Bahan Ajar Minggu 1 Simsis | Simulation

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  Bahan Ajar Minggu Ke-1 Simulasi Sistem Bahan Ajar Minggu Ke-1 Tujuan Instruksional UmumSetelah menyelesaikan mata kuliah ini mahasiswa semester 6 mampu menganalisis output simulasi dengan Promodel dari sistem nyata.Tujuan Instruksional KhususMenjelaskan pengertian sistem, model, dan simulasi. Introduction (1/2) 1.Systems And Models Modeling is the enterprise of deising a simplified representation of a!omple system with the goal of proiding predi!tions of the system#s performan!e measures $metri!s% of interest. Su!h a simplified representation is!alled a model. & model is designed to !apture !ertain 'ehaioral aspe!ts of the modeled system(those that are of interest to the analyst)modeler(inorder to gain knowledge and insight into the system#s 'ehaior $Morris *+6%.Modeling !alls for a'stra!tion and simplifi!ation. In fa!t, if eery fa!et of thesystem under study were to 'e reprodu!ed in minute detail, then the model!ost may approa!h that of the modeled system, there'y militating against!reating a model in the first pla!e.The modeler would simply use the -real system or 'uild an e perimental oneif it does not yet e ist(an e pensie and tedious proposition. Models aretypi!ally 'uilt pre!isely to aoid this unpalata'le option. More spe!ifi!ally,while modeling is ultimately motiated 'y e!onomi! !onsiderations, seeralmotiational strands may 'e dis!erned/ ã 0aluating system performan!e under ordinary and unusual s!enarios. &model may 'e a ne!essity if the routine operation of the real1life systemunder study !annot 'e disrupted without seere !onse2uen!es $e.g.,attempting an upgrade of a produ!tion line in the midst of filling !ustomer orders with tight deadlines%. In other !ases, the e treme s!enario modeled 1  Bahan Ajar Minggu Ke-1 Simulasi Sistem is to 'e aoided at all !osts $e.g., think of modeling a !rash1aoidingmaneuer of manned air!raft, or !ore meltdown in a nu!lear rea!tor%. ã Predi!ting the performan!e of e perimental system designs. 3hen theunderlying system does not yet e ist, model !onstru!tion $andmanipulation% is far !heaper $and safer% than 'uilding the real1life systemor een its prototype. 4orror stories appear periodi!ally in the media on proje!ts that were rushed to the implementation phase, without proper erifi!ation that their design is ade2uate, only to dis!oer that the systemwas flawed to one degree or another $re!all the !ase of the 'rand newairport with faulty luggage transport%. ã 5anking multiple designs and analying their tradeoffs. This !ase is relatedto the preious one, e !ept that the e!onomi! motiation is een greater. Itoften arises when the re2uisition of an e pensie system $with detailedspe!ifi!ations% is awarded to the 'idder with the 'est !ost7'enefit metri!s.Models !an assume a ariety of forms/ ã & physi!al model is a simplified or s!aled1down physi!al o'je!t $e.g.,s!ale model of an airplane%. ã & mathemati!al or analyti!al model is a set of e2uations or relationsamong mathemati!al aria'les $e.g., a set of e2uations des!ri'ing theworkflow on a fa!tory floor%. ã & !omputer model is just a program des!ription of the system. &!omputer model with random elements and an underlying timeline is!alled a Monte 8arlo simulation model $e.g., the operation of amanufa!turing pro!ess oer a period of time%. 2.Analytical ersus Simulation Modeling & simulation model is implemented in a !omputer program. It is generally arelatiely ine pensie modeling approa!h, !ommonly used as an alternatie toanalyti!al modeling. The tradeoff 'etween analyti!al and simulation modelinglies in the nature of their -solutions, that is, the !omputation of their  performan!e measures as follows/*.&n analyti!al model !alls for the solution of a mathemati!al pro'lem, andthe deriation of mathemati!al formulas, or more generally, algorithmi! 2  Bahan Ajar Minggu Ke-1 Simulasi Sistem  pro!edures. The solution is then used to o'tain performan!e measures of interest.9.