future perspectives of optimization: my vie · 2014. 10. 24. · future perspectives of...
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Proceedings of the Twenty-Sixth RAMP SymposiumHosei University, Tokyo, October 16-17, 2014
Future perspectives of optimization: My view
Martin Grötschel1∗
Abstract When thinking about the future of optimization one has to take a broad perspec-tive; disciplines such as operations research (OR), computer science, applied mathematics, andscientific computing need to be taken into account as well as emerging fields such as data scienceand MSO (which is an abbreviation of modelling, simulation and optimization).
Optimization (and its close relatives mentioned before) are, in a sense, difficult disciplines sincethey are becoming more and more interdisciplinary. And moreover, in the academic world, theyare heterogeneous as they may be located in departments or faculties of mathematics, manage-ment science, economics, computer science, industrial or other types of engineering. In industry,the positioning of OR and optimization specialists is similarly fuzzy. There is almost no situa-tion where OR or optimization form the core of some organization. All this makes it somewhatdifficult to pursue “clean” OR/optimization careers in industry or academia.
What makes it worse is that the names used for denoting the activities are unstable. Theyare changing over time and from country to country. Even worse, the various names are notwell understood ― some not at all by the general public, some have different interpretations ―depending on the user community.
It is interesting to observe that all this is a weakness of the field, but it is also a strength, as Iwill point out. In this talk I will elaborate on my view of the past and future of optimizationand its relatives and my conclusion is that it is very bright, if appropriately “positioned andmanaged”.
Keywords optimization, operations research, mathematical programming, future perspectives
1 Zuse Institute, TU und MATHEON BerlinZuse Institute Berlin, Takustraße 7, 14195 Berlin, Germany
∗ E-mail address: [email protected]
－ 133 －
Futu
re p
ersp
ectiv
es o
f op
timiz
atio
n:
My
view
26th
RAM
P Sy
mpo
sium
Toky
o, J
apan
, Oct
ober
17, 2
014
Mar
tin G
röts
chel
Zuse
Ins
titut
e, T
echn
isch
e U
nive
rsitä
t an
dM
ATH
EON
Berli
n
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
2
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
3
My
abst
ract
Whe
n th
inki
ng a
bout
the
futu
re o
f op
timiz
atio
n on
e ha
s to
tak
e a
broa
d pe
rspe
ctiv
e: d
isci
plin
es s
uch
as
�op
erat
ions
res
earc
h (O
R),
�co
mpu
ter
scie
nce,
�
appl
ied
mat
hem
atic
s, a
nd
�sc
ient
ific
com
putin
g ne
ed t
o be
tak
en in
to a
ccou
nt a
s w
ell a
s em
ergi
ng f
ield
s su
ch a
s �
data
sci
ence
and
�
MSO
(an
abb
revi
atio
n of
mod
ellin
g, s
imul
atio
n an
d op
timiz
atio
n).
Mar
tin G
röts
chel
4
－ 134 －
My
abst
ract
�O
ptim
izat
ion
(and
its
clos
e re
lativ
es m
entio
ned
befo
re)
are,
in
a se
nse,
diff
icul
t di
scip
lines
sin
ce t
hey
are
beco
min
g m
ore
and
mor
e in
terd
isci
plin
ary.
�An
d m
oreo
ver,
in t
he a
cade
mic
wor
ld, t
hey
are
hete
roge
neou
s as
th
ey m
ay b
e lo
cate
d in
dep
artm
ents
or
facu
lties
of
mat
hem
atic
s,
man
agem
ent
scie
nce,
eco
nom
ics,
com
pute
r sc
ienc
e, in
dust
rial o
r ot
her
type
s of
eng
inee
ring.
�
In in
dust
ry, t
he p
ositi
onin
g of
OR
and
optim
izat
ion
spec
ialis
ts is
si
mila
rly fu
zzy.
The
re is
alm
ost
no s
ituat
ion
whe
re O
R or
optim
izat
ion
form
s th
e co
re o
f so
me
orga
niza
tion.
�
All t
his
mak
es it
som
ewha
t di
ffic
ult
to p
ursu
e “c
lean
” O
R/op
timiz
atio
n ca
reer
s in
indu
stry
or
acad
emia
.
Mar
tin G
röts
chel
5
My
abst
ract
�W
hat
mak
es it
wor
se is
tha
t th
e na
mes
use
d fo
r de
notin
g th
e ac
tiviti
es a
re u
nsta
ble.
The
y ar
e ch
angi
ng o
ver
time
and
from
co
untr
y to
cou
ntry
. �
Even
wor
se, t
he v
ario
us n
ames
are
not
wel
l und
erst
ood
–so
me
not
at a
ll by
the
gen
eral
pub
lic, s
ome
have
diff
eren
t in
terp
reta
tions
–
depe
ndin
g on
the
use
r co
mm
unity
. �
It is
inte
rest
ing
to o
bser
ve t
hat
all t
his
is a
wea
knes
s of
the
fie
ld,
but
it is
als
o a
stre
ngth
, as
I w
ill p
oint
out
. �
In t
his
talk
I w
ill e
labo
rate
on
my
view
of
the
past
and
futu
re o
f op
timiz
atio
n an
d its
rel
ativ
es a
nd m
y co
nclu
sion
is t
hat
it is
ver
y br
ight
, if a
ppro
pria
tely
“po
sitio
ned
and
man
aged
”.
Mar
tin G
röts
chel
6
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
7M
artin
Grö
tsch
el8
－ 135 －
Curr
ent
Stru
ctur
e of
ZIB
Mar
tin G
röts
chel
9
ZIB
Mar
tin G
röts
chel
10
Eval
uatio
n of
ZIB
on N
ovem
ber
24-2
5, 2
014
Mar
tin G
röts
chel
11M
artin
Grö
tsch
el12
－ 136 －
MAT
HEO
N
Mar
tin G
röts
chel
13
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
14
Opt
imiz
atio
net
c.Th
e in
itial
nam
ew
as�
Prog
ram
min
gor
�M
athe
mat
ical
prog
ram
min
gRo
ot o
fth
ena
me?
(se
eG
eorg
e D
antz
ig‘s
expl
anat
ion)
In G
erm
an:
�O
ptim
ieru
ngBu
t th
enpr
ogra
mm
ing
was
hig
hjac
ked
byco
mpu
ter
scie
ntis
tsan
dth
ena
me
chan
ged
to�
Opt
imiz
atio
nor
�O
ptim
isat
ion
�M
athe
mat
ical
optim
iz(s
)atio
nJu
st lo
okat
MO
S
Mar
tin G
röts
chel
15
MO
S/M
PS
Mar
tin G
röts
chel
16
－ 137 －
Whe
redo
espr
ogra
mm
ing
com
efr
om?
Mar
tin G
röts
chel
17
Wha
t na
mes
do
we
know
for
OR?
Engl
ish:
Ope
ratio
ns R
esea
rch
(Nor
th A
mer
ica
and
mos
t ot
her
coun
trie
s)O
pera
tiona
l Res
earc
h (U
nite
d Ki
ngdo
m)
OR:
The
sci
ence
of
bett
er (
INFO
RMS)
INFO
RMS
rece
ntly
coi
ned
Anal
ytic
s
Ger
man
:U
nter
nehm
ensf
orsc
hung
(Wes
t G
erm
any)
U
nter
nehm
ungs
fors
chun
g(W
est
Ger
man
y)O
pera
tions
fors
chun
g(E
ast
Ger
man
y)no
w m
ostly
Ope
ratio
ns R
esea
rch
and
man
y m
ore
nam
es e
lsew
here
Mar
tin G
röts
chel
18
ORM
S-To
day:
Aug
ust
2013
, vol
. 40,
No.
