,ACCESS Test Bank for Introduction to Management Science and Business Analy
m m m m m m m m m m
tics 7th Edition Hillier
m m m
CHAPTER 1 m
m INTRODUCTIO
N
Review Questionsm
1.1 –1 The mrapidmdevelopmentmofmthe mdiscipline mbeganminmthe m1940’smandm1950’s.
1.1 –2 The mtraditionalmname mgivenmtomthe mdiscipline mismoperationsmresearch.
1.1 –3
Ammanagementmscience mstudymprovidesmanmanalysismandmrecommendations,mba
sedmonmthemquantitativemfactorsminvolvedminmthemproblem,masminputmtomthe mmanagers.
1.1 –4
Managementmscience mismbasedmstronglymonmsomemscientificmfields,mincludingmmathe
maticsmandmcomputermscience.m Itmalsomdrawsmuponmthemsocialmsciences,mespeciallymecono
mics.
1.1 –5
Manymmanagerialmproblemsmrevolvemaroundmsuchmquantitativemfactorsmasmproduction
m quantities,mrevenues,mcosts,mthe mamountsmavailable mof mneededmresources, metc.
1.2 –1
Businessmanalyticsmhasmdrawnmonmvariousmothermquantitativemdecisionmsciences,m
includingmmanagementmscience,mmathematics,mstatistics,mcomputermscience,minformation
mtechnology, m industrialm engineering, m etc.
1.2 –2
The meramofmbigmdatamismwheremmassive mamountsmofmdatam(accompaniedmbymmassive
m amounts m of mcomputationalm power) marem now mcommonly mavailable mtommanym businesses mto
m helpm guide
managerialmdecisionmmaking.mAmprimarymfocusmofmbusinessmanalyticsmismonmhowmtommakemthemmo
stmeffective muse mof mallmthese mdata.
1.2 –
3m Descriptivemanalyticsmusesminnovativemtechniquesm(includingmalgorithms)mtomexploremth
emdata,mlocatemandmextractmthemdatamthatmaremrelevant,mandmthenmidentifymtheminterestingm
patternsmandmsummarymdata.
1.2 –4
Predictive manalyticsmoftenminvolvesmapplyingmstatisticalmmodelsmtompredictmfuturemeventsmormtren
ds.
1.2 –5
Prescriptive manalyticsmusesmpowerfulmtechniquesmdrawnmmainlymfrommmanagement
mscience m tom prescribe m whatm should m be m done m inm the m future.
1.2 –6 Datamscience mtendsmtombe mmore minterdisciplinary, mmore mbasedmonmscientificmmethods,mmore
applicable mtomvariousmareasminmadditionmtombusiness,mandmmoremconcernedmwithmhowmtomdealmwi
thmevenmmassive mamountsmofmdataminmvariousmforms.
mynursytest.store
,ACCESS Test Bank for Introduction to Management Science and Business Analy
m m m m m m m m m m
tics 7th Edition Hillier
m m m
1.2 –7
The mgoalmof mmachinemlearningmismtomallowmcomputersmtomlearnmautomaticallymfrommhist
oricalmrelationshipsmandmtrendsminmthe mdataminmordermtomdomsuchmthingsmasmmakingmdata-
drivenmpredictions.
Machine mlearningmismampopularmmethodmformapplyingmpredictive manalyticsmbymperformingmpattern
m recognition.
1.2 –8
Machine mlearningmismjustmonemimportantmpartmof martificialmintelligence.mBecause mitsm
focusmismonmautomaticallymlearningmfrommhistoricalmrelationshipsmandmtrendsminmthemdata,mm
achine mlearning
providesmanmidealmplatformmformperformingmartificialmintelligence.mThemdifficultmpartmthenmismtaki
ngmthe mnextmstepmtomuse mthismplatformmtomtrulymsimulate mhumanmthinkingmcapabilitymandmbehav
ior.
1–1
©mMcGrawmHillmLLC.mAllmrights mreserved.mNomreproductionmormdistributionmwithoutmthemprior mwrittenmconsentmofmMcGrawmHill mLLC.
mynursytest.store
, ACCESS Test Bank for Introduction to Management Science and Business Analy
m m m m m m m m m m
tics 7th Edition Hillier
m m m
1.3 –1 Businessmanalyticsmprovidesmamlargermtoolkitmwhenmdealingmwithmdescriptive manalytics,mdata
preparation, mandmpredictive manalytics.mManagementmsciencemtechniquesmnormallymtake mthemlead
m when m performingm prescriptive m analytics.
1.3 –2
Analyticsmhave mbeenmmakingmimportantmcontributionsminmsuchmareasmasmsports,mpolitical
m campaigns, m healthcare, m combatingm crime, m personalm financialm analysis, m etc.
