Decision Science-204 MCQ's

8/19/2019 Decision Science-204 MCQ's

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K. K. Wagh Institute Of Engg. Education & Research, Nashik-3

M.B.A. – I Year (Sem. -II)

Weekly Test II

204-DECISION SCIENCE

[Time ! "rs# [$a%. $arks '#

MC !" mark ea#$%

!(

The a))*ication of OR techni+ues ino*es a))roach

a( Indiidua* ( Team c( /ritica* d( None of the aoe

0( The mathematica* techni+ue for finding the est use of *imited resources of a com)an1 in a ma%imum

manner is kno2n as

a( a*ue ana*1sis ( Inestment ana*1sis c( 4inear )rogramming d( 5ueuing theor1

3( $anagement science and o)erations research oth ino*e

a( 5ua*itatie manageria* ski**s ( 5uantitatie a))roaches to decision making

c( O)erationa* management ski**s d( 6cientific research as o))osed to a))*ications

7( The +uantitatie ana*1sis a))roach re+uires

a( the manager8s )rior e%)erience 2ith a simi*ar )ro*em ( a re*atie*1 uncom)*icated )ro*em

c( mathematica* e%)ressions for the re*ationshi)s d( each of the aoe is true

( Which of the fo**o2ing is not the characteristics of the 499

a( Resources must e *imited ( 9arameters a*ue remains constant during )*anning )eriod

c( On*1 one o:ectie function d( The )ro*em must e of minimi;ation t1)e

<( 4et = e the numer of units to make and > e the numer of units to u1. If its cost is Rs. 3 to make

a unit and Rs. 7 to u1 a unit and 3''' units are needed, the o:ectie function is

a( $a% ?3=@7>( ( $in 3''' ?3=@7>( c( $a% ?A'''=@!0'''>( d( $in ?3=@7>(

B( Cind, if )ossi*e, the minimum a*ue of the o:ectie function 3%-71 su:ect to the constraints-0%@1 D !0, %-1 D 0, % F ' and 1 F '

a( No so*ution ( -G c( G d( '

G( Trans)ortation techni+ues are asica**1 used for

a( Reduce the cost of trans)ortation ( a**ocating the man machine comination

c( Increase the tota* reenue d( To )roide +ua*it1 serice to customers

A( When the )ro*em contains tota* numer of a**ocations not e+ua* to ?m@n-!( the case is considered as

a( Hna*anced ( egenerate c( Ceasi*e d( Ja*anced

!'( Which of the fo**o2ing considered to gie o)timum asic feasi*e so*ution,

a( North 2est corner ( *east cost method c( $ d( $OI

!!( What is )ena*t1 in $

a( difference et2een t2o highest cost ( difference et2een a*ternate cost

c( difference et2een *o2est and second *o2est cost d( none of the gien

!0( The una*anced )ro*em of Trans)ortation is so*ed 2ith the he*) of

a( dumm1 ro2 ( dumm1 co*umn c( a( or ( d( oth a( and (



!3( The occurrence of degenerac1 2hi*e so*ing a trans)ortation )ro*em means that

a( tota* su))*1 tota* demand ( the so*ution otained is not feasi*e

c( fe2 a**ocations ecome negatie d( none of the gien

!7( The ta*e re)resents a so*ution that is

a( Initia* so*ution ( Infeasi*e c( egenerate d( a** of the aoe

!( The ste))ing-stone method re+uires that one or more artificia**1 occu)ied ce**s 2ith a f*o2 of ;ero e

created in the trans)ortation ta*eau 2hen the numer of occu)ied ce**s is fe2er than

a( m @ n - 0 ( m @ n - ! c( m @ n d( m @ n @ !

