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AN OVERVIEW OF AN OVERVIEW OF SIMULATION , MODELLING SIMULATION , MODELLING

AND ANALYSIS OF AND ANALYSIS OF MANUFACTURING SYSTEMSMANUFACTURING SYSTEMS

Dr. C S P RaoDr. C S P RaoNIT Warangal.NIT Warangal.

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SimulationSimulation• Is the process of building a

mathematical or logical model of a system or a decision problem, and

• experimenting with the model to obtain insight into the system’s behavior or to assist in solving the decision problem.

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SimulationSimulation• Recent survey of management

science practitioners: simulation and statistics have the highest rate of application over all other tools by over a 2:1 margin!

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SimulationSimulation• Usually, a simulation model is a computer

model that imitates a real-life situation. • It is like other mathematical models, but it

explicitly incorporates uncertainty in one or more input quantities

• When we run simulation, we allow these random quantities to take various values, and we keep track of any resulting output quantities of interest

• In this way, we are able to see how the outputs vary as a function of the varying inputs

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Simulation• Is able do deal with problems

exhibiting significant uncertainty• Shows the whole distribution of

results, not just best guesses• Is useful for determining how

sensitive system is to changes in operating conditions

• Enables us to experiment with a system without actually building / changing the physical system!

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ModelsModels

Prescriptive vs. Descriptive

Deterministic vs. Probabilistic

Discrete vs. Continuous

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Descriptive ModelsDescriptive Models

Inputs:

Decision and Uncontrollable

Variables

Outputs:

Measures of Performance or Behavior: Output variables

Simulation Model

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Simulation ProcessSimulation Process• Develop a conceptual model of the

system or problem under study• Build the simulation model• Verify and validate the model• Design experiments using the

model• Perform the experiment and

analyze the results

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Simulation ModelsSimulation Models• Monte Carlo Simulation

• Distribution of an outcome variable that depends on several probabilistic variables

• Risk analysis

• System Simulation• Models sequences of events that

occur over time

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Benefits and LimitationsBenefits and Limitations• Does not require

simplifying assumptions

• Can deal with problems not possible to solve analytically

• Provides an experimental laboratory: possible to evaluate decisions/systems without implementing them

• Generally easier to understand than analytical models

• Building models and simulating is time-consuming for complex systems

• Simulation results / simulated systems are always approximations of the real ones

• Does not guarantee an optimal solution - lack of precise answers

• Should not be used Should not be used indiscriminately in place indiscriminately in place of sound analytical of sound analytical models.models.

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Simulation modelingSimulation modeling• Simulation modeling on spreadsheets

is quite similar to other MgmtSci modeling approaches

• Main difference:• Simulation uses Random Numbers to

drive the whole process

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Simulation ModellingSimulation Modelling • Simulation is a modelling and analysis tool

used for the purpose of designing planning and control of manufacturing systems.

• Simulation may be defined as a concise framework for the analysis and understanding of a system.

• It is an abstract framework of a system that facilitates imitating the behavior of the system over a period of time.

• In contrast to mathematical models, simulation models do not need explicit mathematical functions to relate variables

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• Therefore ,they are suitable for representing complex systems to get a feeling of real system.

• One of the greatest advantage of a simulation models is that it can compress or expand time.

• Simulation models can also be used to observe a phenomenon that cannot be observed at very small intervals of time.

• Simulation can also stops continuity of the experiment.

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• Simulation modelling techniques are powerful for manipulation of time system inputs, and logic.

• They are cost effective for modelling a complex system, and with visual animation capabilities they provide an effective means of learning, experimenting, and analyzing real-life complex systems such as FMS.

• Simulation are capable of taking care of stochastic variable without much complexity.

• They enable the behavior of the system as a whole to be predicted.

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WHEN SIMULATION IS WHEN SIMULATION IS APPROPRIATE?APPROPRIATE?

• Simulation enables the study of, and experimentation with, the internal interactions of a complex system, or of a subsystem within a complex system.

• Informational, organizational, and environmental changes can be simulated, and the effect of these alterations on the model’s behavior can be observed.

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• The knowledge gained in designing a simulation model may be of great value toward suggesting improvement in the system under investigation.

