Honoring a Family Legacy The Story of Mr. Larson.pdf

From Legacy to Leadership: The Remarkable Journey of Mr. Larson
Pioneering data-driven models and redefining public service, Richard C. Larson, Professor at MIT, honors his family legacy while shaping the future with algorithms, operations research, and real-time innovation.

1. BUSINESS MYNDS

MEDIA
YOUR VISION OUR MISSION

Honoring,
a Family Lega:
The Story of

Mr. Larson

‘Smarter Systems for
a Faster World

The Future of
Operations Research in
a Real-Time World

Algorithms
of the City

How Data-Driven
Models Improve

Public Services

Beyond the Line
How Queueing Theory
Powers Public Service

AT

arson

© Professor, MIT ©

Building the Operating Manual for Modern Lie

2. THE WAY TO CET
TALKING AND

- Walt

3. TARTED IS TO QUIT
BEGIN DOING.

Disney

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5. FROM THE
— EDITOR

CELEBRATING PURPOSEFUL
INNOVATION THAT
SERVES SOCIETY

Ber

Managing Editor

6. (KON

Story
O6
Richard e Larson

7. get!

Articles

> Algorithms of the City
How Data-Driven Models
Improve Public Services

D Beyond the Line

How Queueing Theory Powers
Public Service

The Future of Operations Research
in a Real-Time World

8 Smarter Systems for a Faster World

ID

8. 06

Richard C.

Larson

Building the Operating Manual for Modern Life

6] My greatest

reward as an
educator isn't the
theories I've
taught, but the
students I've
watched turn
those theories into

real-world impact. 99)

wmv businessmindsmedio.

9. Richard C. Larson
MIT

10. © Honoring a Family Legacy The Story of Mr. Larson

breathe, trum, and rc forward in fis observe the grifloks and glitches of city lfe—he
ng complet into equations

impact st confine o control
cal models While

Legacy of Lives Enhanced

and visionary leadership hi
lada long-lasting influence on urban systems,

13. operations esarch, and tchnology-enbanced reference, having amassed over 1,000 cations.
‘lication, In addition to his sehlarly contributions

nd instiona motions Richard let ehinda Richa innowative research ito queucng systems
legacy of countles lives enhanced By is as camed both naonal and inertial
commen 1 inking theory and Pc. ‘recognition Among Hs landmark achievements are

the Queue Inference Engine «pioneering
pplication of ata analytes long before the tem

a ¡became mainsreum, andthe Hypercube Queueing
Richard was bor in Bayside, Ques, à Mode, which ha ben cited extensively i academic
neighborhood ucked within he or New York Tiere

‘The Making

Systems Thinker

In ation to pblishing, Richard has been a we
Snowe leader in hi industry He presided over
INFORMS (2005), te Insite for Operations
ding him o graduate fom Research and the Management Sciences, as well as
eri hesethissighis the Operations Research Society o America
(1998-1994)

He also spent ver LS yearsas coditetor of MIT's
(Opera Research Center, helping shape the next
generation ofesarchen and thought eae.

Ln his chen 1 The field of publi policy has also made use of his

chard fd himself influencedby | experienc, Serving on US. government adsory
bodies sua the Standing Commitee on

Emergency Management and Medical Response
Integration 2009-2015) andthe Institut of
Medicine's Board on Health Sciences Policy
(0008-2010) allowed him to contribute to nasonal
projet

His consulting work has informed major operational
improvement forte US Postal Service and he

A . City of New York
coute ore

> more than 175 pecesoviencd, “The Mission Behind the Honors
spans a wide aay of domains,

Ron whan immense Richart hs een many acotados ris

AS sang acciones The INFORNS
Frits Aware Geo E Kill Mola nd

"inurl Dai! Br icine Achievement

Modlin 2017 eat ev ol oras son
poo him wen sorrow
ci pining pal sie ste and
{ccna inneren