!<( The assignment )ro*em is a s)ecia* case of the

a( trans)ortation )ro*em ( transshi)ment )ro*em

c( ma%ima* f*o2 )ro*em d( shortest-route )ro*em

!B( O)erations Research techni+ues he*)s to find ..so*ution

a( Ceasi*e ( non-feasi*e c( O)tima* d( non-o)tima*

!G( The est use of *inear )rogramming techni+ue is to find an o)tima* use of

a( $one1 ( $an)o2er c( $achine d( a** of the aoe

!A( Whi*e so*ing an assignment )ro*em, an actiit1 is assigned to a resource through a s+uare 2ith ;ero

o))ortunit1 cost ecause the o:ectie is to

a( $inimi;e tota* cost of assignment ( reduce cost of assignment to ;eroc( reduce cost of that )articu*ar assignment to ;ero d( a** of the aoe

0'( The )ur)ose of dumm1 ro2 or co*umn in an assignment )ro*em is to,

a( Otain a*ance et2een tota* actiities and tota* resources

( )reent a so*ution from ecoming degenerate

c( )roide a means of re)resenting a dumm1 )ro*em d( none of the aoe

0!( $a%imi;ation assignment )ro*em is transformed into minimi;ation )ro*em 1

a( adding each entr1 in a co*umn from the ma%imum a*ue in that co*umn

( sutracting each entr1 in a co*umn from the ma%imum a*ue in that co*umn

c( sutracting each entr1 in a ta*e from the ma%imum a*ue in that co*umn d( an1 of the aoe

00( **ocation of sa*es territor1 to sa*es )erson is LLLLLLLLLLLL )ro*em.

a( $a%imi;ation )ro*em ( $inimi;ation )ro*em c( oth a( and ( d( none of the aoe

03( Ja*ance matri% isLLLLLLLLLL

a( no. of *ines no. of ro2s & co*umns ( no. of ro2s no. of co*umns

c( no. of *ines no. of ro2s d( none of the aoe

07( In ssignment techni+ues "ungarian method is a*so kno2n as



a( C*oodMs Techni+ues ( $arko /hain c( $a%i method d( "ungarianMs Techni+ues

0( If there 2ere n 2orkers and n :os there 2ou*d e

a( n so*utions ( ?n-!( so*utions c( nn so*utions d( n so*utions

0<( Traffic Intensit1 means

a( 9roai*it1 that the serer is us1 ( 9roai*it1 that the serer is Id*e

c( 9roai*it1 that there are more than one customer in the s1stem d( Joth a( and c(

t a certain )etro* )um), eer1 minutes ehic*es arrie. The serice )roider takes 7 minutes to fi**

the )etro*. ns2er the fo**o2ing +uestions 0B to 0A)

0B( 6erice rate is

a( !' ( ! c( 0' d( !0

0G( rria* rate is

a( !' ( ! c( 0' d( !0

0A( Traffic Intensit1 is

a( '.G' ( '.B c( '.A' d( '.B'

3'( Whi*e considering the +ueuing mode*s 2e hae considered . distriution for serice time

a( Norma* ( Jinomia* c( E%)onentia* d( /hi-s+uare

3!( The )henomenon in 2hich customers :oin a *ine ut *eae after some time ecause the1 are too

im)atient to 2ait for their turn is ca**ed

a( Ja*king ( Renege c( ea*king d( erenege

30( . Theor1 is an im)ortant o)erations research techni+ue to ana*1;e the +ueuing ehaior.

a( Waiting *ine ( Net 2ork c( ecision d( 6imu*ation

33( What +ueue disci)*ine is assumed 1 the 2aiting *ine mode*s )resented in the te%took

a( first come first sered ( *ast in first out

c( shorting )rocessing time first d( no disci)*ine is assumed

37( The aoe discussed +ueuing mode*s 2ork effectie*1 on*1 2hen

a( If serice rate is greater than arria* rate ( If serice rate is *ess than arria* rate

c( shorting )rocessing time first d( no disci)*ine is assumed

3( se+uence of transitions eginning in state i and ending in state : is ca**ed as

a( transition from i to : ( transition from : to i c( transition from ' to : d( transition from ' to i