1.3 –3 Topmmanagementmnowmunderstandsmverymwellmthe mimpactmthatmbusinessmanalyticsmand
managementmsciencemcanmhavemonmthe mbottommline.mThismwillmcontinue mtomrequire mmanymmorem
people mwhomare mverymwellmtrainedminmbusinessmanalyticsmandmmanagementmscience
1.3 –4
Operationsmresearchmhasmbeenmthemusualmnamemgivenmtommanagementmscience moutside
m ofmbusinessmschools.
1.3 –5 INFORMSm(themInstitutemofmOperationsmResearchmandmthemManagementmSciences) mismthe mlargest
professionalmsocietymofmmanagementmscience mandmbusinessmanalyticsmprofessionalsmormstudentsmi
nmthe mworld.
1.4 –1
Variable mcostsmincludemallmofmthemcostsmthatmaremproportionalmwithmthemnumbermofmunitsmproduc
ed.
1.4 –2
Fixedmcostsmincludemthe mcostsmthatmare mincurredmregardlessmofmhowmmanymunitsmare mpro
duced.mThesemmightmincludemamproratedmshare mofmthemsalariesmformuppermmanagement,mcapitalm
equipment,
propertymtaxes,mandmmore.
1.4 –3
There mare moftenmdiminishingmreturnsmfrommadvertisingmthatmaremnotmcapturedmbymlinearmregressio
n.
1.4 –4
Ifmampolynomialmequationmismusedmtomtrymtompredictmwhatmsalesmwillmbe mformadvertisingmb
udgets,mitmwillmeventuallymstartmslopingmdownward.mWhile mwemintuitivelymwouldmexpectmdiminis
hingmreturnsmasmadvertisingmismincreased, mwe mwouldn’tmexpectmsalesmtomactuallymdecrease.
1.4 –5
Salesmdomnotmincreasemproportionallymwithmthemlevelmofmadvertising,mbutmrat
hermincrease mproportionally mwithmthe msquarem rootm ofmadvertising.
1.4–6
Solvermwillmfindmthe mvalue mofmdecisionmvariable mcellsmthatmwillmoptimizemthemvaluemof
m anm objective m cell.
Problems
1.1 The mthree mmodulesmaremthemIncidentmTicketmClassificationmModulem(whichmlistsmandmclassifiesmt
he mundesirablemincidentsmformthe mvariousmservers),mthemServermClassificationmModule m(whichmli
nksmthe mservermunavailable mincidentsmtomthemserversminvolvedmandmthenmdevelopsmmetricsmformt
mynursytest.store
m m m m m m m m m m
tics 7th Edition Hillier
m m m
CHAPTER 1 m
m INTRODUCTIO
N
Review Questionsm
1.1 –1 The mrapidmdevelopmentmofmthe mdiscipline mbeganminmthe m1940’smandm1950’s.
1.1 –2 The mtraditionalmname mgivenmtomthe mdiscipline mismoperationsmresearch.
1.1 –3
Ammanagementmscience mstudymprovidesmanmanalysismandmrecommendations,mba
sedmonmthemquantitativemfactorsminvolvedminmthemproblem,masminputmtomthe mmanagers.
1.1 –4
Managementmscience mismbasedmstronglymonmsomemscientificmfields,mincludingmmathe
maticsmandmcomputermscience.m Itmalsomdrawsmuponmthemsocialmsciences,mespeciallymecono
mics.
1.1 –5
Manymmanagerialmproblemsmrevolvemaroundmsuchmquantitativemfactorsmasmproduction
m quantities,mrevenues,mcosts,mthe mamountsmavailable mof mneededmresources, metc.
1.2 –1
Businessmanalyticsmhasmdrawnmonmvariousmothermquantitativemdecisionmsciences,m
includingmmanagementmscience,mmathematics,mstatistics,mcomputermscience,minformation
mtechnology, m industrialm engineering, m etc.
1.2 –2
The meramofmbigmdatamismwheremmassive mamountsmofmdatam(accompaniedmbymmassive
m amounts m of mcomputationalm power) marem now mcommonly mavailable mtommanym businesses mto
m helpm guide
managerialmdecisionmmaking.mAmprimarymfocusmofmbusinessmanalyticsmismonmhowmtommakemthemmo
stmeffective muse mof mallmthese mdata.
1.2 –
3m Descriptivemanalyticsmusesminnovativemtechniquesm(includingmalgorithms)mtomexploremth
emdata,mlocatemandmextractmthemdatamthatmaremrelevant,mandmthenmidentifymtheminterestingm
patternsmandmsummarymdata.
1.2 –4
Predictive manalyticsmoftenminvolvesmapplyingmstatisticalmmodelsmtompredictmfuturemeventsmormtren
ds.
1.2 –5
Prescriptive manalyticsmusesmpowerfulmtechniquesmdrawnmmainlymfrommmanagement
mscience m tom prescribe m whatm should m be m done m inm the m future.