4
Mar
tin G
röts
chel
19
How
doe
s “a
naly
tics”
tran
slat
e in
to o
ther
lang
uage
s? D
oes
it ha
ve th
e sa
me
impa
ct in
ot
her c
ultu
res
as is
bei
ng w
itnes
sed
in N
orth
Am
eric
a? In
Ital
ian,
the
liter
al tr
ansl
atio
n is
“ana
lytic
a,” a
love
ly, ly
rical
Ital
ian
wor
d, th
at h
as li
ttle
rele
vanc
e or
mea
ning
in th
e co
ntex
t in
whi
ch w
e de
scrib
e it.
The
Ger
man
O.R
. Soc
iety
has
adop
ted
the
busi
ness
ana
lytic
ste
rm, a
s ev
iden
ce b
y th
eir f
orth
com
ing
conf
eren
ce “B
usin
ess
Ana
lytic
s an
d O
pera
tions
Res
earc
h.” M
arco
Lü
bbec
ke, c
hair
of o
pera
tions
rese
arch
at R
WTH
Aac
hen
Uni
vers
ity in
Aac
hen,
G
erm
any,
wen
t as
far a
s tw
eetin
g th
at “a
naly
tics
sim
ply
is a
goo
d na
me,
no
mat
ter
wha
t.” (s
ee F
igur
e 1.
)M
y Ve
rizon
col
leag
ue a
nd IN
FOR
MS
Pas
t Pre
side
nt R
ina
Sch
neur
men
tione
d th
at a
le
ctur
e sh
e re
cent
ly g
ave
on a
trip
to th
e Te
chni
onin
Isra
el tr
igge
red
a si
mila
r co
nver
satio
n on
this
topi
c. T
he c
oncl
usio
n w
as th
at a
naly
tics
was
a d
iffic
ult c
once
pt to
tra
nsla
te.
Mar
tin G
röts
chel
20
EURO
Gol
d M
edal
win
ners
on
Ope
ratio
nal R
esea
rch
�S.
Vaj
da, (
artic
le o
n E
URO
Gol
d M
edal
Cer
emon
y)“T
he t
hree
age
s of
Ope
ratio
nal R
esea
rch”
,Eu
rope
an J
ourn
al o
f O
pera
tiona
l Res
earc
h 45
(199
0)13
1-13
4“I
bel
ieve
tha
t th
e te
rm f
its a
wkw
ardl
y th
ose
activ
ities
w
hich
OR
com
pris
es n
ow, b
ut it
is t
oo la
te t
o ch
ange
.”�
R. L
. Ack
off,
“The
futu
re o
f O
pera
tiona
l Res
earc
h is
pas
t”Jo
urna
l of
the
Ope
ratio
nal S
ocie
ty 3
0(19
79)9
3-10
4
�J.
Kra
rup,
“EU
RO G
old
Med
al 1
986:
A p
arab
le o
n tw
o-le
vel
para
llelis
m”,
Euro
pean
Jou
rnal
of
Ope
ratio
nal R
esea
rch
38(1
989)
274-
276
“…an
inte
rdis
cipl
inar
y ba
star
d lik
e op
erat
iona
l res
earc
h…”
－ 138 －
Mar
tin G
röts
chel
21
EURO
Gol
d M
edal
win
ners
on
Ope
ratio
nal R
esea
rch
(pos
itive
guy
s)�
P. H
anse
n, “
A sh
ort
disc
ussi
on o
f th
e O
R cr
isis
”Eu
rope
an J
ourn
al o
f O
pera
tiona
l Res
earc
h 38
(198
9)27
7-28
1“N
o ge
nera
l agr
eem
ent
seem
s to
hav
e be
en r
each
ed a
bout
its
met
hodo
logy
, and
the
dire
ctio
ns in
whi
ch it
sho
uld
evol
ve. …
Ther
e ar
e m
any
way
s to
live
a li
fe o
f O
R, t
o di
scov
er n
ew r
esul
ts a
nd
appl
y th
em, a
nd t
hus
to e
njoy
OR’
s tr
uth
and
beau
ty.”
�R.
Bur
kard
, “O
R U
topi
a”
Euro
pean
Jou
rnal
of
Ope
ratio
nal R
esea
rch
119(
1999
)224
-234
“The
bor
ders
of
OR
Uto
pia
have
yet
ano
ther
qua
lity:
peo
ple
can
com
e an
d go
, with
out
pass
port
. The
re is
no
quot
a fo
r fo
reig
ners
…O
R U
topi
a…is
a p
eace
ful b
orde
r be
twee
n O
R, m
athe
mat
ics,
and
co
mpu
ter
scie
nce,
…m
anag
emen
t sc
ienc
e, e
cono
my,
logi
stic
s, …
”
Nam
e co
nsul
tant
sar
ene
eded
Exam
ple:
DFG
-For
schu
ngsz
entr
ums
(FZT
86)
Mat
hem
atik
für
Schl
üsse
ltech
nolo
gien
: M
odel
lieru
ng, S
imul
atio
n un
d O
ptim
ieru
ngre
aler
Pro
zess
eD
FG R
esea
rch
Cent
er (
FZT
86)
Mat
hem
atic
s fo
rke
yte
chno
logi
es:
Mod
ellin
g, s
imul
atio
n, a
ndop
timiz
atio
nof
rea
l-wor
ldpr
oces
ses
beca
me
MAT
HEO
N
The
issu
eis
not
just
goo
dsc
ienc
ebu
t go
odsc
ienc
em
arke
ting.
Mar
tin G
röts
chel
22
MAT
HEO
Nap
plic
atio
n in
200
1
Mar
tin G
röts
chel
23
MATHEON
Term
inol
ogy
(for
thi
s le
ctur
e)
Mor
eove
r, it
has
beco
me
clea
r th
at a
ll th
ree
of t
he fo
llow
ing
scie
ntifi
c ac
tiviti
es
�m
odel
ing
�si
mul
atio
n�
optim
izat
ion
are
nece
ssar
y fo
r th
e so
lutio
n of
rea
l-wor
ld p
robl
ems
and
that
the
y sh
ould
be
cons
ider
ed jo
intly
in a
ll so
lutio
n ap
proa
ches
. Ver
y re
cent
ly,
it ha
s be
com
e fa
shio
nabl
e to
abb
revi
ate
the
com
bine
d ef
fort
s by
�M
SOI
will
follo
w t
his
tren
d (p
artly
) in
my
lect
ure.
In fa
ct, M
athe
on, a
s I
will
exp
lain
late
r w
as o
ne o
f th
e tr
ends
ette
rs in
th
is d
irect
ion,
as
the
next
slid
e sh
ows:
Mar
tin G
röts
chel
24
－ 139 －
New
Dev
elop
men
ts
Mar
tin G
röts
chel
25
MSO
as
a Ke
y En
ablin
gTec
hnol
ogy
(KET
)
Mar
tin G
röts
chel
26
MSO
as
a Fu
ture
and
Emer
ging
Tec
hnol
ogy
(FET
)
Mar
tin G
röts
chel
27
Nam
esO
ur fie
ld is
und
er n
amin
g pr
essu
re!