• By changing simulation inputs and observing the resulting outputs, valuable insight may be obtained into which variables are most important and how variables interact

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• Simulation can be used as a pedagogical device to reinforce analytic solution methodologies.

• Simulation can be used to experiment with new designs or policies prior to implementation, so as to prepare for what may happen.

• Simulation can be used to verify analytic solutions.

• By simulating different capabilities for a machine, requirements can be determined.

• Simulation models designed for training allow learning without the cost and disruption of on-the-job learning.

• Animation shows a system in simulated operation so that he plan can be visualized.

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WHEN SIMULATION IS NOT APPROPRIATE?WHEN SIMULATION IS NOT APPROPRIATE?

• The Problem is solved by common sense.• The Problem is solved by analytical means.• It is easier to perform direct experimentation• The resources are not available• The cost exceeds savings• The time is not available• No enough time and personal are not available • Un-reasonable expectations• The behavior of the system is too complex to

define

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THE ELEMENTS OF DISCRETE SIMULATIONTHE ELEMENTS OF DISCRETE SIMULATION

1. Entity: Building blocks of mfg. sys. (M/Cs, AGVs)2. Activities: Function performed by entites.They

are just like verbs in simulation language. Duration is assumed as fixed. Activity start end times are known

3. Events :Points on time scale at which some changes takes places in the model.they represent the beginning or end of one or more activities. events are classified as endogenous (internal), exogenous (external).

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Queues:formed when an entity is waiting in the system for some activity.

Attributes: These are adjectives of simulation language, qualifying nouns.

States:defines the condition of various elements and the model as the whole.

Activity cycle diagram (ACD): This is used in defining the logic of simulation model.

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environment

dispatchArrival

machine

idle

Waiting in queue

Activity cycle diagram

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CONVERSION FOR DRAWING ACDS ARE AS CONVERSION FOR DRAWING ACDS ARE AS FOLLOWS.FOLLOWS.

Each type of entity has an activity cycle.• The cycle consists of activities and queues• Activities and queues alternate in the cycle.• The cycle is closed• Activities are depicted by rectangles and queues

by circles or ellipses.

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Throughput (number of jobs produced per unit of time).Time in system for jobs (makespan).Times jobs spend in queues.Time that jobs spend being transported.Sizes of in-process inventories (WIP or queue sizes).Utilization of equipment and personnel (i.e., proportion of time busy).Proportion of time that a machine is under fadum, blocked until and starved.Proportion of jobs produced which must be reworked or scrapped.Return on investment for a new or modified manufacturing system.

Number and type of machines for a particular objective.Location and size of inventory buffers. Evaluation of a change in product mix (impact of new products).Evaluation of the effect of a new piece of equipment on an existing manufacturing line.Evaluation of capital investments.Manpower requirements planning.Throughput analysis.Makespan analysis.Bottleneck analysis.Evaluation of operational procedures. Evaluation of policies for component part or raw material inventory levels. Evaluation of control strategies

New equipment and buildings are required (called “green fields”).New equipment is required in an old building.A new product will be produced in all or part of an existing building. Upgrading of existing equipment or its operation. Concerned with producing the same product more efficiently. Changes may be in the equipment (e.g., introduction of a robot) or in operational procedures (e.g., scheduling rule employed).

Performance Measurement of Manufacturing System

Manufacturing Issues Manufacturing Environments

Table – 1 : Use of Simulation in Manufacturing

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Problem Definition

Statement of Objectives

Model Formulation , Planning

Data Collection Model Development

Continues Development

Verified?

Validated?

Experimentation

Results Analysis

More Runs?

Documentation & Presentation

Implementation

Steps in simulation study

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Procedure for Conducting a Procedure for Conducting a Simulation StudySimulation Study

Plan Study

Define System

Build Model

Run Experiments

Analyze Output

Report Results

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SIMULATION SOFTWARESIMULATION SOFTWARE

JAVASIMWEB-BASED SIMULATION. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .

