IBM Math Algorithms Aim to Transform Management of Natural Disasters

Stochastic optimization model to help fight and contain fires, floods and more


New Delhi, Bangalore — April 3, 2008 — IBM today announced that its scientists have created specialized math algorithms to help model and manage such natural disasters as wildfires, floods and diseases, adding to companies' supply chain risk management capabilities.

IBM said its "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 explained that its 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 and societal problems while using open platforms and standards. Our researchers have worked on optimization solutions designed to create a roadmap for a responsive disaster risk reduction," said Dr. Daniel Dias, director, IBM India Research Laboratory.

The deployment of resources during a natural disaster, whether that is water, food, machines, people or something else, requires complex planning and scheduling and the need to adapt to constantly changing scenarios, often involving large numbers 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. IBM said a fully developed, customized and implemented model can 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," said 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."

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