New
con
cept
s/ab
brev
iatio
ns/n
ames
are
inve
nted
tha
t ar
e in
co
mpe
titio
n w
ith o
ptim
izat
ion
and
oper
atio
ns r
esea
rch,
ad
ditio
nally
con
trib
utin
g to
the
con
fusi
on.
Mar
tin G
röts
chel
28
－ 140 －
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
29M
artin
Grö
tsch
el30
Ope
ratio
ns R
esea
rch/
Opt
imiz
atio
n:ap
proa
ched
as
a su
bjec
tAs
a t
opic
in m
athe
mat
ics
�op
timiz
atio
n�
mat
hem
atic
al p
rogr
amm
ing
As a
top
ic in
man
agem
ent
scie
nce
/ bu
sine
ss a
dmin
istr
atio
n�
oper
atio
ns m
anag
emen
t�
man
agem
ent
engi
neer
ing
As a
top
ic in
eng
inee
ring
�in
dust
rial e
ngin
eerin
g�
supp
ly c
hain
man
agem
ent/
flexi
ble
man
ufac
turin
g
As a
top
ic in
com
pute
r sc
ienc
eAs
a t
opic
in p
sych
olog
y/so
ciol
ogy
Syst
ems
Theo
ry/C
yber
netic
sD
ecis
ion
Scie
nces
, Dec
isio
n Ai
d…
Mar
tin G
röts
chel
31
de W
erra
’s sw
eep
D. d
e W
erra
: “W
hat
is m
y ob
ject
ive
func
tion?
”Eu
rope
an J
ourn
al o
f O
pera
tiona
l Res
earc
h 99
(199
7)20
7-21
9
2. O
R is
a p
ure
scie
nce.
3. O
R is
an
open
sci
ence
.4.
OR
relie
s on
bas
ic s
cien
ces
and
on li
fe s
cien
ces.
5. O
R is
a n
atur
al s
cien
ce.
6. O
R is
an
art.
7. O
R do
es m
iracl
es.
Dom
iniq
ue fo
rgot
: O
R is
an
appl
ied
scie
nce.
Mar
tin G
röts
chel
32
Acad
emic
OR/
Opt
in B
erlin
(one
typ
ical
exa
mpl
e)Te
chni
sche
Uni
vers
ität
Berli
n �
Faku
ltät
IV -
Elek
trot
echn
ikun
d In
form
atik
�In
stitu
tfü
r W
irtsc
haft
sinf
orm
atik
und
Qua
ntita
tive
Met
hode
n�
Ope
ratio
ns R
esea
rch
(OR)
�Fa
kultä
tVI
II W
irtsc
haft
und
Man
agem
ent
�Fa
chge
biet
Prod
uktio
nsm
anag
emen
t
�Fa
kultä
tII
Mat
hem
atik
und
Nat
urw
isse
nsch
afte
n�
Inst
itut
für
Mat
hem
atik
�Ar
beits
grup
peAl
gorit
hmis
che
und
Dis
kret
eM
athe
mat
ik
Frei
eU
nive
rsitä
tBe
rlin
�Fa
chbe
reic
hW
irtsc
haft
swis
sens
chaf
t�
Inst
itut
für
Prod
uktio
n, W
irtsc
haft
sinf
orm
atik
und
OR
Hum
bold
t U
nive
rsitä
tBe
rlin
�W
irtsc
haft
swis
sens
chaf
tlich
eFa
kultä
t�
Inst
itut
für
Ope
ratio
ns R
esea
rch
Plus
sev
eral
wor
king
grou
psin
the
mat
hem
atic
alin
stitu
tes
in v
ario
usbr
anch
esof
mat
hs.
－ 141 －
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
33M
artin
Grö
tsch
el
34 Typi
calo
ptim
izat
ion
prob
lem
s
max
()
min
()
()0,
1,2,...,
()0,
1,2,...,
()
i j
nfxor
fx
gx
ik
hx
jm
xandxS
d
��
�(
)
min
0 nT
x
cx
Axa
Bxb
x � d t _^
`
min
0
(0,1
)
T
i i
cx
Axa
Bxb
x xforsomei
xforsomei
d t � �'
linea
rpr
ogra
mLP
(line
ar)
(mix
ed-)
inte
ger
prog
ram
IP, M
IP
„gen
eral
“(n
onlin
ear)
prog
ram
NLP
prog
ram
= o
ptim
izat
ion
prob
lem
Mar
tin G
röts
chel
35
Spec
ial „
sim
ple“
co
mbi
nato
rialo
ptim
izat
ion
prob
lem
s�
Find
ing
a�
min
imum
span
ning
tree
�sh
orte
stpa
th�
max
imum
mat
chin
g�
max
imal
flow
thro
ugh
a ne
twor
k�
cost
-min
imal
flo
w�
…
solv
able
in p
olyn
omia
ltim
e (a
ndve
ryfa
st in
pra
ctic
e)
Mar
tin G
röts
chel
36
Spec
ial „
hard
“ co
mbi
nato
rialo
ptim
izat
ion
prob
lem
s�
trav
ellin
g sa
lesm
an p
robl
em�
loca
tion
und
rout
ing
�se
t-pa
ckin
g, p
artit
ioni
ng, -
cove
ring
�m
ax-c
ut�
linea
r or
derin
g�
sche
dulin
g (w
ith a
few
eas
y ex
cept
ions
)�
node
and
edg
e co
lour
ing
�…
�N
P-ha
rd (
in t
he s
ense
of
com
plex
ity t
heor
y)Th
e m
ost
succ
essf
ul s
olut
ion
tech
niqu
es e
mpl
oy
linea
r pr
ogra
mm
ing
as a
bou
ndin
g pr
oced
ure.
－ 142 －
37
Ope
ratio
ns R
esea
rch,
Ja
n 20
02,
pp. 3
—15
, upd
ated
in 2
004)
Algo
rithm
s (m
achi
ne in
depe
nden
t):
Pr
imal
ver
sus
best
of
Prim
al/D
ual/B
arrie
r3,
300x
Mac
hine
s (w
orks
tatio
ns o
PCs)
:1,
600x
NET
: A
lgor
ithm
×M
achi
ne5,
300,
000x
(2 m
onth
s/53
0000
0 ~
=1
seco
nd)Cou
rtesy
Bob
Bix
by
Prog
ress
in L
P: 1
988—
2004
110100
1000
1000
0
1000
00
012345678910
1.2ĺ
2.1
2.1ĺ
33ĺ
44ĺ
55ĺ
66ĺ
6.5
6.5ĺ
7.1
7.1ĺ
88ĺ
99ĺ
1010ĺ
11
Cumulative Speedup
Version-to-Version Speedup
CP
LEX
Ver
sion
-to-
Vers
ion
Pai
rs
V-V
Spe
edup
Cum
ulat
ive
Spe
edup
Mature�Du
al�
Simplex:�1
994
Mined
�The
oretical
Backlog:�1998
2953
0x
Mix
ed I
nteg
er S
peed
ups
1991
-200
8 Cou
rtesy
Bob
Bix
by
Com
mer
cial
opt
imiz
atio
nso
ftw
are
�CP
LEX
�G
urob
i�
XPRE
SS�
MO
SEK
�…
Mar
tin G
röts
chel
39
The
SCIP
Opt
imiz
atio
n Su
ite o
f ZI
BTh
e SC
IP O
ptim
izat
ion
Suite
is a
too
lbox
for
gene
ratin
g an
d so
lvin
g m
ixed
inte
ger
prog
ram
s. I
t co
nsis
ts o
f th
e fo
llow
ing
part
s:
SCIP
: a
mix
ed in
tege
r pr
ogra
mm
ing
solv
er a
nd c
onst
rain
t pr
ogra
mm
ing
fram
ewor
kSo
Plex
: a
linea
r pr
ogra
mm
ing
solv
erZI
MPL
: a
mix
ed in
tege
r pr
ogra
mm
ing
mod
elin
g la
ngua
geG
CG:
a ge
neric
bra
nch-
cut-
and-
pric
e so
lver
UG
: a
para
llel f
ram
ewor
k fo
r so
lvin
g m
ixed
inte
ger
(line
ar a
nd
nonl
inea
r) p
rogr
ams
The
user
can
eas
ily g
ener
ate
linea
r pr
ogra
ms
and
mix
ed in
tege
r pr
ogra
ms
with
th
e m
odel
ing
lang
uage
ZIM
PL. T
he r
esul
ting
mod
el c
an d
irect
ly b
e lo
aded
into
SC
IP a
nd s
olve
d. I
n th
e so
lutio
n pr
oces
s SC
IP m
ay u
se S
oPle
xas
und
erly
ing
LP
solv
er.
