A Comparison Between Optimization Tools to Solve Sectorization Problem

In sectorization problems, a large district is split into small ones, usually meeting certain criteria. this study, at first, two single-objective integer programming models for are presented. Models contain sector centers and customers, which known beforehand. Sectors established by assigning subset of customers to each center, regarding objective functions like equilibrium compactness. Pulp Pyomo libraries available in Python utilised solve related benchmarks. The problems then solved using genetic algorithm Pymoo, library that contains evolutionary algorithms. Furthermore, the multi-objective versions with NSGA-II RNSGA-II from Pymoo. A comparison made among solution approaches. Between solvers, Gurobi performs better, while case setting proper parameters operators Pymoo better terms time, particularly larger