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IntroctocManagementcScience:cModelingcandcCasecStudies,c6ec(Hillier)c
Chapterc2 LinearcProgramming:cBasiccConcepts
1) Linearcprogrammingcproblemscmaychavecmultiplecgoalscorcobjectivescspecified.
2) Linearcprogrammingcallowscacmanagerctocfindcthecbestcmixcofcactivitiesctocpursuecandcatcwhatcle
vels.
3) Linearcprogrammingcproblemscalwayscinvolveceithercmaximizingcorcminimizingcancobjectivecf
unction.
4) Allclinearcprogrammingcmodelschavecancobjectivecfunctioncandcatcleastctwocconstraints.
5) Constraintsclimitcthecalternativescavailablectocacdecisioncmaker.
6) Whencformulatingcaclinearcprogrammingcproblemconcacspreadsheet,cthecdataccellscwillcshowcthecop
timalcsolution.
7) Whencformulatingcaclinearcprogrammingcproblemconcacspreadsheet,cobjectiveccellscwillcshowcthecle
velscofcactivitiescforcthecdecisionscbeingcmade.
8) Whencformulatingcaclinearcprogrammingcproblemconcacspreadsheet,cthecExcelcequationcforceachco
utputccellccanctypicallycbecexpressedcascacSUMPRODUCTcfunction.
9) Onecofcthecgreatcstrengthscofcspreadsheetscisctheircflexibilitycforcdealingcwithcacwidecvarietycofcpr
oblems.
10) Linearcprogrammingcproblemsccancbecformulatedcbothcalgebraicallycandconcspreadsheets.
11) Thecparameterscofcacmodelcarecthecnumberscincthecdataccellscofcacspreadsheet.
12) Ancexamplecofcacdecisioncvariablecincaclinearcprogrammingcproblemciscprofitcmaximization.
13) Acfeasiblecsolutioncisconecthatcsatisfiescallcthecconstraintscofcaclinearcprogrammingcproblemcsi
multaneously.
14) Ancinfeasiblecsolutioncviolatescallcofcthecconstraintscofcthecproblem.
15) Thecbestcfeasiblecsolutioncisccalledcthecoptimalcsolution.
16) Sincecallclinearcprogrammingcmodelscmustccontaincnonnegativitycconstraints,cSolvercwillca
utomaticallycincludecthemcandcitciscnotcnecessaryctocaddcthemctocacformulation.
17) Theclinecformingcthecboundarycofcwhatciscpermittedcbycacconstraintciscreferredctocascacpa
rameter.
18) Thecorigincsatisfiescanycconstraintcwithcac≥csigncandcacpositivecright-handcside.
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19) Thecfeasiblecregionconlyccontainscpointscthatcsatisfycallcconstraints.
20) Accirclecwouldcbecancexamplecofcacfeasiblecregioncforcaclinearcprogrammingcproblem.
21) Thecequationc5xc+c7yc=c10cisclinear.
22) Thecequationc3xyc=c9cisclinear.
23) Thecgraphicalcmethodccanchandlecproblemscthatcinvolvecanycnumbercofcdecisioncvariables.
24) Ancobjectivecfunctioncrepresentscacfamilycofcparallelclines.
25) Whencsolvingclinearcprogrammingcproblemscgraphically,ctherecarecancinfinitecnumbercofcp
ossiblecobjectivecfunctionclines.
26) Forcacgraphcwherecthechorizontalcaxiscrepresentscthecvariablecxcandcthecverticalcaxiscrepresentscth
ecvariablecy,cthecslopecofcaclineciscthecchangecincycwhencxciscincreasedcbyc1.
27) Thecvaluecofcthecobjectivecfunctioncdecreasescascthecobjectivecfunctionclineciscmovedcawaycfr
omcthecorigin.
28) Acfeasiblecpointconcthecoptimalcobjectivecfunctionclineciscancoptimalcsolution.
29) Aclinearcprogrammingcproblemccanchavecmultiplecoptimalcsolutions.
30) Allcconstraintscincaclinearcprogrammingcproblemcareceitherc≤corc≥cinequalities.
31) Linearcprogrammingcmodelsccanchaveceitherc≤corc≥cinequalitycconstraintscbutcnotcbothcincthe
samecproblem.
32) Acmaximizationcproblemccancgenerallycbeccharacterizedcbychavingcallc≥cconstraints.
33) Ifcacsinglecoptimalcsolutioncexistscwhilecusingcthecgraphicalcmethodctocsolvecaclinearcpr
ogrammingcproblem,citcwillcexistcatcaccornercpoint.