ARENA (1993)AutoMODQUEST EXTEND PROMODEL TaylorED WITNESS. . . . . . . . . . .and many more

GPSS (1965)SIMSCRIPT (1963)SIMULAGASP (1961)ALGOLSLAM (1979)SIMAN GPSS/4 (1977)SLAM – IIAWESIM (1995)GEMS

FORTRANC, C + +VB, VB+ + . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .many other oriented languages

Simulation Packages

Simulation language

Channel purpose language

Webbased simulation

3rd Category 2nd Category 1st Category

Table – 2 : Commercial Simulation Language

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Simulation SoftwareSimulation Software

Ref: Law & Kelton, Chapter 3

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Modelling ApproachModelling Approach• Event Scheduling

– System modelled via characteristic events– Events have subroutines which update state

variables.

• Process Orientation– Time oriented sequence of inter-related events

that describes the experience of an entity as it flows through a system.

– Overlay to an event scheduling system.– Approach adopted in most current software.

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Common FeaturesCommon Features1. Generating random numbers (i.e. ~U(0,1))2. Generating random variates from a specified

probability distribution. 3. Advancing the simulation clock.4. Determining the next event on the list event

and passing control of to the appropriate piece of code.

5. Adding and deleting records from a list.6. Collecting output statistics and reporting the

results of the simulation run.7. Trapping error conditions.

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Simulation LanguagesSimulation Languages• General in nature

• Can model almost any type of system.• Frequently include specific modelling

constructs (such as material handling systems).

• Steep learning curve.• Significant modelling and programming

expertise is necessary.• Long(ish) development cycles.

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SimulatorsSimulators• Facilitates the development of

models related to a specific class of problems.

• Short development cycles.• Rapid model prototypes.• Gentle learning curve.• Lack flexibility to model outside of class.• Do not handle “unusual” situations.• Built in assumptions can be problematic.

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A Brief History of SimulationA Brief History of Simulation• Simulation has been around for some time.• Early simulations were event-driven (see

SimscriptMODSIM) and frequently military applications.

• In the 1960’s Geoffrey Gordon developed the transaction (process) based orientation that we are now familiar with.

• Gordon’s software was called General Purpose Simulation System (GPSS).

• GPSS was originally intended for analyzing time sharing options on mainframe computers.

• The software was included as a standard library on IBM 360s and its use was quite widespread.

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GPSS CodeGPSS Code• Assume an

M/M/1system.• Interarrival time =

2.0 minutes (exponential)

• Service time = 1.0 minutes (exponential).

• Assume an infinite queue.

* Simulation of M/M/1 system

SIMULATE

GENERATE RVEXPO(1, 2.0)

QUEUE SERVQ

SEIZE SERVER

ADVANCE RVEXPO(2, 1.0)

RELEASE SERVER

TERMINATE 1

*

* CONTROL STATEMENTS

*

START 1000

END

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SLAMSLAM• IBM stopped support and development of GPSS

about 1972.• A market developed for alternative software that

could run on newer machines (VAX & UNIX).• In 1979, Alan Pritsker and David Pegden create

SLAM (Simulation Language for Alternative Modeling).

• In the early 80s Pritsker and Pegden develop SLAMII, which ran on engineering workstations.– A feature of this new language is a graphical model

builder. – Users enter their model as a network diagram. When

complete, the network is translated into SLAM code.

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SLAM-II CodeSLAM-II Code• Originally one of the

slowest components of a SLAM model was compiling.

• Compiling really translates the model into a set of FORTRAN subroutines.

• To speed up compiles, “controls” were separated from the main body of the model.

• Controls were designed to be short and changeable, while models were to be big and relatively fixed.

1 RESOURCE,,SERVER,1,{1};

2 CREATE,EXPON(2,1),0.0,,INF,1;

3 ACTIVITY;

4 AWAIT,1,{{SERVER,1}},ALL,,NONE,1;

5 ACTIVITY,1,EXPON(1,1);

6 FREE,{{SERVER,1}},1;

7 ACTIVITY;

8 TERMINATE,INF;

;

1 GEN;

2 LIMITS;

3 INITIALIZE,0.0,1000,YES,,NO;

4 NET;

5 FIN;

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SIMANSIMAN• About 1983 or so,

Dennis Pegden develops his own simulation language.

• SIMAN (SIMulation ANalysis).

• The language was designed to run on a PC.

• It is remarkably similar in look, feel, content and style to SLAM.

• A lawsuit entailed.

