Business Analytics for Flexible Resource Allocation Under Random Emergencies

Mallik Angalakudati (), Siddharth Balwani (), Jorge Calzada (), Bikram Chatterjee (), Georgia Perakis (), Nicolas Raad () and Joline Uichanco ()
Additional contact information
Mallik Angalakudati: Pacific Gas and Electric Company, San Ramon, California 94583
Siddharth Balwani: BloomReach, Mountain View, California 94041; and Leaders for Global Operations, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Jorge Calzada: National Grid, Waltham, Massachusetts 02451
Bikram Chatterjee: Pacific Gas and Electric Company, San Ramon, California 94583
Georgia Perakis: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Nicolas Raad: National Grid, Waltham, Massachusetts 02451
Joline Uichanco: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Management Science, 2014, vol. 60, issue 6, 1552-1573

Abstract: In this paper, we describe both applied and analytical work in collaboration with a large multistate gas utility. The project addressed a major operational resource allocation challenge that is typical to the industry. We study 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 has maintenance crews that perform both standard jobs (each must be done before a specified deadline) as well as respond to 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 maintenance 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 that is motivated by the structure of an optimal solution. We used our models and heuristics to develop a decision support tool that is being piloted in one of the utility's sites. Using the utility's data, we project that the tool will result in a 55% reduction in overtime hours. This paper was accepted by Noah Gans, special issue on business analytics .

Keywords: resource allocation; stochastic emergencies; scheduling; gas pipeline maintenance; utility; optimization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:60:y:2014:i:6:p:1552-1573

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