Sinc
e al
l fiv
e co
mpo
nent
s ar
e av
aila
ble
in s
ourc
e co
de a
nd fre
e fo
r ac
adem
ic
use,
the
y ar
e an
idea
l too
l for
aca
dem
ic r
esea
rch
purp
oses
and
for
teac
hing
m
ixed
inte
ger
prog
ram
min
g.M
artin
Grö
tsch
el40
－ 143 －
http
://s
cip.
zib.
de/
Mar
tin G
röts
chel
41
His
tory
ofth
eSC
IP O
ptim
izat
ion
Suite
1996
SoP
lex
–Se
quen
tial o
bj. s
imPl
ex(R
. Wun
derli
ng[n
ow I
BM])
1998
SIP
–So
lvin
gIn
tege
r Pr
ogra
ms
(A. M
artin
[no
wU
Erla
ngen
])10
/200
2 Be
ginn
ing
of S
CIP
deve
lopm
ent
(T. A
chte
rber
g[n
ow G
urob
i])08
/200
3 Ch
ipde
sign
ver
ifica
tion
Cons
trai
ntPr
ogra
mm
ing
10/2
004
ZIM
PL –
Zuse
Ins
t. M
ath.
Pro
gram
min
gLa
ngua
ge (
T. K
och)
09/2
005
Firs
t pu
blic
ver
sion
0.8
of
SCIP
09/2
007
SCIP
1.0
rel
ease
, ZIB
Opt
imiz
atio
nSu
ite (
Sopl
ex, S
CIP,
ZIM
PL)
11/2
008
Dev
elop
men
t of
GCG
sta
rted
(G
. Gam
rath
)01
/200
9 G
as t
rans
port
opt
imiz
atio
n
nonl
inea
r03
/200
9 Be
ginn
ing
of U
G d
evel
opm
ent
(Y. S
hina
no)
09/2
009
Beal
e-O
rcha
rd-H
ays
Priz
e(T
. Ach
terb
erg)
04/2
010
Supp
ly-C
hain
man
agem
ent
) ex
trem
ely
larg
e LP
s/M
IPs
12/2
010
Goo
gle
Rese
arch
Aw
ard
2011
08/2
012
Vers
ion
3.0.
0, f
irst
rele
ases
of
GCG
and
UG
SCIP
Opt
imiz
atio
nSu
ite (
Sopl
ex, S
CIP,
ZIM
PL, G
CG, U
G)
Mar
tin G
röts
chel
42
BMBF
-For
schu
ngsc
ampu
s M
odal
Mar
tin G
röts
chel
43M
artin
Grö
tsch
el44
SoPl
exSe
quen
tial o
bjec
t-or
ient
ed s
impl
ex
SoPl
ex is
an
impl
emen
tatio
n of
the
rev
ised
sim
plex
alg
orith
m. I
t fe
atur
es p
rimal
and
dua
l sol
ving
rou
tines
for
linea
r pr
ogra
ms
and
is
impl
emen
ted
as a
C+
+ c
lass
libr
ary
that
can
be
used
with
oth
er
prog
ram
s.
Rol
and
Wun
derli
ng,
Par
alle
ler u
nd O
bjek
torie
ntie
rter
Sim
plex
-Alg
orith
mus
, D
isse
rtatio
n, T
U B
erlin
,199
7
now
empl
oyed
byIB
M, d
evel
opin
g C
PLE
X’s
LP
tech
nolo
gy
－ 144 －
Zim
plZi
mpl
is a
litt
le la
ngua
ge t
o tr
ansl
ate
the
mat
hem
atic
al m
odel
of
a pr
oble
m
into
a li
near
or
(mix
ed-)
inte
ger
mat
hem
atic
al p
rogr
am e
xpre
ssed
in
.lp o
r .m
ps f
ile fo
rmat
whi
ch c
an b
e re
ad a
nd (
hope
fully
) so
lved
by
a LP
or
MIP
sol
ver.
Thor
sten
Koc
h, R
apid
Mat
hem
atic
al P
rogr
amm
ing,
Dis
sert
atio
n, T
U
Berli
n 20
04
(aw
arde
d w
ith t
he D
isse
rtat
ion
Priz
e 20
05 o
f th
e G
esel
lsch
aft
für
Ope
ratio
ns R
esea
rch)
Mar
tinG
röts
chel
45
SCIP
h
ttp:
//sc
ip.z
ib.d
e/To
bias
Ach
terb
erg,
Tob
ias,
Con
stra
int I
nteg
er
Prog
ram
min
g , D
isse
rtat
ion,
TU
Ber
lin, 2
007
Dis
sert
atio
n Pr
ize
2008
of
the
Ges
ells
chaf
tfü
r O
pera
tions
Res
earc
h (G
OR)
G
eorg
e B.
Dan
tzig
Dis
sert
atio
n Aw
ard
2008
of
the
Ins
titut
e of
Ope
ratio
ns R
esea
rch
and
the
Man
agem
ent
Scie
nces
(IN
FORM
S),
2nd
priz
e)
Beal
e-O
rcha
rd-H
ays
Priz
e 20
09 o
f th
e M
athe
mat
ical
Opt
imiz
atio
n So
ciet
y fo
r th
e pa
per:
To
bias
Ach
terb
erg,
“SCI
P: S
olvi
ng c
onst
rain
t in
tege
r pr
ogra
ms”
,M
athe
mat
ical
Pro
gram
min
g Co
mpu
tatio
n, 1
(20
09),
pp.
1-4
1.
Now
em
ploy
ed b
y IB
M, d
evel
opin
g CP
LEX’
s M
IP t
echn
olog
y
Mar
tinG
röts
chel
46
GCG
G
ener
ic C
olum
n G
ener
atio
n
Mar
tin G
röts
chel
47
GCG
exte
nds
the
bran
ch-c
ut-a
nd-p
rice
fram
ewor
k S
CIP
to a
gen
eric
bra
nch-
cut-a
nd-p
rice
solv
er.
