AN OVERVIEW OF SIMULATION , MODELLING AND ANALYSIS OF MANUFACTURING MANUFACTURI NG SYSTEMS
Dr. C S P Rao NIT Warangal. 1
Simulation • 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. 2
Simulation • 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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Simulation • 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 4
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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Models Pre Prescrip ripti tiv ve
vs.
Descrip ripti tiv ve
Determ termin inis isti tic c
vs.
Pro Probabil ilis isti tic c
Discret rete
vs.
Conti tin nuous
6
Descriptive Models Outputs:
Inputs: Decision and Uncontrollable Variables
Simulation Model
Measures of Performance or Behavior: Output variables
7
Simulation Simulation Process • Develop a conceptual model of the system or problem under study • Build the simulation model • Ver Verify ify and val valida idate te the mod model el • Design experiments using the model • Pe Perf rfor orm m the exp exper erime iment nt and and analyze the results 8
Simulation 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 9
Benefits 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
• Bui uild ldin ing g mod model els s and and simulating is timeconsuming for complex systems • Sim imul ulat atio ion n re resu sullts / simulated systems are always approximations of the real ones • Does not guarantee an optimal solution - lack of precise answers • Should not be used indiscriminately in place of sound analytical 10
Simulation 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 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 12
• 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. 13
• Simula Simulatio tion n modell modelling ing tec techni hnique ques s 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. • Simu Simulatio lation n are are capa capable ble of takin taking g care care of stochastic variable without much complexity. • They enable the behavior of the system as a whole to be predicted. 14
WHEN SIMULATION IS APPROPRIATE? • Simulation enables the study of, and experimentation experimenta tion with, the t he 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 16
• Simula Simulatio tion n can be used used as a ped pedagog agogical ical dev device ice to reinforce analytic solution methodologies. • Sim Simula ulatio tion n can can be use used d to to exper experime iment nt with with new designs or policies prior to implementation, so as to prepare for what may happen. • Sim Simula ulatio tion n can can be be used used to ver verify ify anal analyti ytic c solutions. • By simula simulating ting dif differ ferent ent capab capabili ilitie ties s for a mach machine ine,, requirements can be determined. • Sim Simula ulatio tion n models models des design igned ed for for trai trainin ning g allow allow learning without the cost and disruption of onthe-job learning. • Ani Animati mation on shows shows a syste system m in sim simulat ulated ed oper operatio ation n so that he plan can be visualized. 17
WHEN SIMULATION IS NOT APPROPRIATE?
• • • • • • • • •
The Prob The Proble lem m is sol solve ved d by comm common on sen sense se.. The Pro Proble blem m is sol solve ved d by anal analyti ytical cal mea means. ns. It is is easie easierr to per perfor form m direc directt exper experime imentat ntation ion The Th e res resour ource ces s are are no nott ava avail ilab able le The Th e co cost st ex exce ceed eds s sav savin ings gs The Th e ti time me is no nott ava avail ilab able le No eno enough ugh time and per persona sonall are are not not avai availab lable le Un-re Un -reaso asonab nable le ex expe pecta ctati tion ons s The beh behavi avior or of the sys system tem is too too comp complex lex to define
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THE ELEMENTS OF DISCRETE SIMULATION 1. 2.
3.
Entity Enti ty:: Bui Build ldin ing g bl bloc ocks ks of mf mfg. g. sy sys. s. (M (M/Cs /Cs,, AGV AGVs) s) Acti Ac tivi viti ties es:: Fun Funct ctio ion n pe perf rfor orme med d by en enti tite tes. s.Th They ey are just like verbs in simulation language. Duration is assumed as fixed. Activity start end times are known 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
Arrival
Waiting in queue
dispatch
machine
idle
Activity cycle diagram
21
CONVERSION FOR DRAWING ACDS ARE AS FOLLOWS. Each type of entity has an activity cycle. • The cyc cycle le cons consist ists s of act activi ivitie ties s and and queue queues s • Activities and queues alternate in the cycle. • The cycle is closed • Act Activi ivitie ties s are dep depict icted ed by by recta rectangl ngles es and and queues queues by circles or ellipses.
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Table – 1 : Use of Simulation in Manufacturing Manufacturing Environments
Manufacturing Issues
Performance Measurement of Manufacturing System
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).
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 (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).
Throughput analysis. Makespan analysis. Bottleneck analysis. Evaluation of operational procedures. Evaluation Evaluati on of policies for component part or raw material inventory levels. Evaluation Evaluati on of control strategies
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 manufacturing system.
