An improved GA approach for distribution system outage and crew scheduling with Google maps integration

In this paper an improved genetic algorithm (GA) approach is proposed to find the optimal solution of crew and outage scheduling of distribution systems with integration of Google maps. Various types of engineering teams with different get-in and get-off times to the fields are considered. The fitness function is to minimize the engineering days, the outage loading, the difference of working time among the crews, and the distances of routings. Improved crossover rules and a weighted dynamic mutation method are presented. The transportation time and distance obtained from Google-Maps are integrated in the scheduling approach. Smartphones are exploited in the fields to communicate with the dispatching center with the scheduling displayed on the Google-Maps. Simulation results for a sample distribution system are performed to demonstrate the effectiveness of the study.