�pe
rform
s D
antz
ig-W
olfe
dec
ompo
sitio
n fo
r de
tect
ed o
r pro
vide
d st
ruct
ure
�S
olve
s re
form
ulat
ion
with
bra
nch-
and-
pric
e ap
proa
ch�
pric
ing
prob
lem
s so
lved
as
MIP
s�
gene
ric b
ranc
hing
rule
s fo
r bra
nch-
and-
pric
eP
rovi
des
easy
acc
ess
to a
noth
erst
ate-
of-th
e-ar
t MIP
sol
ving
tech
nolo
gy.
Ger
ald
Gam
rath
, G
ener
ic B
ranc
h-C
ut-a
nd-P
rice,
D
iplo
ma
Thes
is, T
U B
erlin
, 201
0
Cur
rent
ly d
evel
oped
in c
oope
ratio
n w
ith
RW
TH A
ache
n, a
lso
fund
ed b
y S
PP
1307
UG
U
biqu
ity G
ener
ator
Fra
mew
ork
Mar
tin G
röts
chel
48
UG
is a
gen
eric
fram
ewor
k to
par
alle
lize
bran
ch-
and-
boun
d ba
sed
solv
ers
(e.g
., M
IP, M
INLP
, E
xact
IP) i
n a
dist
ribut
ed o
r sha
red
mem
ory
com
putin
g en
viro
nmen
t.•
Exp
loits
pow
erfu
l per
form
ance
of s
tate
-of-t
he-a
rt "b
ase
solv
ers"
, suc
h as
SC
IP, C
PLE
X, e
tc.
•W
ithou
t the
nee
d fo
r bas
e so
lver
par
alle
lizat
ion Yu
ji S
hina
no,
A G
ener
aliz
edU
tility
for
Par
alle
l Bra
nch-
and-
Bou
ndA
lgor
ithm
us,
Dis
serta
tion,
Tok
yo
Uni
vers
ity o
fSci
ence
,199
7
－ 145 －
Mar
tin G
röts
chel
49
Curr
ent
ZIB
LP/M
IP/S
CIP
Gro
up
Mar
tinG
röts
chel
50
Form
er Z
IB L
P/M
IP/S
CIP
Gro
up
Mar
tinG
röts
chel
51
The
big
trip
le�
Mod
ellin
g�
Sim
ulat
ion
�O
ptim
izat
ion
�M
SO (
new
buz
z w
ord/
abbr
evia
tion,
in p
artic
ular
use
d in
ap
plie
d m
athe
mat
ics)
Mar
tin G
röts
chel
52
－ 146 －
The
Real
Pro
blem
Pure
Mat
hem
atic
sCo
mpu
ter
Scie
nce
Mat
hem
atic
al
Mod
el
Mat
hem
atic
al
Theo
ry
Des
ign
of G
ood
Solu
tion
Algo
rithm
s
Algo
rithm
icIm
plem
enta
tion
Num
eric
alSo
lutio
n
Qui
ck C
heck
:H
euris
tics
Har
d-w
are
Soft
-w
are
Dat
aG
UI
Prac
titio
ner
Spec
ialis
tM
odel
ling
Sim
ulat
ion
Opt
imiz
atio
n
Educ
atio
n
The
Appl
icat
ion
Driv
en A
ppro
ach
Sim
ulat
ion
Impl
emen
tatio
n in
Pra
ctic
e
The
prob
lem
solv
ing
cycl
e
Mar
tin G
röts
chel
54
Mat
hem
atic
al M
odel
ling:
W
hat
isth
at?
Begi
nnin
gw
ithob
serv
atio
ns�
ofou
ren
viro
nmen
t�
a pr
oble
min
pra
ctic
eof
part
icul
arin
tere
stor
�a
phys
ical
, ch
emic
al, o
rbi
olog
ical
phen
omen
on
and
with
guid
ing/
tailo
red
expe
rimen
ts:
the
atte
mpt
ofa
form
al r
epre
sent
atio
nvi
a
„mat
hem
atic
alco
ncep
ts“
(var
iabl
es, e
quat
ions
, in
equa
litie
s, o
bjec
tive
func
tions
, etc
.), a
imin
gat
the
utili
zatio
nof
mat
hem
atic
alth
eorie
san
dto
ols.
Conf
usio
n:Th
ere
are
man
yot
her
way
sof
mod
ellin
g�
com
pute
rm
odel
s�
busi
ness
mod
el�
arch
itect
ural
mod
els
�ch
emic
alm
odel
s�
med
ical
mod
els
�...
Mar
tin G
röts
chel
55M
artin
Grö
tsch
el56
Sim
ulat
ion
Sim
ulat
ion,
Sim
ulat
or o
r si
mul
ate
are
deriv
ed f
rom
the
latin
wor
dssimulare
and similis.
They
mea
n: p
rete
ndto
beor
the
sam
e so
rt.
－ 147 －
Mar
tin G
röts
chel
57
Sim
ulat
ion
„Com
puta
tion“
of
seve
ral (
clos
e to
rea
lity)
var
iant
s of
a
mat
hem
atic
al m
odel
laim
img
at:
�„v
alid
atio
n“of
the
corr
ectn
ess
of a
mod
el�
inve
stig
atio
n of
typ
ical
inst
ance
s in
the
mod
el fra
mew
ork,
e.g
., to
av
oid
expe
rimen
ts o
r to
tes
t so
me
func
tiona
lity
(cra
sh-t
est)
�go
od p
redi
ctio
ns (
wea
ther
)�
com
puta
tion
of r
easo
nabl
e so
lutio
ns fo
r th
e co
ntro
l of
a sy
stem
in
prac
tice
(con
trol
of
tran
spor
t an
d lo
gist
ics-
syst
ems)
58
Sim
ulat
ion
Com
puta
tion
of in
stan
ces
vary
ing
seve
ral p
aram
eter
s Pa
ram
eter
s of
a c
ar c
rash
tes
t, e
.g.:
sp
eed,
mat
eria
l stif
fnes
s, v
ario
us a
ngle
s
3D-r
econ
stru
ctio
n of
a s
cull
from
a m
agne
to-r
eson
ance
to
mog
rafic
inve
stig
atio
n
Kaly
anm
oyD
eb:
The
grea
tco
nfus
ion
From
his
boo
k: „
Mul
ti-ob
ject
ive
optim
izat
ion
usin
g ev
olut
iona
ry a
lgor
ithm
s“ (
Wile
y, 2
001)
Preface
Opt
imiz
atio
nis
a pr
oced
ure
of f
indi
ngan
dco
mpa
ring
feas
ible
solu
tions
until
nobe
tter
solu
tion
can
befo
und.
(Re
ally
?)
Clas
sica
lopt
imiz
atio
nm
etho
dsca
nat
best
find
one
solu
tion
in o
nesi
mul
atio
nru
n, t
here
bym
akin
gth
ose
met
hods
inco
nven
ient
toso
lve
mul
ti-ob
ject
ive
optim
izat
ion
prob
lem
s. (
???)
Evol
utio
nary
algo
rithm
s(E
As),
on
the
othe
rha
nd, c
anfin
d m
ultip
le
optim
also
lutio
nsin
one
sing
lesi
mul
atio
nru
ndu
e to
thei
rpo
pula
tion-
appr
oach
. (Tr
ue?)
Thu
s, E
As a
reid
eal c
andi
date
sfo
rso
lvin
gm
ulti-
obje
ctiv
eop
timiz
atio
npr
oble
ms.
(Ve
ryco
nvin
cing
!)