3<( $arko ana*1sis assumes that conditions are oth

a( com)*ementar1 and co**ectie*1 e%haustie ( co**ectie*1 de)endent and com)*ementar1

c( co**ectie*1 de)endent and mutua**1 e%c*usie d( co**ectie*1 e%haustie and mutua**1 e%c*usie

3B( In a transition )roai*it1 matri%

a( The sum of each co*umn is ! ( The sum of each ro2 is !

c( The sum of each co*umn is ' d( The sum of each ro2 is '

3G( In a market 2ith three rands 2ith e+ua* shares the initia* condition can e sho2n 1

a( [!P3 !P3 !P3# ( ['.0 '.0 '.0# c( [a c# d( ['. '.0 '.0#

3A( In $arko ana*1sis, 2e are concerned 2ith the )roai*it1 that the



a( state is )art of a s1stem ( s1stem is in a )articu*ar state at a gien time

c( time has reached a stead1 state d( transition 2i** occur

7'( na*1sis of a $arko )rocess

a( descries future ehaior of the s1stem ( o)timi;es the s1stem

c( *eads to higher order decision making d( ** of the a*ternaties are true

7!( The in)uts re+uired for $arko )rocess inc*ude

a( transition )roai*ities ( Initia* condition of the market c( oth a( and ( d( none of these

70( simu*ation mode* uses the mathematica* e%)ressions and *ogica* re*ationshi)s of the

a( rea* s1stem ( com)uter mode* c( )erformance measures d( estimated inferences

73( random numer generator )roides numers those are

a( In order*1 fashion ( In a decreasing se+uence c( In a increasing se+uence

d( 6e*ected 1 giing e+ua* chance to a** the aai*a*e range of numers

77( 6imu*ation is a

a( escri)tie techni+ue ( O)timi;ing techni+ue c( oth a( and ( d( none of these

7( s simu*ation is not an ana*1tica* mode* therefore resu*t of simu*ation must e ie2ed as

a( Hnrea*istic ( E%act c( ))ro%imation d( 6im)*ified

7<( /umu*atie )roai*ities are found 1

a( summing a** the )roai*ities associated 2ith a aria*e

( simu*ating the initia* )roai*it1 distriution c( an1 method one chooses

d( summing a** the )reious )roai*ities u) to the current a*ue of the aria*e

7B( Which of the fo**o2ing statements is IN/ORRE/T regarding the adantages of simu*ation

a( 6imu*ation is re*atie*1 eas1 to e%)*ain and understand

( 6imu*ation guarantees an o)tima* so*ution c( 6imu*ation mode*s are f*e%i*e

d( simu*ation mode* )roides a conenient e%)erimenta* *aorator1 for the rea* s1stem

7G( a*ues for the )roai*istic in)uts to a simu*ation

a( are ca*cu*ated 1 fi%ed mathematica* formu*as ( are contro**ed 1 the decision maker

c( are random*1 generated ased on historica* information d( are se*ected 1 the decision maker

7A( In $arko ana*1sis, the *ike*ihood that an1 s1stem 2i** change from one )eriod to the ne%t is reea*ed

1 the

a( identit1 matri% ( transition-e*asticit1

c( matri% of state )roai*ities d( matri% of transition )roai*ities

'( $arko ana*1sis is a techni+ue that dea*s 2ith the )roai*ities of future occurrences 1

a( using Ja1es8 theorem ( ana*1;ing )resent*1 kno2n )roai*ities

c( time series forecasting d( the ma%ima* f*o2 techni+ue



! & 0 # 3 & 7 # '

< ' B a G a A & !' '!! # !0 # !3 & !7 # ! &!< a !B # !G ' !A a 0' a

0! # 00 a 03 & 07 a 0 a0< ' 0B & 0G ' 0A a 3' #3! & 30 a 33 a 37 a 3 a

3< ' 3B & 3G a 3A & 7' a7! # 70 a 73 ' 77 a 7 #

7< ' 7B & 7G # 7A ' ' &

Asers *-

Decision Science-204 MCQ's

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