Separate: Duplicate entities for concurrent or parallel processing, or

separating a previously established batch of entities.

Assign: Change the value of some parameter (during the simulation), such as

Record: Collect a statistic, such as an entity count or cycle time.

Simulation settings are defined in the Run > Setup > Replication

Parameters dialog box. There is also a set of data modules for defining the

characteristics of various process elements, such as resources and queues.

8.4.5 Definition of Model Data

A basic flowchart can be drawn for one mortgage application

process, to define the data associated with the modules, including the name of

the module and information that will be used to simulate the process.

(i) Create module

First the Create module named as Initiate Mortgage Application. Its

data will include the type of entity to be created for a mortgage Application.

1. Double-click the Create module to open its property dialog

box.

2. In the Name field, type Initiate Mortgage Application.

3. For the Entity Type, name our entities by typing Application.

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4. Type 2 in the Value field of the time between arrivals section.

5. Click OK to close the dialog box.

Entities are the items, documents, parts, produced, or otherwise

acted on by the designer process. Manufacturing models typically have some

kind of part running through the process, whether it can be a raw material, a

subcomponent, or finished product.

(ii) Process module

the system being

modeled. The application will be reviewed for completeness this will take

some amount of time, holding the entity at this point in the flowchart for a

delay and requiring a resource

module also as Review Application. The designer should specify the minimum

time in which the work could be done, the most likely value for the time

delay, and the maximum duration of the process. During the simulation run,

each time an entity enters the process.

For some Review Application process, a minimum time of 1 hour,

most likely value of 1.75 hours, and a maximum of 3 hours can be assigned to

a resource, to perform the process.

1. Double-click the Process module to open its property dialog

box.

2. In the Name field, type Review Application.

3. To define a resource to perform this process, pull down the

Action list and select Seize Delay Release.

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Arriving entities will wait their turn for the resource to be

available. When its turn comes, the entity will seize the

resource, delay for the process time, and then release the

resource to do other work.

4. A list of resources will appear in the center of the dialog box.

To add a resource for this process, click Add.

5. In the Resource Name field of the Resource dialog box, type

Mortgage Review Clerk.

6. Click OK to close the Resource dialog box.

7. Define the process delay parameters in the Minimum, Value

(Most Likely), and Maximum fields as 1, 1.75, and 3. (Note

that the default delay type is Triangular and the default time

units are in hours.)

8. Click OK

default values for the other Process module properties. Feel

free to explore their purposes through online help or the

Modeling Concepts and Resources models in the SMARTs

library.

(iii) Decide module

After the mortgage application has been reviewed, it should be

determined whether to accept or return the application. In Arena, whenever an

entity selects among branches in the process logic, taking just one of the

alternatives, a Decide module is used. For the mortgage application process to

determine the outcome of the decision, with 88% of applications accepted as

complete.

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1. Double-click the Decide module to open its property dialog

box.

2. In the Name field, type Complete?

3. For the Percent True field, type 88 to define the percent of

depart through the exit point at the right of the Decide

module).

4. Click OK to close the dialog box.

(iv) Dispose module

For a simple process of reviewing mortgage applications, remove

the mortgage applications from the model, terminating the process by a

Dispose module. Because there are two possible outcomes of the mortgage

application process-applications can be accepted or returned as shown in

Figure 8.27

1. Double-click the first Dispose module (connected to the True

condition branch of the Decide module) to open its property

dialog box, and in the Name field, type Accepted.

2. Click OK to close the dialog box.

3. Double-click the other Dispose module to open its property

dialog box. In the Name field, type Returned.

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Figure 8.27 Decide module window

(v) Resource module

Along with the flowchart, define the parameters associated with

other elements of the model, such as resources, entities, queues, etc. For the

mortgage process, simulation results will report the cost associated with

performing the process.

To provide the parameters to the model, enter them in the

Resources spreadsheet as in Figure 8.28.

1. In the Basic Process panel, click the Resource icon to display

the Resources spreadsheet.