& simulation model !alls for running $e e!uting% a simulation program to produ!e sample histories. & set of statisti!s !omputed from these historiesis then used to form performan!e measures of interest.To !ompare and !ontrast 'oth approa!hes, suppose that a produ!tion line is!on!eptually modeled as a 2ueuing system. The analyti!al approa!h would!reate an analyti!al 2ueuing system $represented 'y a set of e2uations% and pro!eed to sole them. The simulation approa!h would !reate a !omputer representation of the 2ueuing system and run it to produ!e a suffi!ient num'er of sample histories. Performan!e measures, su!h as aerage work in thesystem, distri'ution of waiting times, and so on, would 'e !onstru!ted fromthe !orresponding -solutions as mathemati!al or simulation statisti!s,respe!tiely.The !hoi!e of an analyti!al approa!h ersus simulation is goerned 'y generaltradeoffs. :or instan!e, an analyti!al model is prefera'le to a simulation modelwhen it has a solution, sin!e its !omputation is normally mu!h faster than thatof its simulation1model !ounterpart. Unfortunately, !omple systems rarelylend themseles to modeling ia suffi!iently detailed analyti!al models.;!!asionally, though rarely, the numeri!al !omputation of an analyti!alsolution is a!tually slower than a !orresponding simulation. In the majority of !ases, an analyti!al model with a tra!ta'le solution is unknown, and themodeler resorts to simulation.3hen the underlying system is !omple , a simulation model is normally prefera'le, for seeral reasons. :irst, in the unlikely eent that an analyti!almodel !an 'e found, the modeler#s time spent in deriing a solution may 'ee !essie. Se!ond, the modeler may judge that an attempt at an analyti!alsolution is a poor 'et, due to the apparent mathemati!al diffi!ulties. :inally,the modeler may not een 'e a'le to formulate an analyti!al model withsuffi!ient power to !apture the system#s 'ehaioral aspe!ts of interest. In!ontrast, simulation modeling !an !apture irtually any system, su'je!t to any 3  Bahan Ajar Minggu Ke-1 Simulasi Sistem set of assumptions. It also enjoys the adantage of dispensing with the la'or attendant to finding analyti!al solutions, sin!e the modeler merely needs to!onstru!t and run a simulation program. ;!!asionally, howeer, the effortinoled in !onstru!ting an ela'orate simulation model is prohi'itie in termsof human effort, or running the resultant program is prohi'itie in terms of !omputer resour!es $8PU time and memory%. In su!h !ases, the modeler mustsettle for a simpler simulation model, or een an inferior analyti!al model.&nother way to !ontrast analyti!al and simulation models is ia the!lassifi!ation of models into des!riptie or pres!riptie models. <es!riptiemodels produ!e estimates for a set of performan!e measures !orresponding toa spe!ifi! set of input data. Simulation models are !learly des!riptie and inthis sense sere as performan!e analysis models. Pres!riptie models arenaturally geared toward design or optimiation $seeking the optimal argumentalues of a pres!ri'ed o'je!tie fun!tion, su'je!t to a set of !onstraints%.&nalyti!al models are pres!riptie, whereas simulation is not. Morespe!ifi!ally, analyti!al methods !an sere as effe!tie optimiation tools,whereas simulation1'ased optimiation usually !alls for an e haustie sear!hfor the optimum.;erall, the ersatility of simulation models and the feasi'ility of their solutions far outstrip those of analyti!al models. This a'ility to sere as an initro la', in whi!h !ompeting system designs may 'e !ompared and !ontrastedand e treme1s!enario performan!e may 'e safely ealuated, renderssimulation modeling a highly pra!ti!al tool that is widely employed 'yengineers in a 'road range of appli!ation areas. In parti!ular, the !omple ity of industrial and seri!e systems often for!es the issue of sele!ting simulation asthe modeling methodology of !hoi!e. !.Simulation Modeling And Analysis 4
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