BEGIN;

CREATE,,EX(2,1);

QUEUE, 1;

SEIZE: SERVER;

DELAY: EX(1,1);

RELEASE:SERVER:DISPOSE;

END;

BEGIN;

DISCRETE, 1000, 1, 1;

RESOURCES: 1, SERVER;

REPLICATE,1;

END;

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SIMAN AND SLAMSIMAN AND SLAM• SIMAN is tailored for the PC market.• SLAM remains focused on workstations.• SIMAN introduces an animation package

(CINEMA) about 1985 or so.• The animation is an add on unit for the model.• Originally it required specialized (& expensive)

hardware.

• SLAM responds with a PC version of SLAM in the late 1980s (which also has animation).

• Both firms develop software to integrate factory scheduling into simulation runs.

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Early PC VersionsEarly PC Versions• By the mid-80s the PC market is

dominant.• Mainframes are expensive.• Cycles are expensive.• Central IS groups are expensive.

• Engineers become computer experts.• A lot of IE’s end up writing simulations.• There are a number of issues:

• User development cycles are very long.• Total memory (model, entities, etc) limited to about

32k by FORTRAN/C and early versions of DOS.• Simulation language development tends to lag OS

development.

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SIMULATORSSIMULATORS• Advent of Windows 3.0/3.1.• Mass penetration of PCs.• Powerful hardware and software (especially OOC)

becomes available.• We start to see the creation of a variety of

simulators.• The simulators are usually graphically oriented

(drag & drop model development),have integrated animation, and low purchase cost.

• Huge number of simulators come onto the market.

• These often lack statistical rigour.

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Today’s MarketToday’s Market• There has been something of a rationalization in

terms of the number of simulation languages/simulators available.

• See the May 1999 edition of IIE Solutions.

• The large simulation companies have all been bought or sold at least a couple of times in the past two – three years.

• SLAM Frontstep Systems (a logistics software supplier). Not in active development (last release ’99).

• SIMAN Rockwell Software (logistics & controls).

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TrendsTrends• Virtual reality animations.• Advanced statistical functions

• Curve fitting for input data.• Automatic detection of warm up• Output analysis modules (including

replication).

• Bolt on “Optimizers” – Tools to search for optimal settings of parameters.

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Witness (Lanner Inc)Witness (Lanner Inc)• Simple building block

design• Interactive• Full range of logic and

control options• Elements for discrete

manufacture, process industries, BPR, e-commerce, call centers, health, finance and government

• Statistical input and reports

• Link system to other software easily (CAD/Excel)

• Optional 3D/VR views• Model Optimisation

$13,000-$17,000 ($US)

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ARENAARENA• Process hierarchy. • Integrates with Microsoft desktop tools• Spreadsheet interface• Crystal reports• Free runtime software. • Fully graphical environment. No programming required.• VBA embedded.• Optimization with OptQuest for Arena.• Builds reusable modules.• $1,000 - $17,000 ($US). Various add-in modules available.

44

ARENAARENA

• Arena can be used for simulating discrete and continuous systems

• Arena employs an object based design for entirely graphical model development.

• Modules are organized into collections called templates.

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GPSS/HGPSS/H• Successor to the “orginal”

simulation language (GPSS).– Was freeware on IBM

360’s • Makes use of common

program blocks.• Proven, reliable software.• Extremely flexible.• Extensive error checking

routines.• Post-process animations

(Proof) can be built.• ~$5,000 ($US)

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AutomodAutomod• Combines Virtual

Reality (VR) graphics with a discrete and continuous simulation environment. – Manufacturing operations – Material handling systems – Tanks and pipe networks – IC Manufacturing– Transportation and

logistics systems

• $15,000 - $100,000 ($US)

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AutoModAutoMod

• It includes the AutoMod simulation package, AutoStat for experimentation and analysis, and Auto View for making AVI movies of the built-in 3-D animation.

• The main focus of the AutoMod simulation product is manufacturing and material handling systems.

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QUESTQUEST• QUEST if offered by Deneb Robotics • QUEST models are based on 3-D CAD

geometry. • A QUEST model consists of elements from

a number of element classes. Built-in element classes include AGV and transporters, buffer, conveyor, labour, machine, parts and process.

• Each element has associated geometric data and parameters that define its behaviour

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ProModel ProModel

• ProModel is offered by ProModel Corporation

• It is a simulation and animation tool designed to model manufacturing systems.

