DELHI, INDIA and BANGALORE, INDIA–( EMWPresswire – April 1, 2008) – IBM (NYSE: IBM) today

announced that its scientists have created specialized math algorithms to

help model and manage natural disasters: wildfires, floods, diseases and

more.

IBM’s ‘stochastic optimization model’ was developed by IBM math scientists

from IBM Research Labs in New York and India working with business experts

from IBM’s Global Business Services and directly with clients to arm

government bodies, relief agencies and companies with tools for strategic

planning for more effective allocation of resources for natural disaster

management and mitigation.

IBM’s math team works on seemingly unsolvable problems in business,

government and society. Mathematics underlies just about everything that

happens in the world, and many big societal and business issues can be

attacked by understanding the math, and using it to give much deeper

insights into data of all sorts. The mathematicians magic potion is bottled

up in complex algorithms — essentially math equations that help speed up

and simplify complex tasks into everyday life — such as determining the

fastest route to deliver packages, detecting fraud in health insurance

claims, automating complex risk decisions for international financial

institutions, scheduling supply chain and production at a manufacturing

plant to maximize efficiency or detecting patterns in medical data for new

insights and breakthroughs.

“The challenge lies in matching high-end mathematical programming

technologies with high-impact business & societal problems, while using

open platforms and standards. Our researchers have worked on innovative

optimization solutions designed to create a roadmap for a responsive

disaster risk reduction,” says Dr. Daniel Dias, Director, IBM India

Research Laboratory.

The deployment of resources during a natural disaster, whether it be water,

food, machines, people or something else, requires complex planning and

scheduling and the need to adapt to constantly changing scenarios, often

involving large number of resources, unique requirements based on location

and the varying staffing levels associated with each resource. Government

agencies use different systems to estimate their program needs, including

preparedness resource planning, yet no one system has been able to adapt to

the increasing complexity of natural disaster management.

These challenges resulted in IBM developing a large-scale strategic

budgeting framework for managing natural disaster events, with a focus on

better preparedness for future uncertain disaster scenarios. The underlying

optimization models and algorithms were initially prototyped on a large

U.S. Government program, where the key problem was how to efficiently

deploy a large number of critical resources to a range of disaster event

scenarios. That system generated a single solution for each disaster

scenario. The current enhancements to the budgeting system include the

development of simulation models to assess and evaluate the impact of

alternative strategies based upon criteria selected by the client.

Optimization allows the client to trade off among multiple priorities to

understand the impacts to performance measures.

The same models can be explored to manage floods or famines in India, or

natural disasters anywhere in the world. A fully developed, customized and

implemented model can significantly help the country’s approach for

disaster risk reduction and disaster management.

“We are creating a set of intellectual properties and software assets that

can be employed to gauge and improve levels of preparedness to tackle

unforeseen natural disasters,” says Dr. Gyana Parija, senior researcher and

optimization expert at IBM India Research Laboratory, New Delhi. “Most

real-world problems involve uncertainty, and this has been the inspiration

for us to tackle challenges in natural disaster management.”

In the case of flooding, for example, the stochastic programming model

would use various flood scenarios, resource supply capabilities at

different dispatch locations, and fixed and variable costs associated with

deployment of various flood-management resources to manage various risk

measures. By assigning probabilities to the factors driving outcomes, the

model outlines how limited resources can meet tomorrow’s unknown demands or

liabilities. In this way, the risks and rewards of various tradeoffs can be

explored.

Stochastic programming offers greater modeling power and flexibility, but

it comes at a cost-premium processing time. However, recently, stochastic

programming has benefited from the development of more efficient algorithms

and faster computer processors. This means that rather than predicting a

limited future using forecasting, decisions supporting a wide range of

probable scenarios can be taken. The model allows all unforeseen

challenges to be solved, mostly within an hour, and has very good

scalability that promises to gracefully manage even larger models in the

future.

“What we have been able to accomplish is to make such innovative

optimization solutions accessible and affordable to a wide spectrum of

clients operating in diverse socio-economic environments,” says Tarun

Kumar, an optimization researcher at IBM’s T.J. Watson Research Center in

Yorktown Heights, New York.

As stochastic models become more sophisticated, researchers like IBM’s Dr.

Gyana Parija have been able to infuse the models with “human” factors, such

as politics, custom and culture. As researchers factor in human behavior in

the models, the results grow less uncertain and more accurate and

acceptable.

About IBM India Research Laboratory

The IBM India Research Laboratory (IRL) was established in April 1998 in

New Delhi as the eighth of IBM’s research labs. The lab expanded to its

second site, Bangalore, in August 2005. IRL has a world-class team of

researchers who have been recognized for their thought leadership and have

developed innovations that help differentiate IBM both in the region and

globally. For more information, please visit

http://www.research.ibm.com/irl/.

For additional information contact: Steven Tomasco IBM Media Relations 914-945-1655 stomasc@us.ibm.com