2. Because we defined the Mortgage Review Clerk as the

resource in the Review Application process, Arena has

automatically added a resource with this name in the

Resources spreadsheet. Click in the Busy/Hour cell and define

the cost rate when the clerk is busy by typing 12. Click in the

Idle/Hour cell and assign the idle cost rate by typing 12.

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Figure 8.28 Resource module spread sheet.

(vi) Prepare for the simulation

To make the model ready for simulation, one should specify the

general project information and the duration of the simulation run. Just by

testing the first-cut model, to perform a short, 20-day run.

1. Open the Project Parameters dialog box by using the Run >

Setup menu item and clicking the Project Parameters tab.

In the Project Title field, type Mortgage Review Analysis;

then leave the Statistics Collection check boxes as the

defaults, with Entities, Queues, Resources, and Processes also

check the costing box.

2. Next, click the Replication Parameters tab within the same

Run Setup dialog box. In the Replication Length field, type

20; and in the Time Units field directly to the right of

Replication Length, select days from the drop-down list.

Click OK to close the dialog box. Save the simulation model

by click the Save on the standard toolbar or select the File >

Save menu item.

of the model definition, including the

flowchart, other graphics drawn, and the module data entered. By perform a

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simulation run; the results are stored in a database using the same name as the

model file.

(vii) Simulate the process

With these few short steps, the mortgage application model

contains all of the information needed to run the simulation.

Start the simulation run by clicking the Go button or clicking the

Run > Go menu item. Arena first will check to determine whether a valid

model is defined, then will launch the simulation. As the simulation

progresses, one can see small entity pictures resembling pages moving among

the flowchart shapes as like in Figure 8.29. Also, a variety of variables change

values as entities are created and processed, as illustrated in Figure 8.29. If the

animation is moving too fast, it can be slow down by adjusting the animation

scale factor.

Open the Run Setup dialog box via the Run > Speed > Animation

Speed Factor menu item and enter a smaller value (e.g., 0.005) for the scale

factor; or

Use the less-than (<) key during the run to decrease the scale factor

by 20%. Be sure that the model window is active not the Navigate panel

or > and < < repeatedly is an easy way to fine tune

the animation speed. The greater-than (>) key speeds up animation by 20%.

Use the slider bar in the main toolbar. Move the slider to the left to

slow down the animation; move the slider to the right to speed up the

animation.

To pause the simulation, click the Pause button or press the Esc

key. With the automatic flowchart animation, one can see how many entities

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have been created and currently in the Review Application process, have left

each branch of our Decide module, and have left the model at each of

terminating Dispose modules. These variables can be helpful in verifying the

model.

Figure 8.29 Simulation process

One can step through the simulation one event at a time i.e. pause

the simulation. Each time by stepping the simulation, an entity is moved

movement,

(viii) Simulation reports

After watching some of the animated flowchart, one can quickly

run to the end of the simulation to view reports as in Figure 8.30. Pause the

simulation and then click the Fast Forward button to run the simulation

without updating the animation. At the end of the run, Arena will display the

default report in a report window, as shown below.

On the left side of each report window a tree listing the types of

information is available in the report. The project name is listed at the top of

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the tree, followed by an entry for each category of data. This report

summarizes the results across all replications.

Figure 8.30 Simulation model report

By clicking on the entries inside the category sections, one can

view various types of results from the simulation run. Each report will be

displayed in its own window. After viewed the reports end the Arena run

session.

(ix) Enhancing the visualization process

After completing the basic steps for analyzing the simulation

application process, one can return to the model and embellish the graphical

animation to gain further insight into the process dynamics. Animation will be

of great benefit in enticing others in the organization to be interested in

process improvement. It has to enhance the visualization components to the

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model. So first a Review Clerk working at a desk, either busy or idle to gain a

better sense of how many applications are waiting in the Review Application

process over time. Secondly a dynamic plot of the work-in-process (WIP)

simulation variable to be added. Now the Arena model will appear as in

Figure 8.31 after adding two those two components.