• ProModel offers 2-D animation with an optional 3-D like perspective view.

50

WITNESSWITNESS

• WITNESS is offered by the Lanner Group.• WITNESS is strongly machine oriented

and contains many elements for discrete-part manufacturing.

• WITNESS models are based on template elements. Elements may be combined into a designer element module to be reused

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METHODOLOGY FOR SELECTION OF METHODOLOGY FOR SELECTION OF SIMULATION SOFTWARESIMULATION SOFTWARE

Need for purchasing simulation software

Initial software survey

Evaluation

Software selection

Software contract negotiation

Software purchase

Stage 1

Stage 2

Stage 3

Stage 4

Stage 5

Stage 6

Figure – 3 : Stages of simulation software selection methodology

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Need for purchasing simulation software

Purpose of

simulation

Constraints

Models to be

simulated

Model develope

rs

Education

Individual

preference

“Quick and

dry” - ind

D/C –ind or

research

Time Discrete

Continuo.

Combined

disc/cont

Previous exper. in

simulation

Initial software

survey

Continued in the next slide

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Initial software survey

Initial software survey

Initial software survey

Initial software survey

Software selection

Software contract

negotiation

Software purchase

Contract acceptable

Legend:

Stages

IntermediateResults

Elements

Short list of software for evaluation

Results of Evaluation

Selection of software

Initial software survey

Initial software

survey

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PromodelPromodel• State-of-the-art

simulation engine • Graphical user interface • Distribution-fitting.• Output analysis module• Optional optimizer.• Modules designed for:

– Manufacturing– Healthcare– Services

• $17,000 ($US)

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Case studyCase study

20 x 20

20 x 20

20 x 20

20 x 20

20 x 20

20 x 20

20 x 20

20 x 20

20 x 20

20 x 20

M1

M2

M3

M4

M5

M6

M7

M8

M9

M10

Area (m2) Machine

Table – 1 : Machine Area Information

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16031028026580

12536024017595

10023028531550

275260

150

8-6-8-10-47-9-26-5

3-1-35-6-7-107-9-7-8

7-93-4-1-6

2-72-7-9-510-8-51-3-10

8-10-5-69-2-76-8-10

4-36-5

4-3-1

P1P2P3P4P5P6P7P8P9

P10P11P12P13P14P15P16P17

P18

Quantity Job Sequence Part Type

Table – 2 : Part Job Sequence and Quantity Information

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Part TypeMachines

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10

P1 0 0 0 2 0 3 0 2+1

0 4

P2 0 6 0 0 0 0 2 0 3 0

P3 0 0 0 0 4 1 0 0 0 0

P4 2 0 2+5

0 0 0 0 0 0 0

P5 0 0 0 0 4 2 1 0 0 5

P6 0 0 0 0 0 0 2+1

2 4 0

P7 0 0 0 0 0 0 4 0 1 0

P8 3 0 4 2 0 2 0 0 0 0

P9 0 3 0 0 0 0 2 0 0 0

P10 0 5 0 0 1 0 3 0 1 0

P11 0 0 0 0 2 0 0 2 0 1

P12 1 0 3 0 0 0 0 0 0 3

P13 0 0 0 0 1 2 0 2 0 3

P14 0 2 0 0 0 0 1 0 3 0

P15 0 0 0 0 0 2 0 1 0 2

P16 0 0 4 2 0 0 0 0 0 0

P17 0 0 0 0 5 3 0 0 0 0

P18 4 0 6 2 0 0 0 0 0 0

Table – 3 : Processing Time Information (in minutes)

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Cell FormationCell 1 = 3 1 10 4Cell 2 = 8 6Cell 3 = 2 9 7 5

Here the initial solution for the above case study is obtained using genetic algorithm as below

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(Run Hours 231.57)

60

from above table average process time in percentage of total scheduled hours =

(39.62+14.97+20.04+11.88+11.16+21.7+16.99+22.89+23.89+20.51)/18 = 11.31%=0.1131 average process time = 0.1131*231.57*60=1572.05

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average material handling time per part type = (85.70*231.57*60)/(100*18) = 661.52 min.