Berween 1995 and 2003, Richard led MIT's Center
"for Advanced Educational Services (CAES), where
championed the integration of digital ering into

14. "Networks Consortium), which brought together
ator from around the world fo dialogue and
‘llaboraion through a series of ntratona
symposia

Today Richard continues his mision to expand
acces to quality cation ste principal
investigator of MIT BLOSSOMS an open-source
leaning nave focused on science and
mathematics. He remains active in research
particulary in developing operations research
Frameworks to adres large-scale challenges suchas
pandemic response and educational efor in he
United Sates

Education That Transeends the Four Wall

Richard was renowned as teacher for ing
rigorous academic instruction with hands-on,
‘expecta laine, He avoided a purely "core
proof” approach besuse he thought that sues
dying operations research node experience
real systems inorder to have an inuit
understanding of them, His own clases a MIT
required students to apply analytical teenies to
‘complex ancre situations rough ld
research case studies, and group projets

Richard infence extend much beyond the
“lo. He mentor numerous PRD students
ring his ore, including Kent W. Colon and Maia
“Majumder, who have achieved distinguished carers
in academia, business and government His genio
‘cancer fr his pupil professional and personal
‘evelopment, a well a his commitment o fostering
‘moral responsibilty and intelectual curiosity, were
Fallmar of his mentoring.

Richard managed ofcampus consulting fins such
Public System Evaluation, Ie and ENFORTH
Corp in edition to his teaching responsible.
‘These companies allowed students to spend their
summer breaks working on real projets, ypclly in
‘haleging wan environment ke New York Cy
Many students were inspired to pursue fling
aces in operations research and related subjects
ter being exposed to working on eld operational
“iles

012

Mentorship, to me, is
about encouraging
curiosity, embracing
failure as part of the

process, and never
letting complexity

scare you away from
a problem worth

Reforming 911 from the Inside Out

Richard Larson played a crcl role in ovehauing
New York Cay emergency cal system, tuming 2
once soie process no a far mor responsive
tnd eficient operation. Before the 91 system was
implemented, New Yorkers were required to dal
diferent numbers based other borough — a
Arrangement tht often Io confusion and costly
delays in times of eis Even afer the centrale
511 serve was induced, significant sues
remained, paula with long caller wait ies

To tackle these persistent inficiences, Richard
armer rely with NYPD lieutenants and
spich tame o investigate the underiying
problems. Through detailed data analy nd hands:
fn collaboration, hepinpoinied weaknese in
‘operator deployment and scheduling. His
‘commendations ed t significant operations
anges that dramatically shortened response times.

‘While and data onthe exact life-saving outcomes
may not exist, Richard is onfident tha these
improvement had a meanigfl impact on publie
safe Perkaps just as import, he metic
record the sages and systems sed during the

sew businessmi

18. lead 1 more thoughtful five
décisions in dal ie

y and Pr

Driving Change nthe Pu

Sector

emi is been
atively translated ino practical
improvements for major public

roles with organizaios such as he
US. Postal Service and New York
Ch municipal services, Richard has
helped tum complex theoretical
ing into tangible advance in
resource allocation, service delivery,
and operational strategy His ability o
clearly communicate tecnica! ideas 10
audiences hs made him a traste

Advaneing the Discipline

Beyond individual projet, Richard
Tas played a majo oe in shaping the
broader Rel of operations esearch. As
former president ofboth ORSA and
building a song. collaborative
profesional community. His os
have supported knowledge exchange,
innovation, and global engagement in
‘complex challenges, His leadership has
been recognized though multiple
ward for both research excellence

A Lasting Legacy of Thought and
Action

Richard Larson impact endures
through the stents es mentored, the

‘he institutions he has helped evolve
His dedication to bending academie
theory wit real-world relevance, Bis

conten to leame centred
‘duction, and is belie in sing
technology wisely conn to influence
bash chola disciplines and publie
systems worldwide