Kaly
anm
oyD
eb:
The
grea
tco
nfus
ion
From
his
boo
k: „
Mul
ti-ob
ject
ive
optim
izat
ion
usin
g ev
olut
iona
ry
algo
rithm
s“ (
Wile
y, 2
001)
Cons
trai
nts
are
inev
itabl
ein
any
real
-wor
ldop
timiz
atio
npr
oble
m. (
A de
epob
serv
atio
n)
In o
rder
tow
iden
the
appl
icab
ility
of a
n op
timiz
atio
nal
gorit
hmin
var
ious
diffe
rent
pro
blem
dom
ains
, nat
ural
and
phys
ical
prin
cipl
esar
em
imic
ked
tode
velo
pro
bust
optim
izat
ion
algo
rithm
s. E
volu
tiona
ryal
gorit
hms
and
sim
ulat
edan
neal
ing
are
two
exam
ples
of s
uch
algo
rithm
s. (
Jugg
ling
wor
ds)
But
I ha
vele
arne
dab
out
mar
ketin
g: „
pow
erfu
l“.
－ 148 －
Exam
ples
Alm
ost
all f
rom
ZIB
proj
ects
Mar
tin G
röts
chel
61M
artin
Grö
tsch
el62
Her
litz
at F
alke
nsee
(Be
rlin)
Opt
imiz
atio
nan
d co
ntro
lof
tran
spor
tde
vice
s(s
uch
asel
evat
ors,
sta
cker
cran
es)
in fa
ctor
ies
Her
litz,
Fal
kens
ee
Mar
tin G
röts
chel
64
Prin
ted
Circ
uit
Boar
d:D
rillin
g an
d As
sem
bly
Mac
hine
s
－ 149 －
Tele
com
mun
icat
ion
netw
ork
desi
gnM
ATH
EON
B3
65
Logi
cal c
onne
ctio
nsPh
ysic
al c
onne
ctio
ns
Net
wor
k de
sign
MAT
HEO
NB3
66
Logi
cal c
onne
ctio
ns:
solu
tion
Phys
ical
con
nect
ions
: so
lutio
n
Mar
tin G
röts
chel
67
Regi
on B
erlin
-D
resd
en
2877
ca
rrie
rs
50 c
hann
els
Inte
rfere
nce
redu
ctio
n:83
.6%
A Pr
ojec
t w
ith B
HP
Billi
ton
Mar
tin G
röts
chel
68
－ 150 －
Mar
tin
Grö
tsch
el69
Som
e TS
P W
orld
Rec
ords
year
auth
ors
# c
ities
# v
aria
bles
1954
DFJ
42/4
982
0/11
46
1977
G12
071
40
1987
PR53
214
1,24
6
1988
GH
666
221,
445
1991
PR2,
392
2,85
9,63
6
1992
ABCC
3,03
84,
613,
203
1994
ABCC
7,39
727
,354
,106
1998
ABCC
13,5
0991
,239
,786
2001
ABCC
15,1
1211
4,17
8,71
6
2004
ABCC
24,9
7831
1,93
7,75
3
num
ber
of c
ities
2000
xin
crea
se
4,00
0,00
0tim
espr
oble
msi
zein
crea
se
in ~
60ye
ars
2005
W. C
ook,
D. E
psin
oza,
M. G
oyco
olea
33,8
10
5
71,5
41,1
45
2006
pla
85,9
00
solv
ed3,
646,
412,
050
varia
bles
Tele
com
mun
icat
ion
topi
cs:
Har
dwar
e an
d lo
gist
ics
(ZIB
and
MAT
HEO
N)
Des
igni
ngm
obile
pho
nes
�Ta
sk p
artit
ioni
ng�
Chip
des
ign
(VLS
I)�
Com
pone
ntde
sign
Prod
ucin
gM
obile
Pho
nes
�Pr
oduc
tion
faci
lity
layo
ut�
Cont
rolo
fCN
C m
achi
nes
�Co
ntro
lof
robo
ts�
Cutt
ing
and
wel
ding
�Pr
inte
dCi
rcui
t Bo
ards
�Vi
a m
inim
izat
ion
�Co
mpo
nent
Plac
emen
t�
Mou
ntin
gD
evic
es�
Rout
ing
�Lo
t si
zing
�Sc
hedu
ling
�Lo
gist
ics
Mar
ketin
g an
dD
istr
ibut
ion
of M
obile
Pho
nes
Mar
tinG
röts
chel
70
Opt
imiz
atio
nin
Pub
lic T
rans
it
Railw
ay O
ptim
izat
ion
and
Inte
ger
Prog
ram
min
g71
Slid
e of
IVU
Multicom.
Flow
Set
Partitioning
Integrated
Scheduling
Other
Mar
tin G
röts
chel
72
Vehi
cle
sche
dulin
gin
pub
lictr
ansp
ort
mor
ning
pea
k
68
－ 151 －
Publ
ic T
rans
port
Pro
ject
s (Z
IB a
nd M
athe
on)
Buss
es(B
erlin
and
els
ewhe
re)
�Te
lebu
s(T
rans
port
atio
n of
di
sabl
ed p
erso
ns)
�Bu
s Ci
rcul
atio
n�
Bus
driv
er S
ched
ulin
g�
Inte
grat
ed V
ehic
le a
nd D
river
Sc
hedu
ling
�Ti
met
able
Exc
hang
eSu
bway
s an
d Li
ght
Railw
ays
�Su
bway
Tim
e Ta
blin
g�
Vehi
cle
Sche
dulin
g
Infr
astr
uctu
re P
lann
ing
�Li
ne P
lani
ng�
Net
wor
k Pl
anni
ng (
Pots
dam
)�
Fare
Pla
ning
Airli
nes
�Ai
rline
Cre
w S
ched
ulin
g�
Tail
Assi
gnm
ent:
Rob
ustn
ess
Railw
ays
�Ra
ilway
Tra
ck A
lloca
tion
�IC
E Ci
rcul
atio
nSp
in-O
ffs :
LBW
, Int
rane
tz
Opt
imiz
atio
n O
verv
iew
73
Appl
icat
ions
(ge
nera
l top
ics)
Area
s w
ith s
igni
fican
t op
timiz
atio
n de
man
d:�
Indu
stria
l pro
duct
ion
(con
trol
of
CNC
mac
hine
s, a
ssem
bly
line
optim
izat
ion,
rob
ot c
ontr
ol,…
)�
Min
ing
(Sch
edul
ing,
roc
k da
mag
e an
d fr
actu
re m
odel
s, …
)�
Hea
lth c
are
& m
edic
ine
(sup
port
for
oper
atio
ns, d
rug
desi
gn,…
)�
Ener
gy (
optim
izat
ion
of e
nerg
y pr
oduc
tion
and
mix
, uni
t co
mm
itmen
t,…
)�
Reso
urce
pla
nnin
g (e
nviro
nmen
tal i
ssue
s,...
)�
Fina
ncia
l mat
hem
atic
s (m
odel
ling
of r
isk,
…)
�In
fras
truc
ture
pla
nnin
g (p
ublic
tra
nspo
rt, w
ater
, str
eet,
gas
,…
netw
orks
, har
bor
desi
gn)
�Ag
ricul
ture
(no
per
sona
l exp
erie
nce,
but
…)
�Te
leco
mm
unic
atio
n �
Logi
stic
s &
Tra
ffic
and
Tra
nspo
rt
I ca
n go
on
fore
ver
Mar
tinG
röts
chel
74
Isth
ere
mor
eth
anop
timiz
atio
n?�
Dat
a (c
olle
ctin
g da
ta, m
akin
g th
em c
orre
ct, u
pdat
ing,
...)
