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Not-so-risky business: New computational models will offer decision support

February 15, 2010

A customer on hold for a long time with a call center support person probably feels somewhat frustrated. A call center manager would likely prefer to avoid this customer response, but the answer isn’t necessarily as straightforward as scheduling additional call-takers for the next day. The manager has to balance the costs of additional employees with the risk of customer dissatisfaction, and without knowing exactly how many calls to expect the next day, it can be almost impossible to achieve that balance.

Jim Luedtke, an assistant professor of industrial and systems engineering at the University of Wisconsin–Madison, is working on a new set of tools to help decision-makers in such settings. He has received a prestigious CAREER award from the National Science Foundation and a five-year, $400,000 grant to research risk modeling and computational optimization for decision support.

“I look at settings where complex decisions must be made in the face of uncertain outcomes,” he says. “I’m studying new models to allow decision-makers to specify their level of risk.”

Current computational approaches to decision-making suggest solutions for the best outcome on average. For example, standard tools could produce a call center workforce schedule that results in a small number of unhappy customers on an average day. However, the schedule may not help the center be equipped to deal with days that have an abnormally high number of calls, and therefore an abnormally high number of unhappy customers. This may be an unacceptable risk for the manager.

Luedtke is working to develop new algorithms in a field known as stochastic programming to specifically address uncertainty in decision-making settings, while allowing for individual preferences for risk. These new algorithms, which will address constraints that limit the probability of bad outcomes, will offer alternative solutions to the best-on-average solutions produced by current models.

“When you aren’t making a decision thousands of times, being best on average doesn’t matter to you,” explains Luedtke. “If a decision-maker has just one shot, she may be willing to give up making a choice that is best on average to reduce the risk of being one of the bad outcomes.”

Luedtke’s methods could have a broad range of applications in fields such as medicine, business and finance. Physicians could use the computational tools to help design effective, individual radiation treatment plans for cancer patients, and government agencies could use the tools to choose cost-effective disease control policies. Businesses could design effective supply-chain management and workforce staffing schedules. Financial planners could help people develop investment portfolios tailored to their personal levels of acceptable risk.

Luedtke is also working to increase the number of decision-makers who use computational optimization — a priority that fits well with the CAREER award emphasis on creative projects that effectively integrate advanced research with outreach and education. He will work with the Wisconsin Center for Academically Talented Youth to educate middle- and high-school students about how optimization strategies, and advanced math in general, can solve real-world problems.

Luedtke also will incorporate concepts about decision-making in the face of uncertainties into an undergraduate engineering course and develop a new graduate course about stochastic programming models and computational tools. These courses will reinforce critical-thinking skills and help students learn to question the assumptions underlying the mathematical models.

“I want this research to help decision-makers, but this will only have an impact if people use the models,” says Luedtke.

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