[As the els of analytes, systems
engineering and educational
Richard work remains a guiding light.
is creer demonstrates that the most
meaning breakthroughs happen
Science ome together and hat rs
legacy is built ot jst on knowles,

bon purpose EE

19. ‘ye bosinessmindamedio.com

20. Algorithms of the City

Data-Driven

Models
Public

I: he it bum of ity ie,
beneath the talc ight, ies,
cto schedules, and garbage
Fouts algorithms ar hard a work
They're oo always visible, ut hey
rip decido where ambulances go, how
talc lights change, when bsos
anis, and even how schools and
ost allocate resoures. As ites
become increasingly complex and
denslspopulted, dt drive
model rte in operations research
and systems analytes are stepping in
A powerful wos to improve eieieny,
qui, and esponsiveness in publie

The Rise ofthe Smart City

The em "sat iy" often conjees
images of arise bald,
autonomous ars, and sensors
‘beled in every comer But the ral
choc of sat cit à ab o
make inteligent decisions based on
realtime data. This shit is powered by
lgorhns ha proces enomous
Stream of information and comer
‘hem into actionable insight

018

These algoritims some powered by
Al others by traditional optimization
Techniques. aer tbortcal
experiments They are atively
reshaping how ies function. From
sanitation to emergeney respons, they
are helping governments alo urban
halenges with speed and precision,

Managing Urban Trac

Tr congeson one ofthe most
visible —and frustrating urban
halleges Traditional, aie Now
vs managed using ned schedules or
ira signals and edocated guesses
sed on historical dt. But now
cits tke Los Angles and Singapore
‘reaping adaptive wai contol
System that repond o reine
conditions,

These stems use machine lemming
lgorithns and queing models to,
adjust signal ming reroute vehicles,
and prevent haulenccs before they
‘evr The result? Reduced congestion
Shorr asl times, nd lower

Improve

Services

‘missions Algoritims are also being
‘od to manage desing ets nd.
‘sorte publi trepa
Schedules, creating a more seamless
nd ingre urban mobility
experience.

Emergency Services Every Second
Counts

In publi safe, every moment matters

Dateien models help emergeney
services“ police, fe, and medial
teams respond more quickly and
sffecively. Algorithms can predet
hand ones using historical
incident data, enabling proactivo
stationing of ambulances or ptr! can.

Queusing theory foundations
amet in operations research, helps
balance resource allocation across
cighorhoods fo ensure optimal
‘overage and minimize esposo times.
In New York City or instance,
improvements in IL operations and
space algorithms guided by
‘da-—havesignficandy improved
emergency response perio,

vw businessmindsmediaicom

22. Waste Management and Uses:
Cleaner, Smarter Citi

Even systems as routine us garbage
collection and water sage Beef rom
Sart modeling. Cites tke
CCopcnagen and Amsterdam use
seasoregupped bias to detect il
leves allowing collection routes tobe
dynamically updated. This minimizes
noces tps, roces fuel
«cosumpon, and saves publie money

Antes, predictive model forecast
energy or water usage spikes based on
Sehr, tne o day nd historical
mand. These insights enable ui
Provides 1 balance lod, prevent

otages, and manage costal while
more ing wana
Education and Health: Equity

Through Algorithms

Beyond physical infrasructrs,
gorse also improving human
‘centered services like eduction and
ele. For example, school ise
Planning canbe optimized using
Brographic and demographic daw»
falancecorolinet, duce
onerrowding. and improve acess 10
‘galt education,

In public health ces ar increasingly
‘sing real-time analytes to detect
breaks, tack vacation its, and
‘epoy mobile clinic where they ae
sede most During the COVID-19
Pandemic, dau-diven modas payed
Crucial mie in dtemining hospital
capaci esting locations, and vaccine
raton satgis,

this, Equity, and Transpareney

However. te growing role of
algontlms in ety governance mios
importan questions: Who designs
thew algorithms? What data are they
trained om? And do they reinforce

020

existing inequities? Without carta
verga ven wllintenioned
‘models can unintentionally prorize
eficieny over fines.