34) Whencsolvingcacmaximizationcproblemcgraphically,citciscgenerallycthecgoalctocmovectheco
bjectivecfunctionclinecout,cawaycfromcthecorigin,cascfarcascpossible.
35) Whencsolvingcacminimizationcproblemcgraphically,citciscgenerallycthecgoalctocmovectheco
bjectivecfunctionclinecout,cawaycfromcthecorigin,cascfarcascpossible.
36) Acmanagercshouldcknowcthecfollowingcthingscaboutclinearcprogramming.
A) Whatcitcis.
B) Whencitcshouldcbecused.
C) Whencitcshouldcnotcbecused.
D) Howctocinterpretcthecresultscofcacstudy.
E) Allcofcthecanswercchoicescareccorrect.
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37) Whichcofcthecfollowingciscnotcaccomponentcofcaclinearcprogrammingcmodel?
A) constraints
B) decisioncvariables
C) parameters
D) ancobjective
E) acspreadsheet
38) Inclinearcprogramming,csolutionscthatcsatisfycallcofcthecconstraintscsimultaneouslycarecreferredcto
cas:
A) optimal.
B) feasible.
C) nonnegative.
D) targeted.
E) Allcofcthecanswercchoicescareccorrect.
39) Whencformulatingcaclinearcprogrammingcproblemconcacspreadsheet,cwhichcofcthecfollowingcisctr
ue?
A) Parameterscareccalledcdataccells.
B) Decisioncvariablescareccalledcchangingccells.
C) Nonnegativitycconstraintscmustcbecincluded.
D) Thecobjectivecfunctioncisccalledcthecobjectiveccell.
E) Allcofcthecanswercchoicescareccorrect.c
40)
Wherecarecthecdataccellsclocated?
A) B2:C2
B) B2:C2,cB5:C7,candcF5:F7
C)cB10:C10
D) F10
E) Nonecofcthecanswercchoicescareccorrect.
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41)
Wherecarecthecchangingccellsclocated?
A) B2:C2
B) B2:C2,cB5:C7,candcF5:F7
C)cB10:C10
D) F10
E) Nonecofcthecanswercchoicescareccorrect.c
42)
Whereciscthecobjectiveccellclocated?
A) B2:C2
B) B2:C2,cB5:C7,candcF5:F7
C)cB10:C10
D) F10
E) Nonecofcthecanswercchoicescareccorrect.
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c c c c c c c c c c
on Hillier c
IntroctocManagementcScience:cModelingcandcCasecStudies,c6ec(Hillier)c
Chapterc2 LinearcProgramming:cBasiccConcepts
1) Linearcprogrammingcproblemscmaychavecmultiplecgoalscorcobjectivescspecified.
2) Linearcprogrammingcallowscacmanagerctocfindcthecbestcmixcofcactivitiesctocpursuecandcatcwhatcle
vels.
3) Linearcprogrammingcproblemscalwayscinvolveceithercmaximizingcorcminimizingcancobjectivecf
unction.
4) Allclinearcprogrammingcmodelschavecancobjectivecfunctioncandcatcleastctwocconstraints.
5) Constraintsclimitcthecalternativescavailablectocacdecisioncmaker.
6) Whencformulatingcaclinearcprogrammingcproblemconcacspreadsheet,cthecdataccellscwillcshowcthecop
timalcsolution.
7) Whencformulatingcaclinearcprogrammingcproblemconcacspreadsheet,cobjectiveccellscwillcshowcthecle
velscofcactivitiescforcthecdecisionscbeingcmade.
8) Whencformulatingcaclinearcprogrammingcproblemconcacspreadsheet,cthecExcelcequationcforceachco
utputccellccanctypicallycbecexpressedcascacSUMPRODUCTcfunction.
9) Onecofcthecgreatcstrengthscofcspreadsheetscisctheircflexibilitycforcdealingcwithcacwidecvarietycofcpr
oblems.
10) Linearcprogrammingcproblemsccancbecformulatedcbothcalgebraicallycandconcspreadsheets.
11) Thecparameterscofcacmodelcarecthecnumberscincthecdataccellscofcacspreadsheet.
12) Ancexamplecofcacdecisioncvariablecincaclinearcprogrammingcproblemciscprofitcmaximization.
13) Acfeasiblecsolutioncisconecthatcsatisfiescallcthecconstraintscofcaclinearcprogrammingcproblemcsi
multaneously.
14) Ancinfeasiblecsolutioncviolatescallcofcthecconstraintscofcthecproblem.
15) Thecbestcfeasiblecsolutioncisccalledcthecoptimalcsolution.
16) Sincecallclinearcprogrammingcmodelscmustccontaincnonnegativitycconstraints,cSolvercwillca
utomaticallycincludecthemcandcitciscnotcnecessaryctocaddcthemctocacformulation.
