A comparison between optimization tools to solve sectorization problem

Aydin Teymourifar*, Ana Maria Rodrigues, José Soeiro Ferreira, Cristina Lopes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In sectorization problems, a large district is split into small ones, usually meeting certain criteria. In this study, at first, two single-objective integer programming models for sectorization are presented. Models contain sector centers and customers, which are known beforehand. Sectors are established by assigning a subset of customers to each center, regarding objective functions like equilibrium and compactness. Pulp and Pyomo libraries available in Python are utilised to solve related benchmarks. The problems are then solved using a genetic algorithm available in Pymoo, which is a library in Python that contains evolutionary algorithms. Furthermore, the multi-objective versions of the models are solved with NSGA-II and RNSGA-II from Pymoo. A comparison is made among solution approaches. Between solvers, Gurobi performs better, while in the case of setting proper parameters and operators the evolutionary algorithm in Pymoo is better in terms of solution time, particularly for larger benchmarks.
Original languageEnglish
Title of host publicationModelling, computation and optimization in information systems and management sciences
Subtitle of host publicationproceedings of the 4th international conference on modelling, computation and optimization in information systems and management sciences - MCO 2021
EditorsHoai An Le Thi, Hoai Minh Le, Hoai An Le Thi, Tao Pham Dinh
PublisherSpringer
Pages40-50
Number of pages11
ISBN (Print)9783030926656
DOIs
Publication statusPublished - 2022
Event4th International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - Hanoi, Viet Nam
Duration: 13 Dec 202114 Dec 2021

Publication series

NameLecture Notes in Networks and Systems
Volume363 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference4th International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences
Abbreviated titleMCO 2021
Country/TerritoryViet Nam
CityHanoi
Period13/12/2114/12/21

Keywords

  • Gurobi
  • Optimization
  • Pulp
  • Pymoo
  • Pyomo
  • Sectorization

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