Forinsance, predictive policing
algorithms have come under re for
perpetatin Mas when tained on
Bisorialiy skewed data. Similar
resource allocation models must
account or marginalized communis
That may ot generate as much digital
ata ut have rene neds.

Toadies his, cis mus prie
tica! modeling, eur rampareney
in algorimi decision-making, and
include community vices inthe
sen process Publ sees should
serve even one not jun those easiest
to quan

“Toward a Model-Driven Public
Sector

‘The future of urban governance not
Jus digitalis algorithm. But
success depends not only on smart
technology, ut alo on smart
leadership. Publi oil must be
‘educated in data ec, and
inerisciplinary teams — combinig
‘engineers, urban planes, sociologists,
“and ici should pide
Implementation

More importa the public must
understand and us te models hat
Shape thei Ines This means making

lait interpretbl,aodiable and

aapiable—so they canbe reinos.
‘onions cvolve and as commun
Values shit

Conclusion: Modeling a Better
Tomorrow

As urban populations grow and
institue stains under pressure

the demand for imeligent response,
and equitable publi services il nl

increase, Data-drven modes are no
longer aris au hey ae à
ive noes

From routing ambulance 1 reducing
pollaion, optimizing classroom to
Siting water demand. algoritmo
Sethe insbe engines making
‘moder cities livable, scalable, and
‘more humane, The challenge now ito
‘wil this power with responsibility.
ring ha technology
nhances no rplacos human
Judgmer and pal ccoumabiiy

"he algoritmo the iy are here to
Sa. The question is: Can We design
‘het not uso optimize DE systems,
Tutto clero our socie Zas

‘www businessmindsmedi.com

24. f © in " —

25. www.businessmindsmedia.com

66

dá DON'T WATCH THE
CLOCK, DO WHAT IT
DOES. KEEP GOING.

Son Lom

26. Beyond the tine

|

Queueing Theory |

|
Public
Service

have all waited in ‘randomness, variability, and imite
Tine at the bank. resources, aiming to optimize the
‘naff, at hospital. orn perfomance of ystems under

Pod witha customer service center pressure,

‘These moment, though oes

frustrating, are more than jos pauses in — Developed initial 10 solve problems

our dy. They arereflecions af how in elcommunications queueing

Systems function, how resources are theoy has evolved into aeitcl

allocated and how imei valued.
Behind every queue is decision Balhcare, ransporation, al
making proces And behind those emergency management. value es
decisions es a powerfl oo: ins bla to predict botleneks,
‘queueing theory. improve service delivery, and ensure

that nite ewurces are wed inthe
‘Tough it may sound echnical manes possible way:

aucuns theory à ne of the ost
Practical lds in appli mathematics Healthcare: More Than Just

and operations records the Waking Room

Formation, behavior, and management

o ques, But more than ta, it helps In hospitals and eins the takes of
a vi dr pre ol Ca

testes tea Dance

see eee Y |
es [|

vanonsenon CEE
reine J

Atits core, queuing theory isthe minimize wait mes while maximizing
science of waiting nes. I models how care quali.

emite people, vehicles, data L
puts, o requess area Fersen deparment in panico
Service point, how they are porized, re on these models 1 eine cases
ow long they wait and how quickly based on urgency and availability of
they are served I factors in ‘aff or equipment. During the COVID-

(fe

024

27. ¡q E da

28. 19 pandemic, queveing models payed
val role helping hospitals plan

U capacity, velar allocation, and
‘esting ste throughput. When resources
‘elimi and the nod is great.
‘ater aise become form
of public je

“Transportation: From Gridlock to
Flow

{Urban transportaron systems are
haps the most visible maifsiations
‘of queueing in everyday ie. Trac
congestion subway delays, bus wait
mes are nlcnced by quening
names. By modeling imersctons,
bici ow, and rider demand, cis
cam better manage trans schedule,
reduce congestion, and improve
Sommersaison.