�D
ata
secu
rity,
priv
acy
and
long
ter
m a
rchi
ving
�To
day:
Big
dat
a in
all
med
ia�
Test
pro
blem
s�
Mod
elin
g La
ngua
ges
�D
ecis
ion
Mak
ing:
Dec
isio
n H
euris
tics
�Ps
ycho
logy
/Soc
iolo
gy�
Gam
e Th
eory
and
Bou
nded
Rat
iona
lity
�M
echa
nism
Des
ign
�Sa
fety
�In
tegr
atio
n of
diff
eren
t vi
ews
Mar
tin G
röts
chel
75
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
76
－ 152 －
Failu
res
ofO
R/O
ptim
izat
ion
1.In
abili
ty t
o co
me
up w
ith a
“go
od”
nam
e.2.
Inab
ility
to
mak
e th
e su
bjec
t a
trad
emar
k.3.
OR/
Opt
imiz
atio
n re
mai
ns “
fuzz
y” t
o th
e ou
tsid
er.
4.O
R vi
rtua
lly u
nkno
wn
in t
he p
ublic
med
ia.
5.O
ptim
izat
ion
has
(at
leas
t in
Ger
man
y) u
nwan
ted
conn
otat
ions
(la
bor
unio
ns).
6.Pr
omis
es o
f O
R/O
ptim
izat
ion
mad
e in
the
six
ties
and
seve
ntie
s co
uld
not
be fu
lfille
d. T
his
led
to t
he s
hut-
dow
n of
man
y O
R de
part
men
ts in
indu
stry
and
aca
dem
ia.
7.To
o m
athe
mat
ical
ly o
rient
ed o
ptim
izer
s (ig
norin
g, e
.g.,
data
ha
ndlin
g an
d ps
ycho
logy
of
the
wor
k fo
rce)
stil
l do
not
deliv
er.
8.Ac
adem
ic fo
cus
on p
ublis
habl
e bu
t pr
actic
ally
irre
leva
nt r
esul
ts
(e.g
. wor
st-c
ase
or a
vera
ge-c
ase
perf
orm
ance
of
heur
istic
s).
Mar
tin G
röts
chel
77
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
78
Sim
ple
answ
ers
Theo
ry a
nd a
lgor
ithm
s �
Cont
inue
dev
elop
ing
the
mat
hem
atic
al s
olut
ion
tech
nolo
gy.
Inte
grat
e so
lutio
n te
chno
logi
es, e
.g.,
�lin
ear
�no
nlin
ear
�co
mbi
nato
rial
�in
tege
r�
mix
ed-in
tege
r�
stoc
hast
ic�
robu
st�
onlin
e�
real
-tim
e�
mul
ti-ob
ject
ive
�un
cert
ain
and
not
nece
ssar
ily r
elia
ble
data
�...
Mar
tin G
röts
chel
79
Sim
ple
answ
ers
Prac
tice
�Fo
cus
on r
eal a
nd n
ot o
n ar
tific
ial p
robl
ems.
�Th
e w
orld
is fu
ll of
exc
iting
new
uns
olve
d qu
estio
ns.
�Co
oper
ate
with
oth
er d
isci
plin
es:
They
all
need
us!
�Al
l gra
nd c
halle
nges
hav
e op
timiz
atio
n as
pect
s su
ch a
s m
akin
g be
st u
se o
f sc
arce
res
ourc
es. P
artic
ipat
e in
the
ir so
lutio
n!
Mar
tin G
röts
chel
80
－ 153 －
Prob
lem�classes
81
CP Constraint
Programming
MINLP
CIP
MIP LP
SAT
IP
Constraint�Integer�
Programming
Mixed
�Integer�N
onͲline
arProgramming
Integer�Linear
Programming
Line
arProgramming
Mixed
�Integer�
Line
ar�Program
ming
Satisfiability
Net
wor
k, L
ine
and
Fare
Plan
ning
(Pot
sdam
)
Opt
imiz
atio
n O
verv
iew
82
Mar
tinG
röts
chel
Mod
ellin
gAs
pect
sof
Gas
Tra
nspo
rtat
ion
Non
linea
r N
onco
nvex
:•
Loss
of
pres
sure
ove
r pi
pes:
•Po
wer
of
com
pres
sor:
Mix
ed-I
nteg
er:
•Fl
ow d
irect
ion
•Co
uplin
g co
nstr
aint
s
Mar
tinG
röts
chel
Mod
ellin
gAs
pect
sof
Gas
Tra
nspo
rtat
ion
Unc
erta
inty
:•
Stoc
hast
ic n
omin
atio
n at
exi
ts•
Unk
now
nno
min
atio
n at
ent
ries
Cons
trai
nt I
nteg
er P
rogr
amm
ing:
•co
mbi
nes
SAT,
MIP
, and
CP
¾st
rong
mod
elin
g ca
pabi
lity
¾fu
ll po
wer
of
MIP
sol
ving
tec
hniq
ues
－ 154 －
85
Goa
l:In
tegr
atio
n of
mul
ti-la
yer b
ackb
one
and
regi
onal
net
wor
ksFu
ture
net
wor
ks: I
P/E
ther
net l
ayer
ove
r sha
red
optic
al fi
ber l
ayer
Hug
e ne
twor
ks (9
00 n
odes
), co
mbi
ne d
iffer
ent s
ervi
ces/
tech
nolo
gies
Mor
e st
ruct
ure:
hie
rarc
hica
l rou
ting
Mul
ti-la
yer
mul
ti-le
vel P
lann
ing
Cou
rtesy
NS
N
Hau
ptve
rteile
rC
entra
l Offi
ceD
TAG
: ~8.
000
Kern
netz
stan
dort
Poi
nt o
fPre
senc
e P
OP
DTA
G: 7
3
Traf
fic c
lass
es:
deriv
ede
man
dm
atric
esfo
rac
cess
poin
ts(c
entra
loffi
ces)
chea
pte
chno
logy
expe
nsiv
e te
chno
logy
LER
: Lab
el E
dge
Rou
ter
LSR
: Lab
el S
witc
h R
oute
r
Bro
adba
nd re
mot
e ac
cess
serv
er
Mar
tin G
röts
chel
86
Cont
ribut
ions
to
answ
erin
g so
me
HAR
D q
uest
ions
Wha
t do
I m
ean?
We
shou
ld s
tart
add
ress
ing
polit
ical
ly r
elev
ant
“glo
bal q
uest
ions
” se
rious
ly.
Mar
tin G
röts
chel
87
Hig
h Q
ualit
y Pu
blic
Tra
nspo
rtat
ion:
Mat
hem
atic
al, S
ocia
l, Po
litic
al, a
nd B
usin
ess
Aspe
cts
Wha
t is
a "
good
" pu
blic
tra
nspo
rtat
ion
syst
em?
Can
such
a s
yste
m r
esul
t fr
om d
ereg
ulat
ion?
H
ow d
oes
one
regu
late
/der
egul
ate,
e.g
., th
e ra
ilway
sys
tem
of
a co
untr
y, p
rope
rly?
We
are
curr
ently
inve
stig
atin
g su
ch a
nd r
elat
ed is
sues
whi
ch a
re
high
ly r
elev
ant
for
ever
ybod
y’s
ever
yday
life
. The
re a
re m
ore
ques
tions
tha
n an
swer
s.