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Run hours 352 The solution for the above case study using heuristic method is as follows Step 1 : Arrange all machines randomly according to the given dimensions of machines. Here machine to machine clearance of 1 m is also considered.

(.85)

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Step 2 : From job sequence of parts, check the minimum sequence (2 machines) common for all parts e.g. M7 – M9, M5 – M6, M4 – M3, M8 – M6, M8 – M10 and bring those 2 machines closer or nearer to each other.

(Run Hours 229.37)

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Step 3 : From job sequence, calculate number of times, all parts uses the same machines.

M1-4M2-4M3-6M4-4M5-6M6-7M7-8M8-6M9-5

M10-6

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The least utilized machines are M1,M2 and M4. these machines are kept away from remaining machines or at periphery so that they will not obstruct other more utilized machines.( e.g. 3-1-3, 1-3-10, 3-4-1-6 i.e. 3 must be closer to 1 and 2-7, 2-7-9-5, 9-2-7, i.e. 2 must be closer to 7 & 9). In this step, since the row distance is high, it will take more time for the vehicle to move from one machine to another machine. So the row distance is reduced from 5 machines to 3 and 4 machines.

(Run hours 223.24)

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Step 4: from above we can see that most utilized machine is M7. checking the position of this machine in the job sequence and corresponding parts to be processed.1st –3, 310+125+360=7952nd – 2, 175+95=2703rd –3, 80+125+315=520from above 1st position is higher. It must be placed at corner position so that processing starts from that position only.( eg. 4, 8 also.)

Run hours 220

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Step 5: Here M5 is accompanied M6, M7 is accompanied by M9, M3 is accompanied by M4. These machines are kept at minimum possible distance. Step 6: now considering maximum number of parts to be processed and their job sequence.P7=360, 7-9P2=310, 7-9-2,P14=315, 9-2-7So these machines are at minimum distance in straight line manner (7-9-2)In next iteration next lower maximum parts are considered.

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(Run hours 213)

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Step 7: place remaining machines closer to respective machines according to job sequence.

average process time(%) per part = (42.98+16.24+21.75+12.88+12.10+23.54+18.43+24.83+25.92+22.25)/18 = 12.273%=0.1273average process time per part type =0.1273*213.45*60 = 1571.84 min.

70

average material handling time per part type = (82.26*213.45*60) / (100*18) = 585.27 min average process time(%) per part = (42.98+16.24+21.75+12.88+12.10+23.54+18.43+24.83+25.92+22.25)/18 = 12.273%=0.1273average process time per part type =0.1273*213.45*60 = 1571.84 min.

71

ELL FORMATION

ell 1 – 2 9 7

ell 2 - 10 5 6

ell 3- 4 3 8 1

72

.3 EFFECT OF NUMBER OF VEHICLES ON CYCLE TIME PER JOB TYPE

he effect of number of vehicles used for material handling purpose on the cycle time is shown below in the tables for case study both for GA and heuristic layout. It is seen that as the number of vehicles are increased, cycle time( average material handling time) is getting reduced. From this we can find out optimum number of vehicles needed for given throughput

Case study 2(GA)

No. of vehicles

1 2 3 4 5 6 7 8

Total Time(hrs.) 457.35 231.57 160.29 123.55 100.79 91.75 91.75 91.75

Material Handling time(min.)

1322.96

661.52 440.95 330.74 264.57 220.5 189 165.36

Process Time(min.

)1572.2

21572.2

21572.2

21572.2

21572.2

21572.22 1572.22 1572.22

73

Cycle Time(min

.)2895.

182233.

742013.

171902.

961836.

791792.7

21761.2

21737.5

8

Case study 2 (heuristic) No. of

vehicles 1

2 3 4 5 6 7 8

Total Time(hrs.

)

422.39

213.45

144.09

109.73

91.78 91.78 91.78 91.78

Material Handling time(min.

)

1170.58

585.27

390.19

292.61

234.10

195.01

167.22

146.32

Process Time(min

.)

1571.95

1571.95

1571.95

1571.95

1571.95

1571.95

1571.95

1571.95

Cycle Time(min

.)

2742.53

2157.22

1962.14

1864.56

1806.05

1766.96

1739.17

1718.27

74

RESULTS AND DISCUSSIONSRESULTS AND DISCUSSIONS

) Summarized Table of grouping efficiency, total throughput time and total material handling time for two case studies.