Adaptive rai signal contro systems
te etme quese data adj light
puters dynamical allowing
mer ows and rend wit
times In public ran, quucing
models ep plannersdetemine where
to dd capaci, how to optimize
routes, and how to mai service
during peak demand or disruptions.

Emergency Services Saving Time to
Save Eee

For fire deparmens, police unis, and
ambulance serves, every second
saved in response ime can mean the
drone Been fe and death,
‘Qucucing theory ass optimizing
the placement of emergency vehicles,
Fulancing werkioods among
dlsgtchers, and planning coverage
Across eighborbods.

By simulating dire scenarios He
surge in 911 cal during astral
dater public agencies cantes
response strategies ad improve
resture rediness One ofthe mos
transformative examples ofthis was

026

the redesign of New York Cis 911
call tem which used queue
models to sed oldimes and
impr emergency coordinan

‘Government Services Ein, aie,
and Transparent

From visa applications to tx oes,
queucin theory is also shaping the
Experience eens engnging ith
nement services Long lies at
Public agencies ca signal deeper
Inelficienciesunderstaing, outdated
‘scheduling systems or poor
‘nator, Model that prods peak
ours, simulate demand paies, and
‘optimize sang levels ean
Samuel reduce publi rro,

Viral queues and appoiniment-tased
services powered by queueing
gorithms, re now replacing
triton ins, fering ser
transgureny and pretability These
improvement not oly boost
tion but also reist a more
respect ad inclusive approach o.
publi serie,

Beyond Een: ties ln
‘Queueing

White queueing theory i 101 or
ciety ls ass import
‘ial questions. Who gts served
Fast? How are rites determined?
Can opimizing speed come at the ost
fines?

In hallar, should younger pair
a priori in rica are? In
‘ansportation should wali ystems
priori uses over pese cars In
aovemment services, how do we
naar qual aves fo those ith
‘habe or mid digital tery?

‘These questions show that queueing la
not jst a technical issue sa
foci one, And the best qucuing

‘ystems don't simply reduce wait
fies: hy elect ales of the

iil and serves
queucing theory is voli
tine dt rom sensor,
and cloud platoons al
‘dynamic queuing model

Aria intelligence anal
Ieaming re bin à

them entre
Conclusion: More Than a Line

‘Queueing cory may tart wit nes,
tite vel sc systems that
noch every pat diy ie. I

‘poser pull Renan 10 make

peopl tie for gu
fe hohl d
‘of publi sevice, ars al
sanding ina FF

ww businessmindsmedio.com

30. ‘Smarter Systems for a Faster World

The Future of

Operations Research
in a Real-Time

nan age defined by immediacy,
tuber decisions are made in
milseons and dat flows in.
‘omens, te Held of Operations
Research (OR) sands trial
into point Tradl rooted in
‘pining comple systems though
‘mathematical modes, OR Bas long
ben behind-the-scenespoerhous,
uty shaping military login,
Public hal responses, nd industrial
‘ciency. Bu the ur so longer
shout sl, enospeth any. KS
about rate adaptabiit
rnamie decision-making and
Amar seleorrecting systems

Anew Pace for an Old Science

Operations Research emeged in the
‘mid 20th century as tee
Ssenee— deployed in Word War to
ini adr placement convoy
movement, and resource locaton
‘Overtime, spre to manufacturing
chere, wansporain, and pie
services, Bat for mich of is histo
OR operated ona etrospocive mode
ther dt, bi a model, slate
Scenario, and then implemen pln

In today’ digital world, tat cadence
reis oundatd. We now live ina ea
time word where GPS reos us
rmid-commute, emergency rooms

028

World

reprit patents a dat updates,
fad supply hais shi esta based
‘on weiter, labor shortages, or global
“man The future of OR es in
Abandoning the waitand

nd embracing one thats

Time Solutions

{What makes he fre oF OR so
promising and so challenging isthe
Seer volume. speed, nd variety of
valable da, From lo sensor in
Sart cit to action logs ine
‘Sommerce platforms, ih data sears
ever sop. This explosion of data
Incas ht operations esearch must
ow funtion more ike a nervous
‘Sate than a Bling cabinet