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Problem Definition Statement of Objectives
Model Formulation , Planning
Data Collection
Steps in simulation study
Model Development
Continues Development Verified? Validated? Experimentation Results Analysis More Runs? Documentation & Presentation
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Procedure for Conducting a Simulation Study Plan Study Define System Build Model Run Experiments Analyze Output Report Results
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SIMULATION SOFTWARE 1st Category
2nd Category
3rd Category
Channel purpose language
Simulation language
Simulation Packages
FORTRANC, C + +VB, VB+ + . . ............ . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .many other oriented languages
GPSS (1965)SIMSCRIPT (1963)SIMULA GASP (1961)ALGOL SLAM (1979)SIMAN GPSS/4 (1977)SLAM – IIAWESIM (1995)GEMS
ARENA (1993)AutoMOD QUEST EXTEND PROMODEL TaylorED WITNESS. . . . . . . . . . .and many more
Webbased simulation
JAVASIMWEBBASED SIMULATION.. . . SIMULATION . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . .....
Table – 2 : Commercial Simulation Language
26
Simulation Software
Ref: Law & Kelton, Chapter 27 3
Modelling Approach • Event Scheduling – System System modelled modelled via character characteristic istic event events s – Events have subroutines which update state variables.
• Pr Proc oces ess s Or Orie ient ntat atio ion n – Time oriente oriented d sequence sequence of inter-re inter-related lated events events that describes the experience of an entity as it flows through a system. – Overl Overlay ay to an event event schedu scheduling ling system. – Appro Approach ach adopted adopted in most most current current software. software. 28
Common Features 1. Gene Genera rati ting ng ra rand ndom om nu numb mber ers s (i. (i.e. e. ~U ~U(0 (0,1 ,1)) )) 2. Ge Gene nera rati ting ng ra rand ndom om var varia iate tes s fro from m a spe speci cifi fied ed probability distribution. 3. Ad Adva vanc ncin ing g th the e si simu mula lati tion on cl cloc ock. k. 4. De Dete term rmin inin ing g the the ne next xt ev even entt on on the the li list st ev even entt and passing control of to the appropriate piece of code. 5. Ad Addi ding ng an and d del delet etin ing g rec recor ords ds fr from om a lis list. t. 6. Co Coll llec ecti ting ng out outpu putt stat statis isti tics cs and and rep repor orti ting ng the the results of the simulation run. 7. Tra rap pping error co con ndit itiions. 29
Simulation Languages • General in nature • Can model almost any type of system. • Frequently include specific modelling constructs (such as material handling systems). • Ste Steep ep learni learning ng curve. curve. • Signif Significant icant modelli modelling ng and programming programming expertise is necessary. • Long(i Long(ish) sh) develop development ment cycles. cycles.
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Simulators • Facilitates the development of models related to a specific class of problems. • Short deve developmen lopmentt cycles. cycles. • Rapid model protot prototypes. ypes. • Gentle learni learning ng curve curve.. • Lack flexibi flexibility lity to model model outside outside of class. class. • Do not handle “unusual” situations. • Built in assumptions can be problematic. 31
A Brief History of Simulation • Simul Simulat atio ion n has bee been n arou around nd for for some some time time.. • Ear Early ly simu simulat lation ions s were were eve event-d nt-driv riven en (se (see e SimscriptMODSIM) and frequently military applications. • In the the 1960 1960’s ’s Geoff Geoffrey rey Gor Gordon don dev develo eloped ped the transaction (process) based orientation that we are now familiar with. • Gor Gordon don’s ’s soft softwar ware e was was calle called d Gene General ral Purp Purpose ose Simulation System (GPSS). • GPS GPSS S was ori origin ginall ally y inten intended ded for ana analyz lyzing ing tim time e sharing options on mainframe computers. • The sof softwar tware e was was includ included ed as as a stan standar dard d libra library ry on IBM 360s and its use was quite widespread. 32
GPSS Code • Assume an M/M/1system. • Interarrival time = 2.0 minutes (exponential) • Service time = 1.0 minutes (exponential). • Assume an infinite queue.