Figure 8.31 Review clerk and WIP plot

(x) Review clerk animation

During the simulation run, the Review Clerk resource can be in one

of two states. If no application entity is in-process, then the resource is idle. A

picture of a person sitting at a desk to depict idleness can be used. When an

entity seizes the resource, busy, for this

case the picture will show the person reviewing a document.

(xi) WIP plot animation

The second animation enhancement is a plot of how many

applications are under review as the simulation progresses. It will give us a

sense of the dynamics of the workload, which can vary quite a bit when the

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random nature of processes is incorporated into a simulated model as in

Figure 8.31.

(xii) Rerun the simulation

To make the animation more interesting and valuable, the

simulation can be rerun again. Because without changing any of the process

parameters the simulation has to provide the same results. By starting the

picture change from idle (sitting at the desk) to busy (reading a document)

and back again, as application entities move through the Review Application

process. The plot as in Figure 8.31 shows some significant peaks in the

number of applications that are under review, caused by the combination of

the variation in the time between arrivals of applications and the time to

process applications.

8.5 NCM SIMULATION

Simulation tools are very familiar and widely used for processing

the manufacturing systems due to some most important reasons and

advantages like i) Realistic models are possible; ii) Options and alternative

designs may be considered without direct system experimentation. iii) A

computer simulation models directly addresses the performance measures. iv)

Non-existent systems may be modeled. v) Visual output helps and assists the

end-user in model development and validation; vi) Manufacturing cell sizing,

queue sizes, and others design parameters can be done.

Simulation models can provide increased comprehension and

improved insight into the performance of a manufacturing system. The

construction of simulation model forces the modeler to ask the above

questions before modeling. Analysis of the numerical results of the simulation

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runs can be used to identify true performance indicators for the system such

as total time in the system for a part, work-in-process inventory, and machine

utilization for making etc. Most of simulation studies have indicated the

importance of workload balancing and machine utilization in determining the

advantage of production. The proposed simulation methodology which is

described in this subsection is working by the logic shown in Figure 8.32. The

logic is by providing the number of jobs as input, getting processed in NCM

cell and released as output by finished product.

Figure 8.32 Simulation logic diagram.

Figure 8.33 Modeling of NCM system model.

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8.5.1 Model construction & performance measures

The Arena simulation language is used to develop the simulation

model for NCM cell. Figure 8.33 shows a modeling of NCM system by Arena

software. The construction of simulation model has some assumptions that

machine capacities are enough to process all forecasted demand with some

considerations incorporated in the model used are, i) All processing times

acquired were deterministic ii) Each transfer movement of a job will have

durations that are exponentially distributed iii) Queue capacities for

processing machines are set at 100 products iv) Jobs can be removed in

batches from queues for processing according to the SPT rule v) When jobs

are arrived to the queues serving processing machines, they will be placed at

the back of the queue according to the scheduling order.

The Figure 8.34 shows the zoomed view of NCM model, the main

arrivals, cells, and jobs departures. For instance, the module refer as

ensures the entry of the job batches within

the system as input. Then each batch is assigned a set of attributes such as job

type and SPT sequence routing, via module. As the part proceeds

through the cell, different attributes record the time delays associated with

material handling, processing, machine transfer etc. The module is

referred as allows parts to the corresponding cell type. Each

part families are assigned to the corresponding cell via modules

Then, it is ready to send parts on its way to the

transfers an entity to a specified machine station, or the next station in the

station visitation sequence defined for the entity. The machine transfer time is

ente for each part sequence as a route time. Now that have the arriving

batches of parts being routed according to their assigned part sequences, a

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part arrives to the cell, queues for a machine, is processed by the machine,

and sent to its next step in the part sequence.

