ORIE Colloquium: Joline Uichanco (MIT) - Business Analytics for Flexible Resource Allocation Under Random Emergencies Thursday, January 24, 2013 at 4:15pm Frank H. T. Rhodes Hall, 253 In this work we describe both applied and analytical work in collaboration with a large multi-state gas utility company. The project addressed a major operational resource allocation challenge that is typical to the industry and to other application domains. In particular, we developed analytical decision support tools to address the resource allocation problem in which some of the tasks are scheduled and known in advance, and some are unpredictable and have to be addressed as they appear. The utility company has maintenance crews that perform both standard jobs (each must be done before a specified deadline) as well as repair emergency gas leaks (that occur randomly throughout the day, and could disrupt the schedule and lead to significant overtime). The goal is to perform all the standard jobs by their respective deadlines, to address all emergency jobs in a timely manner, and to minimize maintenance crew overtime. We employ a novel decomposition approach that solves the problem in two phases. The first is a job scheduling phase, where standard jobs are scheduled over a time horizon. The second is a crew assignment phase, which solves a stochastic mixed integer program to assign jobs to service crews under a stochastic number of future emergencies. For the first phase, we propose a heuristic based on the rounding of a linear programming relaxation formulation and prove an analytical worst-case performance guarantee. For the second phase, we propose an algorithm for assigning crews to replicate the optimal solution structure. We used our models and heuristics to develop a web-based planning tool for the utility which is currently being piloted in one of the company's sites. Using the utility's data, we project that the tool will result in 55% reduction in overtime hours. This represents potential savings for the company of $66 million per year, which are considered dramatic. Bio: Joline Uichanco is a Ph.D. student at the MIT Operations Research Center. Her advisors are Retsef Levi and Georgia Perakis. Her current research involves the design and analysis of data-driven heuristics for stochastic optimization models in scheduling, inventory management, supply chain management, and finance. She also has work inspired by collaborations with major companies on developing decision frameworks for settings with high uncertainty and risk.