Application of the Fix-and-Optimize Metaheuristic to Optimize the Allocation of Service Technicians in Electric Power Utilities

Abstract

The effective allocation of technical teams to fulfill service orders is a significant challenge for electric utility companies, as balancing capacity and demand is difficult given the system's dynamic nature. As a result, operating costs and service quality, as defined by the frequency and duration of electricity supply interruptions, determine the outcomes of the operating indices measured by regulatory agencies. This paper presents a solution approach, using the Fix-and-Optimize (F&O) meta-heuristic, for a mathematical model developed based on concepts from the backlogging technique and Mixed Integer Linear Programming (MILP), to reduce the number of orders delayed by unstarted and unfinished services, through the optimized distribution of technical teams. The F&O meta-heuristic iteratively partitions the binary allocation variables, fixing 30% based on adaptive criteria and optimizing the remaining variables. For the simulation, an instance of 5,830 orders recorded over a period of 90 days was used; five capacity scenarios were analyzed using meta-heuristics, with incremental return on investment (ROI) measured. The results showed significant improvements, with costs varying from 3.8% with the initial model to 17.5% with F&O. Additionally, the trade-off analysis developed allowed us to determine the ideal configuration for the “Normal Team with 4 hours of overtime/day,” with a total cost of USD $8600, a service rate of 92%, and an incremental ROI of 1.75. The proposed approach offers quantitative support for planning the allocation of human resources in contexts of providing technical services related to electricity.