Sim
ilar
ques
tions
in�
Elec
tric
ity p
rodu
ctio
n an
d di
strib
utio
n�
Gas
tra
nspo
rt�
In g
ener
al:
Ener
gy�
Fina
ncia
l mar
kets
�Po
litic
al d
ecis
ion
mak
ing
(Ger
man
vot
ing
syst
em)
�Li
fe s
cien
ces
and
heal
th c
are
... Mar
tin G
röts
chel
88
－ 155 －
Net
wor
ks
Appl
icat
ion
Area
B –
Net
wor
ks89
Net
wor
ks:
A M
ATH
EON
Visi
on
Appl
icat
ion
Area
B –
Net
wor
ks90
Net
wor
ks:
Indu
stry
Par
tner
s
Appl
icat
ion
Area
B –
Net
wor
ks91
acat
ech
Ger
man
Nat
iona
lAc
adem
y of
Engi
neer
ing
2010
92
－ 156 －
Som
e bi
g “O
R st
uff”
tha
t is
not
opt
imiz
atio
n�
Inte
grat
ion
of a
ppro
ache
s to
mod
elin
g of
pro
blem
s an
d pr
oble
m s
olvi
ng (
engi
neer
ing,
man
agem
ent
scie
nce,
co
mpu
ter
scie
nce,
law
,...)
�Bi
g D
ata:
“D
ata
Scie
nce”
is a
new
key
wor
d th
at m
ay c
ut
sign
ifica
ntly
into
the
dom
ain
of O
R �
Dec
isio
n M
akin
g: D
ecis
ion
Heu
ristic
s/M
echa
nism
s in
So
ciol
ogy
Psyc
holo
gy a
nd t
he N
euro
Scie
nces
Mar
tin G
röts
chel
93
ORM
S-To
day:
Aug
ust
2013
, vol
. 40,
No.
4
Mar
tin G
röts
chel
94
Rega
rdle
ss, t
he u
nive
rsal
app
reci
atio
n an
d re
cogn
ition
of
the
pow
er o
f ap
plyi
ng o
ur a
naly
tical
too
lbox
for
bett
er
deci
sion
-mak
ing
is p
erva
sive
. The
inte
rest
in m
athe
mat
ical
m
odel
ing
and
crea
tive
appl
icat
ions
of
data
(es
peci
ally
whe
n th
e w
ord
“big
” is
use
d) is
glo
bal.
This
invi
gora
ted
inte
rest
is
help
ing
driv
e m
ore
stud
ents
to
lear
n ou
r fie
ld, a
s w
ell a
s gr
eate
r le
vera
ge a
nd a
pplic
atio
n of
ope
ratio
ns r
esea
rch
theo
ry a
nd p
ract
ice.
In
the
wor
ds o
f W
illia
m S
hake
spea
re,
“A r
ose
by a
ny o
ther
nam
e w
ould
sm
ell a
s sw
eet.”
Cont
ents
1.O
utlin
e2.
Whe
re d
o I
com
e fr
om?
3.N
amin
g bu
sine
ssO
pini
ons
and
Fact
s4.
Wha
t ar
e O
R an
d op
timiz
atio
n?
Opi
nion
s an
d Fa
cts
5.Su
cces
s st
orie
s6.
Failu
res?
7.
Wha
t sh
ould
OR/
optim
izat
ion
do?
8.Co
nclu
sion
s
Mar
tin G
röts
chel
95M
artin
Grö
tsch
el96
Dire
ctio
nsI
am m
ysel
f a
pers
on p
refe
rrin
g to
add
ress
que
stio
ns
�th
at c
an b
e qu
antif
ied
prec
isel
y �
that
hav
e cl
ean
data
and
�
that
hav
e cl
ear
obje
ctiv
es a
nd
�th
at c
an b
e m
odel
ed n
icel
y.H
owev
er, I
thi
nk w
e sh
ould
sta
rt a
ddre
ssin
g pr
oble
ms
mor
e
serio
usly
�th
at c
an’t
be q
uant
ified
pre
cise
ly
�th
at d
on’t
have
cle
an d
ata
and
�th
at d
on’t
have
cle
ar o
bjec
tives
and
�
that
can
’t be
mod
eled
nic
ely.
but
that
are
of
high
pol
itica
l and
soc
ial r
elev
ance
.O
R/O
pt h
as t
he p
oten
tial f
or d
oing
tha
t an
d ha
s be
gun
to
cont
ribut
e to
suc
h is
sues
!
－ 157 －
Mar
tin G
röts
chel
97
Scie
nce
in t
he V
iew
of
the
Publ
ic
I am
con
cern
ed s
ince
I s
ee d
ownf
alls
of
scie
ntifi
c fie
lds
such
as
nucl
ear
tech
nolo
gy t
hat
has
mad
e m
any
prom
ises
and
bro
ught
fear
.
The
sam
e is
pre
sent
ly h
appe
ning
to
biot
echn
olog
y(m
any
peop
le a
re
sim
ply
afra
id o
f th
e pr
ogre
ss a
nnou
nced
).
Sim
ilar
tend
enci
es a
re c
urre
ntly
com
ing
up in
nan
ote
chno
logy
.
I se
e a
begi
nnin
g of
a r
educ
tion
of “
trus
t in
sci
ence
”.
Mar
tin G
röts
chel
98
OR/
Opt
imiz
atio
n in
the
Vie
w o
f th
e Pu
blic
O
R ha
s to
wat
ch o
ut t
hat
it ke
eps
the
right
bal
ance
and
peo
ple
do n
ot s
tart
get
ting
afra
id o
f O
R an
d op
timiz
atio
n.
I se
e te
nden
cies
in p
ublic
tal
ks o
f co
mpa
ny b
osse
s, p
oliti
cian
s,
jour
nalis
ts, a
nd u
nion
lead
ers
that
opt
imiz
atio
n m
eans
not
hing
but
el
imin
atin
g jo
bs, c
uttin
g do
wn
serv
ices
, etc
.
Mar
tin G
röts
chel
99
My
advi
ce�
Don
’t ca
re t
oo m
uch
abou
t de
finiti
ons
of O
R an
d op
timiz
atio
n an
d al
l the
var
iatio
ns.
�Be
fle
xibl
e.�
Enjo
y th
e st
uff
that
you
do.
�Po
sitio
n yo
urse
lf de
pend
ing
on y
our
own
need
s, g
oals
, and
w
ishe
s an
d th
at o
f yo
ur c
ompa
ny o
r ac
adem
ic in
stitu
tion.
�An
d ta
lk a
bout
wha
t yo
u ar
e do
ing
and
how
it im
pact
s so
ciet
y an
d in
dust
ry p
ositi
vely
, not
onl
y to
aca
dem
ic a
nd in
dust
ry
peop
le b
ut a
lso
to t
he g
ener
al p
ublic
.
Mar
tin G
röts
chel
100
The
OR/
Opt
imiz
atio
n m
iracl
eIn
my
opin
ion,
it is
a m
iracl
e th
at O
R/O
ptim
izat
ion
has
su
rviv
ed s
o w
ell t
hrou
gh t
he la
st 6
0 ye
ars,
hav
ing
unde
rgon
e m
any
batt
les,
spl
its, (
re-)
unifi
catio
ns a
nd n
ame
chan
ges.
Th
is is
goo
d si
gn fo
r ro
bust
hea
lth a
nd in
dica
tor
for
long
evity
of
the
fie
ld –
inde
pend
ent
of t
he n
ame
mut
atio
ns.
－ 158 －