Q. No. O.

No

QNO.
|ae-Hrvahesnu
302 -
YUce
Decisio SGene.
cdars SMeA

t).te short noles

) Tm povtane Deision
deision
Siente 2 9ole
tgaantitahe
techngue io nating

Importane Deciion Sientes :

a) Holistic De istor - TMaking
Decision Sdences înuolue th
înkgrafoo Uantous diaiplines includng mathearae,
Ostatbtics
economics,
and opexatfon, geseaNch,
-to
Support Compxehensîve dectston. making

b) Data -
DscueoDecision biq data,
Mabn
Decisfon Sences emphasi the Use data and
anaytics to akeiofornedand eviece
mak based
Seistons, to beller outcomes
leadiog

optiníz at on, Resoces

Deisior Sdences
help orgaiates,
opimiaa 90S0sces allocati budget and
fhecanty
enhance oUexal ograti onal tfeakienss
 Q. Q. No
No.
(2

r
Q.No.
139/6

a)Risk Managem ent 3

Deis?oo Siencesplag Gucial
Plag
and ishs

by prooting toos and modute to evalut

Cho

pokntial oakcones and unedaioiesn

Shakgfc plannig

postdng fnsigbts into mathet

pannig
ound
b
O compeitoy añatyts and long
texro
Korecastirg aiding o7gans aions
ahoR in

Role d Quanlitafue
Quantitatiue Techniges
io Deci sion

Mangt
iDala Analysis 5
CQuantitaiue technizucsSuch
8tefis ical sis, alloo
analy sis orfanisation
dataet
to analys

n)opfininalion
Maduls : melha, GE
Quanttatie
help optimiee
Sneax Dgramming,
9sowet
chains ioti ca,
Schedules,
to Cos

impzooeal
 Q. No. No
Q.
(3)

iii) Decisten Trees

Ta and Simulaion

assis oio Gmlating Uaious

Jhese
Scenaiousan
techriqus

their
as«ceng. Upoknttad outonss

Mocals
Forecasng
ime Serdes analysis and
models enable oTqaniSaions
tranel dernand, and
mdehel CondiRorns proate cecisen.
blaing
making

finand al Decisiorn Making

Quantitatie techntqucs
a

io kinantalede dslon mahing

Ioteqral
includinq udgeing sb assess ment
Cagitad
and
poielio manaqend

Contol:
S4attstcal Melhods Contibute to

Control
processes enswing thadpveduts
qualby Sewices Oglandards and
meel
Seiad
Custo mer
capeclakong.

vn) Sufply chain man genend

Quantlaliue technigues
tech

e3sentia chaio operatons

opiriing Suppy
nundon rmenagumend
 Q. No
Q. No.

QNo.

Pemante measwament - metics and

V

Quantaue
(kPI) proodea
indicadors
emnte
Oméas rable basis le eualuatng

Sysem -
Deciston Suppor S4s
Quantitatue techogucs

Suppord Syskem, afding

Cenbeddec in decsfon
Stetions
managers Comgex
anayatg

Prget ManagenendtQuantitalfve toos helas in

and SusowCe
3dedabog
peojeel plani
tocaion

2 Iransportation:
nppL
Deinon 3 fo the
Transporlalion

gooda people iafumetin

mouererd 'anothor t a
pesm one plae
to

d (ogisics and
cuit cal élement
hain

Sevoad model franspor

7heee
widely ued 1ogotcs
tationhal
and oporation
rescarch.
 Q. No. Q. No.

No.

Models d
ranap-lalon
)Transshipmen! Model:

B Inuolus ntemediat ponts, oT traneshpmend

neds. oheu good Stored or
gpoda
Aransgred dutng traraporlat

lseke in Scenassos oheru goo needs to

be Consolidalrdy deconsoltidatad on transd betuen

cGexerd mocdy d txanspordetion

Assiqnment model .
ios Inuolues odeediat base d on 4he mt
Cost - ectue
dstance
dent assirments
Cost.
Consdoing
fadors Gka
Appli cctton
Dpplied i situcions wheu assigning
asigntg
task
12Sowrces (eq o or kers, rmachine) to
is

Dekicle Roufing Proben lURP):

Deiaitton
focuses Aodtng the opfimal ods or a
oehiceto Seteje O Sel d locakon,
tota dc-tanes traeled or
mônimiag transporaion
(osts
 Q. No.
a. No.