*
Sim imul ula ati tion on of M/ M/M/ M/1 1 sy syst stem em SIMULATE GENERATE
RVEXPO(1, 2.0)
QUEUE
SERVQ
SEIZE
SERVER
ADVANCE
RVEXPO(2, 1.0)
RELEASE
SERVER
TERMINATE
1
* *
CONTROL ST STATEMENTS
* START
1000
END
33
SLAM • IBM stop stopped ped sup support port and dev develo elopme pment nt of of GPSS GPSS about 1972. • A market market dev develo eloped ped for alt altern ernativ ative e softw software are that that could run on newer machines (VAX & UNIX). • In 1979 1979,, Alan Alan Prit Pritske skerr and and David David Peg Pegden den cre create ate SLAM (Simulation Language for Alternative Modeling). • In the the earl early y 80s 80s Prits Pritsker ker and Pegd Pegden en deve develop lop SLAMII, which ran on engineering workstations. – A feature feature of this this new new language language is a graphic graphical al model model builder. – User Users s enter enter their model as a netw network ork diagr diagram. am. Whe When n complete, the network is translated into SLAM code. 34
SLAM-II Code • Ori rigi gin nal ally ly 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 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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SIMAN • About 1983 or so, Dennis Pegden develops his own simulation language. • SIM IMAN AN (S (SIM IMul ulat atio ion n ANalysis). • The lang ngua uag ge wa was s 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;
36
SIMAN 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 anima animation tion is an an add on unit unit for the model. model. • Originally it required specialized (& expensive) hardware.
• SLAM responds with a PC version of SLAM in the late 1980s (which also has animation). • Bo Both th firms firms devel develop op soft softwar ware e to integr integrate ate factory scheduling into simulation runs. 37
Early PC Versions • By the mid-80s the PC market is dominant. • Mainfr Mainfram ames es are expen expensive sive.. • Cyc Cycles les are exp expens ensive. ive. • Centr Central al IIS S groups groups are are expens expensive. ive.
• 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. 38
SIMULATORS • Adve Advent nt of of Win Windo dows ws 3.0 3.0/3 /3.1 .1.. • Ma Mass ss pe pene netr trat atio ion n of PC PCs. s. • Pow Powerf erful ul hard hardware ware and sof softwar tware e (espe (especia cially lly OOC OOC)) becomes available. • We star startt to see the cre creati ation on of of a var varie iety ty of simulators. • The simu simulat lators ors are usua usually lly gra graphi phical cally ly orie oriente nted d (drag & drop model development),have integrated animation, and low purchase cost. • Hu Huge ge num numbe berr of sim simul ulato ators rs com come e onto onto the the market. • Th Thes ese e ofte often n lack lack sta statis tisti tica call rigo rigour ur.. 39
Today’s Market • There There has has bee been n someth something ing of a rati rationa onaliz lizati ation on in 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). 40
Trends • Virtual reality animations. • Advanced statistical functions • Curve fitting for input data. • Automati Automatic c detection detection of of warm warm up • Output analysi analysis s modules modules (including (including replication).
• Bolt Bolt on on “Opti “Optimiz mizers ers”” – Tool Tools s to to search for optimal settings of parameters. 41
Witness (Lanner Inc) • • • •
• •
Simple building block design Interactive Full range of logic and control options Elements for discrete manufacture,, process manufacture industries, BPR, ecommerce, call centers, health, finance and government Statistical input and reports Link system to other software easily (CAD/Excel) Optional 3D/VR views
$13,000-$17,000 $13,000-$17,00 0 ($US) 42
ARENA • • • • • • • • • •
Pro roce cess ss hie ierrarch chy. y. Inte In tegr grat ates es with with Mic Micro roso soft ft desk deskto top p tools tools Spr prea eads dshe hee et int interf rfa ace Crystal re reports Fre Fr ee ru runti tim me sof softtwar are. e. Fully Ful ly graph graphica icall enviro environm nment ent.. No progr program ammin ming g requir required. ed. VBA embedded. Opti Op timi miza zati tion on wit with h OptQ OptQue uest st for for Are Arena na.. Buil Bu ilds ds re reus usab able le mo modu dule les. s. $1,000 $1, 000 - $17 $17,00 ,000 0 ($U ($US). S). Va Vario rious us add add-in -in mo modul dules es ava availa ilable ble.
43
ARENA • 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.
44
GPSS/H • Succ Succes esso sorr to to the the “o “org rgin inal al” ” simulation language (GPSS). – Was freeware on IBM 360’s
• Make kes s us use of of co common program blocks. • Proven, reliable software. • Extremely flexible. • Extensive error checking routines. • Post-process animations (Proof) can be built. • ~$5,000 ($US) 45
Automod • Combines Vi Virtual Reality (VR) graphics with a discrete and continuous simulation environment. – Manufacturing Manufacturing operations – Material handling systems – Tanks and pipe networks – IC Manufacturing – Transportation Transportation and logistics systems
• $15, $15,00 000 0 - $100 $100,0 ,000 00 ($US ($US)) 46
AutoMod • 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. 47
QUEST • 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 48
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. 49
WITNESS • WITNESS is offered by the Lanner Group. • WITNESS is strongly machine oriented and contains many elements for discretepart manufacturing. • WIT WITNE NESS SS model models s are are base based d on tem templa plate te elements. Elements may be combined into a designer element module to be reused
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METHODOLOGY FOR SELECTION OF METHODOLOGY SIMULATION SOFTWARE Stage 1
Need for purchasing simulation software
Stage 2
Initial software survey
Stage 3
Stage 4
Evaluation
Software selection
Stage 5
Software contract negotiation Stage 6
Software purchase
Figure – 3 : Stages of simulation software selection methodology methodology
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Need for purchasing simulation software
Purpose of simulatio n
Educati on
“Quick and dry” ind
Models to be simulate d
Constrai nts
D/C – ind or researc h
Time
Discret e
Contin uo.