17) Theclinecformingcthecboundarycofcwhatciscpermittedcbycacconstraintciscreferredctocascacpa
rameter.
18) Thecorigincsatisfiescanycconstraintcwithcac≥csigncandcacpositivecright-handcside.
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19) Thecfeasiblecregionconlyccontainscpointscthatcsatisfycallcconstraints.
20) Accirclecwouldcbecancexamplecofcacfeasiblecregioncforcaclinearcprogrammingcproblem.
21) Thecequationc5xc+c7yc=c10cisclinear.
22) Thecequationc3xyc=c9cisclinear.
23) Thecgraphicalcmethodccanchandlecproblemscthatcinvolvecanycnumbercofcdecisioncvariables.
24) Ancobjectivecfunctioncrepresentscacfamilycofcparallelclines.
25) Whencsolvingclinearcprogrammingcproblemscgraphically,ctherecarecancinfinitecnumbercofcp
ossiblecobjectivecfunctionclines.
26) Forcacgraphcwherecthechorizontalcaxiscrepresentscthecvariablecxcandcthecverticalcaxiscrepresentscth
ecvariablecy,cthecslopecofcaclineciscthecchangecincycwhencxciscincreasedcbyc1.
27) Thecvaluecofcthecobjectivecfunctioncdecreasescascthecobjectivecfunctionclineciscmovedcawaycfr
omcthecorigin.
28) Acfeasiblecpointconcthecoptimalcobjectivecfunctionclineciscancoptimalcsolution.
29) Aclinearcprogrammingcproblemccanchavecmultiplecoptimalcsolutions.
30) Allcconstraintscincaclinearcprogrammingcproblemcareceitherc≤corc≥cinequalities.
31) Linearcprogrammingcmodelsccanchaveceitherc≤corc≥cinequalitycconstraintscbutcnotcbothcincthe
samecproblem.
32) Acmaximizationcproblemccancgenerallycbeccharacterizedcbychavingcallc≥cconstraints.
33) Ifcacsinglecoptimalcsolutioncexistscwhilecusingcthecgraphicalcmethodctocsolvecaclinearcpr
ogrammingcproblem,citcwillcexistcatcaccornercpoint.
34) Whencsolvingcacmaximizationcproblemcgraphically,citciscgenerallycthecgoalctocmovectheco
bjectivecfunctionclinecout,cawaycfromcthecorigin,cascfarcascpossible.
35) Whencsolvingcacminimizationcproblemcgraphically,citciscgenerallycthecgoalctocmovectheco
bjectivecfunctionclinecout,cawaycfromcthecorigin,cascfarcascpossible.
36) Acmanagercshouldcknowcthecfollowingcthingscaboutclinearcprogramming.
A) Whatcitcis.
B) Whencitcshouldcbecused.
C) Whencitcshouldcnotcbecused.
D) Howctocinterpretcthecresultscofcacstudy.
E) Allcofcthecanswercchoicescareccorrect.
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37) Whichcofcthecfollowingciscnotcaccomponentcofcaclinearcprogrammingcmodel?
A) constraints
B) decisioncvariables
C) parameters
D) ancobjective
E) acspreadsheet
38) Inclinearcprogramming,csolutionscthatcsatisfycallcofcthecconstraintscsimultaneouslycarecreferredcto
cas:
A) optimal.
B) feasible.
C) nonnegative.
D) targeted.
E) Allcofcthecanswercchoicescareccorrect.
39) Whencformulatingcaclinearcprogrammingcproblemconcacspreadsheet,cwhichcofcthecfollowingcisctr
ue?
A) Parameterscareccalledcdataccells.
B) Decisioncvariablescareccalledcchangingccells.
C) Nonnegativitycconstraintscmustcbecincluded.
D) Thecobjectivecfunctioncisccalledcthecobjectiveccell.
E) Allcofcthecanswercchoicescareccorrect.c
40)
Wherecarecthecdataccellsclocated?
A) B2:C2
B) B2:C2,cB5:C7,candcF5:F7
C)cB10:C10
D) F10
E) Nonecofcthecanswercchoicescareccorrect.
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41)
Wherecarecthecchangingccellsclocated?
A) B2:C2
B) B2:C2,cB5:C7,candcF5:F7
C)cB10:C10
D) F10
E) Nonecofcthecanswercchoicescareccorrect.c
42)
Whereciscthecobjectiveccellclocated?
A) B2:C2
B) B2:C2,cB5:C7,candcF5:F7
C)cB10:C10
D) F10
E) Nonecofcthecanswercchoicescareccorrect.
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