ONo.

in lnea
fxplain

Ars -
Dekoidfon
hinear Sa mathemscl
Pgrning to ind he
opimiaadton echnqedesigred
(mamun minimum ) in
best outome
madhemat ad modl f4b

selaionsh

be
Comporerts

Deision Vaiables

delomined
.
hineax progmming!

Represert the
optmieel
quanities do

20 functon
Obieckue s
Del?oes he goal, wohethor
tosts
minimiz'ng

3.
expressed
de'usioo

Constrants

decisior
. Uaables

Restichon

Uaršablas
?

tycali
o maton s
epresented

Gneax îreaualttes
 Q. No. Q. No.

No.

Contepls a.
Fey

1neafltty
Bolh the objecltue functon and Con stratnt
mus! be linen meanîng each asfabu to raled
to th pouoen o nd ts nod multtpled oY
dot ded other Uaiab.
by
2. feasible Re
he el al possibe Soutons
Hhal Constats
DJ

oph mizaton ?
Jhe qoal to toind the Ualues
de ist ons atable, U tha! ehe mazimiza
mini miA
fnchon
4:Bpplí caions :
Rinean roqrmning nds applahon în
Uatow nanee marulacošg
trarsporddion and Aesxu allocatoo

Pduantaqs

prouides a Syskrmate and ehiciend

melhad Jallocaton and cecisPon -
mahng
 Q. No. Q. No.

9.5.
QNo.

)No

Applfcabl to a w?de hange

elaltbnshi s
world poblems
probems otth (inear

Q3 Explain in bie CPM QQ PERT

Ans : Coilfcal path Melho (ee)

CceM):

Detnfon
prefect managenent
echni
CPM
to and sthedule Jaciues ind
Use plan
the coRcal path.

:
projecl. 34 identes

Components
Lhe

Tasbs fobs had need o be acor

dwtng the

Nocus cuhen acioes

LoT poinds io
Represendpoin
n

3)Duralion - each atoi

to Compleke
The 4ime uquired
(rHfal path ?
The Ahrrough
th nekeorh

longesi
patt
atfng he Ushorded Hime to
baindi
 ONo. QNo.

Q. No.

2) pro gram fualuclon and Reotew Techaique

(PERT) :9
Delioen
PERT
P£AT is another projecl managrrerd

tool hat the

dyense and berospace

key Componerds t

Acfoitesu
imilex to CPM,ashs tha neel

to be compleked
2: Nodes

Repsuend potnts in time cohere actii

3 Jione esimatu
eptfmistte, masl Gkely, and pestmthc
4mes estimatu kor each actoty
Crittcal path
Kike cPM, PERT identiltes fhe
Cuitcal path too de
delomne the
minirnun ime.
Competion
 Q.No.

O.No
LNo
5.

PERT ie panlfalany wt!

untidaiain
and wheu
to ine

Decisfon
Eoplain

Defnitton
is
De tsion thaory a ysemahc
and mathe rmatical Bpe ach to

3itudtion
making dacistons in
Cuncestnty or rish.

hey Components 1

Decis?on Maker;
roThejndiodual or

enthy esponsbie fr manny a

lthi deeis ion

Ja 2 De cisior 9leinattve

Diloxand
aetion chofces auailabu to
4he decison mahor.
 Q.No. QNo

7. 5.
Q. No.

3) lales Natures :
.
The vayious po sstbe
everts or outcomes 4hat
outorne that au unertain

and beyond heConrol d the dedsicn

maker

4 Decision (oiteria
Rles o medh wed to
hevala and
and choose
choase among deiclon
o alttmate.
among

Types of De cisonr Enuron ments

Centainty

înform atfon is knoton, and

otcomnes predical

3. Sone and

po babi h Cann at be
axignad
accuratay
 No. O.No.

O. No.

No

Deciston - protess &

Makin

Hhe Decior poblem

be made
eleaxly
cleasly deine tho descis lon ;
20
Tdenti
his
Deciston

Possi ble
Ateralius 3
(ouTRS
;
3. Deterroine tales d Nat

Tdenti pokeotad atcomes

Asiah. pobabilies
Io a

4he ikethoo
belhooe each slat d nat

5. Delkotne
valua ha outcomes
ass odatd oith each Com binaion d
decision allernatiues ands stat