Initial solution by GA

Improved solution by heuristic

Percentage improvement

GE( % )

Tt

(hrs.)

Mt

(min.)GE

( % )Tt

(hrs.)

Mt

(min.)GE

( % )Tt

(hrs.)

Mt

(min.)

Case study 2

76 231.57661.5

281

213.45

585.27

6.57 7.82 11.52

GE : Group EfficiencyTt : Total Throughput Time

Mt : Total material Handling Time

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Summarized Table of machine utilization for the case study

Machine Initial solution Improved solution Percentage

M1 14.97 16.24 8.48

M2 21.7 23.54 8.47

M3 39.62 42.98 8.48

M4 11.88 12.88 8.41

M5 23.89 25.92 8.5

M6 20.51 22.25 8.48

M7 22.89 24.83 8.47

M8 11.16 12.1 8.42

M9 16.99 18.43 8.35

by GA ( % ) by heuristic ( % ) improvement

M10 20.04 21.75 8.53

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The bar charts of machine utilization for 2 case studies are shown below.Case study 2(GA)

77

Case study 2(HEURISTIC METHOD)

78

1)   Summarized Table of resource utilization for two case studies

Case study 1 Run hours 40.68 37.59 7.6

forklift 1 31.67 27.33 20.25

Case study 2 Run hours 231.57 213.45 7.82

forklift 1 99.83 99.92 7.74

Resource

Initial solutionby GA ( % )

Improved solution by heuristic ( % )

Percentageimprovement

forklift 2 30 25.3 22.07

forklift 2 99.83 99.93 7.73

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Resource utilization bar charts for 2 case studies Case Study 2 (GA)

Case Study 2 (Heuristic Method)

80

Resource states graphs of 2 case studies

Case Study 2 (Heuristic Method)

81

Graphs of cycle time verses number of vehicles for two Graphs of cycle time verses number of vehicles for two

case studiescase studies

Graph - 2 : Case Study - 2

1500

1700

1900

2100

2300

2500

2700

2900

1 2 3 4 5 6 7 8

Number of Vehicles

Cy

cle

Tim

e

GA

Heuristic

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CONCLUSIONSCONCLUSIONS

• the application for simulation to address manufacturing problems.

• Developments in the area of simulation – existing softwares for discrete event simulation and conduction of simulation studies were reviewed.

• The necessity and importance of simulation for modeling and analyzing the various classes of manufacturing problems was focused in this paper;

• we hope this paper may encourage the extensive use of simulation in manufacturing and development of simulation technology for addressing the problems which need serious attention.

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JournalsJournals• ACM Transactions on Modelling and Computer Simulation• Computer Simulation Modeling and Analysis• European Journal of Operations Research• IEEE Journal of Systems, Man and Cybernetics• IIE Transactions on IE Research• International Journal in Computer Simulation• Management Science• ORSA Journal on Computing• Simulation• System Dynamics Review• Journal of the Operational Research Society

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ReferencesReferences• Averill M. Law, W. David, Kelton,2000 “Simulation Modeling

and Analysis”, McGraw-Hill• Charles Harrell, et al., 2000, “Simulation Using ProModel”,

McGraw-Hill• Ramsey Suliman, et al.,2000 “Tools and Techniques for

Social Science Simulation”, Physica Verlag• Michael Pidd, 1998, “Computer Simulation in Management

Science”, John Wiley & Sons• Michael Prietula, et al., 1998, “Simulating Organizations:

Computational Models of Institutions and Groups”, Mit. Press

• David Profozich,1997, “Managing Change with Business Process Simulation”, Pearson Ptr.

• Paul A. Fishwick, Richard B. Modjeski, 1991, “Knowledge-Based Simulation”,Springer-Verlag

• Klaus G. Troitzsch, et al., 1996, “Social Science Microsimulation”, Springer Verlag

• Harry A. Pappo, 1998, “Simulations for Skills Training”, Educational Technology Publications

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Thank youThank youContact information

Dr.C.S.P. RaoManufacturing Simulation Lab.

Department of Mech. Engg.Regional Engineering College - Warangal –

506 004E-mail : cspr@recw.ernet.in (or)

csp_rao@rediffmail.com