Model develope rs
Combi ned disc/co nt
Individ ual prefere nce
Previous exper. in simulation
Initial software survey
Continued in the next slide
52
Short list of software for evaluation
Initial
Initial software software survey
survey
Initial software survey
Initial software survey
Initial software survey
Initial software survey
Results of Evaluation
Software selection
Selection of software Legend:
Software contract negotiation Contract acceptable
Software purchase
Stages
Intermediate Results
Elements
53
Promodel • 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) 54
Case study Table – 1 : Machine Area Information Machine
Area (m2) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
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
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Table – 2 : Part Job Sequence and Quantity Information Information
Part Type P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 P15 P16 P17 P18
Job Sequence 8-6-8-10-4 7-9-2 6-5 3-1-3 5-6-7-10 7-9-7-8 7-9 3-4-1-6 2-7 2-7-9-5 10-8-5 1-3-10 8-10-5-6 9-2-7 6-8-10 4-3 6-5 4-3-1
Quantity 160 310 280 265 80 125 360 240 175 95 100 230 285 315 50 275 260 150 56
Table – 3 : Processing Time Information (in minutes) Part Type
Machines M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
P1
0
0
0
2
0
3
0
0
4
P2
0
6
0
0
0
0
2
2+ 1 0
3
0
P3
0
0
0
0
4
1
0
0
0
0
P4
2
0
0
0
0
0
0
0
0
P5
0
0
2+ 5 0
0
4
2
1
0
0
5
P6
0
0
0
0
0
0
2
4
0
P7
0
0
0
0
0
0
2+ 1 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
57
Here the initial solution for the above case study is obtained using genetic algorithm as below
Cell Formation
Cell 1 = 3 Cell 2 = 8 Cell 3 = 2
1 6 9
10
4
7
5
58
(Run Hours 231.57) 59
from above table average process time in percentage perc entage 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 0.1131*231.57*60=1572.05 60
average material handling time per part type = (85.70*231.57*60)/(100*18) = 661.52 min .
61
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)
62
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)
63
Step 3 : From job sequence, calculate number of times, all parts uses the same
machines.
M1-4 M2-4 M3-6 M4-4 M5-6 M6-7 M7-8 M8-6 M9-5 M10-6 64
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, 13-10, 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)
65
Step 4: 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=795 2nd – 2, 175+95=270 3rd –3, 80+125+315=520 from 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
66
Step 5: 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: 6: now considering maximum number of parts to be
processed and their job sequence. P7=360, 7-9 P2=310, 7-9-2, P14=315, 9-2-7 So these machines are at minimum distance in straight line manner (7-9-2) In next iteration next lower maximum parts are considered.
67
(Run hours 213)
68
Step 7: 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+2 2.25)/18 = 12.273%=0.1273 average process time per part type =0.1273*213.45*60 = 1571.84 min.
69
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 (42.98+16.24+21.75+12.88+12 .10+23.54+18.43+24.83+25.92+22.25)/18 +22.25)/18 = 12.273%=0.1273 average process time per part type =0.1273*213.45*60 = 1571.84 min.
70
ELL FORMATION
ell 1 – 2
9
7 71
.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 Case study 2(GA) reduced. From this we can find out optimum number of vehicles needed for given throughput 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.)
1322.9 6
661.52
440.95
330.74
264.57
220.5
189
165.36
Process Time(min. )
1572.2 2
1572.2 2
1572.2 2
1572.2 2
1572.2 2
1572.22
1572.22
1572.22
72
Cycle Time(min .)
2895. 18
2233. 74
2013. 17
1902. 96
1836. 79
1792.7 2
1761.2 2
1737.5 8
Case study 2 (heuristic) No. of vehicles
2
3
4
5
6
7
8
422.3 9
213.4 5
144.0 9
109.7 3
91.78
91.78
91.78
91.78
Material Handling time(min. )
1170. 58
585.2 7
390.1 9
292.6 1
234.1 0
195.0 1
167.2 2
146.3 2
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
Total Time(hrs. )
1
73
RESULTS AND DISCUSSIONS
Summarized Table of grouping efficiency, total throughput time and total material handling time for two case studies. )
Initial solution by GA
Case study 2
GE (%)
Tt (hrs.)