1.2 –6 Datamscience mtendsmtombe mmore minterdisciplinary, mmore mbasedmonmscientificmmethods,mmore
applicable mtomvariousmareasminmadditionmtombusiness,mandmmoremconcernedmwithmhowmtomdealmwi
thmevenmmassive mamountsmofmdataminmvariousmforms.
mynursytest.store
,ACCESS Test Bank for Introduction to Management Science and Business Analy
m m m m m m m m m m
tics 7th Edition Hillier
m m m
1.2 –7
The mgoalmof mmachinemlearningmismtomallowmcomputersmtomlearnmautomaticallymfrommhist
oricalmrelationshipsmandmtrendsminmthe mdataminmordermtomdomsuchmthingsmasmmakingmdata-
drivenmpredictions.
Machine mlearningmismampopularmmethodmformapplyingmpredictive manalyticsmbymperformingmpattern
m recognition.
1.2 –8
Machine mlearningmismjustmonemimportantmpartmof martificialmintelligence.mBecause mitsm
focusmismonmautomaticallymlearningmfrommhistoricalmrelationshipsmandmtrendsminmthemdata,mm
achine mlearning
providesmanmidealmplatformmformperformingmartificialmintelligence.mThemdifficultmpartmthenmismtaki
ngmthe mnextmstepmtomuse mthismplatformmtomtrulymsimulate mhumanmthinkingmcapabilitymandmbehav
ior.
1–1
©mMcGrawmHillmLLC.mAllmrights mreserved.mNomreproductionmormdistributionmwithoutmthemprior mwrittenmconsentmofmMcGrawmHill mLLC.
mynursytest.store
, ACCESS Test Bank for Introduction to Management Science and Business Analy
m m m m m m m m m m
tics 7th Edition Hillier
m m m
1.3 –1 Businessmanalyticsmprovidesmamlargermtoolkitmwhenmdealingmwithmdescriptive manalytics,mdata
preparation, mandmpredictive manalytics.mManagementmsciencemtechniquesmnormallymtake mthemlead
m when m performingm prescriptive m analytics.
1.3 –2
Analyticsmhave mbeenmmakingmimportantmcontributionsminmsuchmareasmasmsports,mpolitical
m campaigns, m healthcare, m combatingm crime, m personalm financialm analysis, m etc.
1.3 –3 Topmmanagementmnowmunderstandsmverymwellmthe mimpactmthatmbusinessmanalyticsmand
managementmsciencemcanmhavemonmthe mbottommline.mThismwillmcontinue mtomrequire mmanymmorem
people mwhomare mverymwellmtrainedminmbusinessmanalyticsmandmmanagementmscience
1.3 –4
Operationsmresearchmhasmbeenmthemusualmnamemgivenmtommanagementmscience moutside
m ofmbusinessmschools.
1.3 –5 INFORMSm(themInstitutemofmOperationsmResearchmandmthemManagementmSciences) mismthe mlargest
professionalmsocietymofmmanagementmscience mandmbusinessmanalyticsmprofessionalsmormstudentsmi
nmthe mworld.
1.4 –1
Variable mcostsmincludemallmofmthemcostsmthatmaremproportionalmwithmthemnumbermofmunitsmproduc
ed.
1.4 –2
Fixedmcostsmincludemthe mcostsmthatmare mincurredmregardlessmofmhowmmanymunitsmare mpro
duced.mThesemmightmincludemamproratedmshare mofmthemsalariesmformuppermmanagement,mcapitalm
equipment,
propertymtaxes,mandmmore.
1.4 –3
There mare moftenmdiminishingmreturnsmfrommadvertisingmthatmaremnotmcapturedmbymlinearmregressio
n.
1.4 –4
Ifmampolynomialmequationmismusedmtomtrymtompredictmwhatmsalesmwillmbe mformadvertisingmb
udgets,mitmwillmeventuallymstartmslopingmdownward.mWhile mwemintuitivelymwouldmexpectmdiminis
hingmreturnsmasmadvertisingmismincreased, mwe mwouldn’tmexpectmsalesmtomactuallymdecrease.
1.4 –5
Salesmdomnotmincreasemproportionallymwithmthemlevelmofmadvertising,mbutmrat
hermincrease mproportionally mwithmthe msquarem rootm ofmadvertising.
1.4–6
Solvermwillmfindmthe mvalue mofmdecisionmvariable mcellsmthatmwillmoptimizemthemvaluemof
m anm objective m cell.
Problems
1.1 The mthree mmodulesmaremthemIncidentmTicketmClassificationmModulem(whichmlistsmandmclassifiesmt
he mundesirablemincidentsmformthe mvariousmservers),mthemServermClassificationmModule m(whichmli
nksmthe mservermunavailable mincidentsmtomthemserversminvolvedmandmthenmdevelopsmmetricsmformt
mynursytest.store
