Title
Business analytics for flexible resource allocation under
random emergencies
Presented by
Joline Uichanco
Abstract
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.
Biography
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.