Take urban mabiliy systems, for
instance Trafic optimization once
‘meat simulatng pair and
adjusting light exes every few
monts. Now, cites cod OR models
that ingest elie senso data, tet
‘anomalies instal and recalibrte
tate ia on they. In such

ay o adaptive, data ein control

Healthcare i another example
Hospital capacity planning has ways

involved complex modeling Balle
COVID-19pandemie showed how
‘quickly condón an change
Forecast ult on ast eek numbers
‘sere rendred oboe in hous The
‘ow one fr OR is hele 8
predictive resource allocation that
updates by the minute mtching
ints eds staf and equipment i
sever changing environment

Marrying OR wi
Learning

AL and

To keep pace th dis restes,
Operations Research à ieressingly
moine wi arial intelligence
(AD and machine terning (SIL)
“This nen rnsfoming
transl OR models rom ii ool
ino adapine temo

Machin leasing provides he
price honcpower recognizing
tem and anomalies san datasets
‘OR. on th other hand, supplies the
‘sion logicestublishing
‘Somat, optimizing wade and
‘ring system elicieno, Together
{hey enable prescripive ana“

‘ot jot forcasing What might happen.
‘but ecommending the best couse of
ion in that moment

sr businessmindsmedia.com

31. EL ROK IASC
Sa INU AE
Je” AV tea

32. In warehouse logistics, frexample, act more lik copilots—surfacing

realtime inventory levels ean be insights suggesting actions, and

continuously monitored and paired allowing humans make the nal cll
D Si ML gore a reir
‘demand surges. OR models hen Looking Ahead: Real-Time Etes
‘optimize sang, outing and and Eq
restocking strategies
> “dynamic delivering precision and ASOR becomes faster and more
‘ficeney simultaneous ‘Powerful new ethical questions as,
cal ime systems can optimize for

The Rise of Streaming Analytics spa ad icin but what about

~ f is and ranareney? How do we
Aie her of realtime OR isa ensure that optimization does
technology called streaming spropoionteiy disadiamage

analytics Ah ability to proces and vulnerable populations?
analyze daa in mein. Uk

{radial bt processing, which Forexample, an emergeney response
Shales data chunks air tps a Bed on

been collected, steaming anales pates might
gras data ie moment ars. unimentonaly underserve

hi lou operations systemsto maialize comme the input
respond in velltime, sometimes even data rec past inequities. The faute
‘before a human can intervene, FOR must therefore integrate clica

‘modeling bis detection, ad equity

For OR profesoras thismesns aware optimization a core

building models at ae notomiy component nt allerhaught

accurate ut shit, fs a
but under constant change Italso Conclusion: A Living Discipline
als fr closer collaboration with data

Sngineer andsystem architects who Th futur of Operations Reseach is
"ga e aer to dle esl) confined to si ter one
time ows, eur simulations Its exolving into

Team, inlignt decision

Essential engine one that continuously aap,
Teams, and cs This shift demande ot

Despite es tecnologica advances, — Jus tr lois, but a new

te future o operations esearch sot” minde: one hat embraces une,

purely autonomes. The most effective ui and constan feedback.

Stems sil incorporate uman-inhe-

uman-in-the-Loop: Sil

Toop decision-making While OR in acattime word iso longer a
gorithms may dee ptr or back discipline. Isa uni
Anomale, human judgment romains ool freien, aii nd
‘ctl fr content sand Innovation And systems grow more
inerpreing ambiguous stations. Complex andimerconneee ls

Ämperincs will ony rows
“his is especially ts in aras like

public policy disaster response, or

‘medical age where takes ae high

and the data may be incomplete or

biased OR systems inthe future wi

rn businessmindsm

RS ew pat

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