76
231.57
Mt (min.)
661.5 2
GE : Group Efficiency Tt : Total Throughput Time Mt : Total material Handling Handling Time
Improved solution by heuristic
GE (%)
Tt (hrs.)
81
213.4 5
Mt (min.)
585.2 7
Percentage improvement
GE (%)
Tt (hrs.)
6.57
7.82
Mt (min.)
11.52
74
Summarized Table of machine utilization for the case study
Machine
Initial solution
Improved solution
Percentage
by GA ( % )
by heuristic ( % )
improvement
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
M10
20.04
21.75
8.53
75
The bar charts of machine utilization for 2 case studies are shown below. below. Case study 2(GA)
76
Case study 2(HEURISTIC METHOD)
77
1)
Summarized Table of resource utilization for two case studies
Case study 1
Case study 2
Resource
Initial solution by GA ( % )
Improved solution by heuristic ( % )
Percentage improvement
Run hours
40.68
37.59
7 .6
forklift 1
31.67
27.33
20.25
forklift 2
30
25.3
22.07
Run hours
231.57
213.45
7.82
forklift 1
99.83
99.92
7.74
forklift 2
99.83
99.93
7.73
78
Resource utilization bar charts for 2 case studies stud ies Case Study 2 (GA)
Case Study 2 (Heuristic Method)
79
Resource states graphs of 2 case studies
Case Study 2 (Heuristic Method )
80
Graphs of cycle time verses number of vehicles for two case studies Graph - 2 : Case Study - 2
2900 2700 2500
m i T 2300 e l c y 2100 C
GA Heuristic
1900 1700 1500 1
2
3
4
5
6
7
8
Number of Vehicles
81
CONCLUSIONS • the app applic licati ation on for sim simula ulation tion to add addres ress s manufacturing problems. • Developments in the area of simulation – existing softwares for discrete event simulation and conduction of simulation studies were reviewed. • The nec necess essity ity and imp importa ortance nce of simul simulatio ation n for 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. 82
Journals • • • • • • • • • • •
ACM Transactions on Modelling and Computer Sim Computer Simulation Modeling and Analysis Europe Eur opean an Jou Journal rnal of Ope Operat ration ions s Rese Researc arch h IEEE IEE E Journa Journall of of Syst Systems, ems, Man and Cyb Cybern erneti etics cs IIE II E Tra Transa nsact ctio ions ns on IE Re Rese sear arch ch Interna Int ernatio tional nal Jour Journal nal in Com Compute puterr Simul Simulatio ation n Man anag age eme ment nt Sc Scie ienc nce e ORSA OR SA Jo Jour urna nall on on Com Compu puti ting ng Simulation Syst Sy stem em Dy Dyna nami mics cs Re Revi view ew Journal of the Operational Research Society 83
References • Averill M. Law, W. David, Kelton,2000 “Simulation Modeling and Analysis”, McGraw-Hill • Charles Harrell, et al., 2000, “Simulation Using ProModel”, McGraw-Hill • Ra Rams msey ey Sulim Suliman, an, et al., al.,200 2000 0 “Tools “Tools an and d Techn Techniqu iques es for for Social Science Simulation”, Physica Verlag • Michael Pidd, 1998, “Computer Simulation in Managemen Managementt Science”, John Wiley & Sons • Michael Prietula, et al., 1998, “Simulating Organizations: Computational Models of Institutions and Groups”, Mit. Press • Da David vid Prof Profozi ozich, ch,199 1997, 7, “Man “Managin aging g Change Change with Bus Busine iness ss Process Simulation”, Pearson Ptr. • Paul A. Fishwick, Richard B. Modjeski, 1991, “KnowledgeBased Simulation”,Springe Simulation”,Springer-Verlag r-Verlag • Klaus G. Troitzsch, et al., 1996, “Social Science Microsimulation”, Microsimulation ”, Springer Verlag • Ha Harry rry A. A. Pappo, Pappo, 1998 1998,, “Simu “Simulat lation ions s for Skill Skills s Train Training ing”, ”, Educational Technology Publications 84
Thank you Contact information
Dr.C.S.P. Rao Manufacturing Simulation Lab. Department of Mech. Engg. Regional Engineering College - Warangal – 506 004 E-mail :
[email protected] (or)
[email protected]
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