Figure 8.34 NCM Simulation Model by Arena

All cells can be modeled by a set of machines, which each one is

modeled using the module sequence. Finally, the batches leave the system

J OB A

J OB B

M 1

J OB C

J OB D

M 2

M 3

J OB E

J OB F

M 4

J OB G

J OB H

M 5

J OB I

J OB J

J OB K

J OB L

M 6

J OB M

J OB N

J OB O

M 7

J OB P

J OB Q

M 8

J OB R

J OB S

J OB T

M 9

M 1 0

M 1 1

M 1 2

Tr u e

Fa ls e

C OM P L E T E A C C E P T E D

R E T U R N

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

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through the module refer as or as

finished parts. Here four performance measures like time per entity, time per

process, time per resources and time per queue were employed to evaluate the

effect of simulation time in NCM system about VA time, NVA time, Wait

time, Transfer time etc.

8.5.2 Simulation Results and Analysis

Attention to be focused on the routing and on the sufficient

machines capacity in each manufacturing cell. The purpose of procedure

consists about evaluation of movements using the initial model which permits

the part transfers for all jobs. Before proceeding simulation run, the steady

state be established. A plot realized by the depicts the

transient behavior of the simulation model after start-up from the "empty and

idle" state.

Figure 8.35 WIP plot

By running the simulation a plot as shown in Figure 8.35 is created

which explains how the jobs are processing. The plot consists of some SIM

expressions M1 WIP, M2 WIP, up to M12 WIP with time range.

That plot reports the warm up period as 6293 minutes for several run length.

0.0 60.00 .0

1 .0

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Several simulation runs were made for the initial system configuration, each

run for total demand. The results of these simulation runs are realized with the

help of the simulator ARENA. The result of these runs is shown in Table 8.1

to Table 8.4.

Table 8.7 Process summary table

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Table 8.8 Entities summary table

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Table 8.9 Resources summary table

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Table 8.10 Queues summary table

Some sample problems of up to 20 jobs and 12 machines job

resources, queues, process and entities are shown in appendix section. Though

Table 8.1 to 8.4 gives the experiment performance measures for 20 jobs

processed in 12 machines, the average number of batches waiting in machine

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queue, number of jobs sized, VA time, other queue time and the machine

utilization are shown too. The results indicate that M11 is a bottleneck

machine with accumulated waiting time of 4832.12 minutes. It requires

priority for machine rescheduling the jobs. But from the Table 8.2 it is

observed that NVA time and transfer time was zero. So it decides the

effective utilization of machine i.e. idle time of machine is zero. It is observed

that in this problem there are generous benefits gained from employing a

mixed transfer batch.

8.5.3 Conclusion

A methodology-based simulation for evaluating the NCM

scheduling system used to test the industrial case study up to 20 jobs

conducted on 12 machining centers to analyze the optimization scheduling

parameters was carried out. ARENA simulation software version 11.00 is

used to model this problem and study about results for different performance

measures like NVA time, VA time, wait time, queue length and machine

utilization. The model is built based on one of the optimum flow shop

sequence obtained from SPT heuristic and run for more number of

replications. The results are analyzed and modified based on their utilization

and queue lengths. Also, the results show that some resources are excessively

used and lead to slow throughput. This may drastically reduce the number of

parts produced out of the system and increase the average WIP. This causes

bottlenecks in the system can be solved by modifications through increasing

the machine capacity in the NCM. Necessary changes can be made and

simulation results with statistical analysis will enhance the production

manager to view in depth all scenarios of the operations and resource

limitations and optimization with complete solution.

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8.6 SUMMARY

This chapter clearly explains about a development of simulation

methodology to construct the simulation models for small to medium

companies in helping the building of manufacturing models. Some important

factors like availability, risk, cost and performance should be considered

during processing. The simulation tools can be used for utilizing the resource

availabilities of the enterprises, analyzing how the new work order

opportunities might change the system workload to determine the time

constraints that will be assigned for the new project etc. The sub-sections

contain brief definitions of modeling and simulation through ARENA. A

detailed explanation of the simulation process has been explained in this

chapter. Further from NCM simulation section one can understand the use of

simulation technique for helping the decision maker to have all details about

resource analysis and can make scientific decision.