A comparison between optimization tools to solve sectorization problem

Aydin  Teymourifar; Ana Maria Rodrigues; José Soeiro  Ferreira; Cristina  Lopes

doi:10.1007/978-3-030-92666-3_4

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 proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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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 language	English
Title of host publication	Modelling, computation and optimization in information systems and management sciences
Subtitle of host publication	proceedings of the 4th international conference on modelling, computation and optimization in information systems and management sciences - MCO 2021
Editors	Hoai An Le Thi, Hoai Minh Le, Hoai An Le Thi, Tao Pham Dinh
Publisher	Springer
Pages	40-50
Number of pages	11
ISBN (Print)	9783030926656
DOIs	https://doi.org/10.1007/978-3-030-92666-3_4
Publication status	Published - 2022
Event	4th International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - Hanoi, Viet Nam Duration: 13 Dec 2021 → 14 Dec 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	363 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	4th International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences
Abbreviated title	MCO 2021
Country/Territory	Viet Nam
City	Hanoi
Period	13/12/21 → 14/12/21

Keywords

Gurobi
Optimization
Pulp
Pymoo
Pyomo
Sectorization

Access to Document

10.1007/978-3-030-92666-3_4

40015510Accepted author manuscript, 310 KBLicence: Unspecified

http://hdl.handle.net/10400.14/36554Licence: Unspecified

Cite this

Teymourifar, A., Rodrigues, A. M., Ferreira, J. S., & Lopes, C. (2022). A comparison between optimization tools to solve sectorization problem. In H. A. Le Thi, H. M. Le, H. A. Le Thi, & T. Pham Dinh (Eds.), Modelling, computation and optimization in information systems and management sciences: proceedings of the 4th international conference on modelling, computation and optimization in information systems and management sciences - MCO 2021 (pp. 40-50). (Lecture Notes in Networks and Systems; Vol. 363 LNNS). Springer. https://doi.org/10.1007/978-3-030-92666-3_4

Teymourifar, Aydin ; Rodrigues, Ana Maria ; Ferreira, José Soeiro et al. / A comparison between optimization tools to solve sectorization problem. Modelling, computation and optimization in information systems and management sciences: proceedings of the 4th international conference on modelling, computation and optimization in information systems and management sciences - MCO 2021. editor / Hoai An Le Thi ; Hoai Minh Le ; Hoai An Le Thi ; Tao Pham Dinh. Springer, 2022. pp. 40-50 (Lecture Notes in Networks and Systems).

@inproceedings{781f436bff7d4bd2b38aafab8b643969,

title = "A comparison between optimization tools to solve sectorization problem",

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.",

keywords = "Gurobi, Optimization, Pulp, Pymoo, Pyomo, Sectorization",

author = "Aydin Teymourifar and Rodrigues, {Ana Maria} and Ferreira, {Jos{\'e} Soeiro} and Cristina Lopes",

note = "Funding Information: This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Funda¸c{\~a}o para a Ci{\^e}ncia e a Tecnologia within project POCI-01-0145-FEDER-031671. Funding Information: Financial support from Funda??o para a Ci?ncia e Tecnologia (through project UIDB/00731/2020) is gratefully acknowledged. This work is financed by the ERDF-European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation-COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT-Funda??o para a Ci?ncia e a Tecnologia within project POCI-01-0145-FEDER-031671. Funding Information: Acknowledgements. Financial support from Funda¸c{\~a}o para a Ci{\^e}ncia e Tecnologia (through project UIDB/00731/2020) is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 4th International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences, MCO 2021 ; Conference date: 13-12-2021 Through 14-12-2021",

year = "2022",

doi = "10.1007/978-3-030-92666-3_4",

language = "English",

isbn = "9783030926656",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "40--50",

editor = "{Le Thi}, {Hoai An} and Le, {Hoai Minh} and {Le Thi}, {Hoai An} and {Pham Dinh}, Tao",

booktitle = "Modelling, computation and optimization in information systems and management sciences",

address = "Germany",

}

Teymourifar, A, Rodrigues, AM, Ferreira, JS & Lopes, C 2022, A comparison between optimization tools to solve sectorization problem. in HA Le Thi, HM Le, HA Le Thi & T Pham Dinh (eds), Modelling, computation and optimization in information systems and management sciences: proceedings of the 4th international conference on modelling, computation and optimization in information systems and management sciences - MCO 2021. Lecture Notes in Networks and Systems, vol. 363 LNNS, Springer, pp. 40-50, 4th International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences, Hanoi, Viet Nam, 13/12/21. https://doi.org/10.1007/978-3-030-92666-3_4

A comparison between optimization tools to solve sectorization problem. / Teymourifar, Aydin ; Rodrigues, Ana Maria; Ferreira, José Soeiro et al.
Modelling, computation and optimization in information systems and management sciences: proceedings of the 4th international conference on modelling, computation and optimization in information systems and management sciences - MCO 2021. ed. / Hoai An Le Thi; Hoai Minh Le; Hoai An Le Thi; Tao Pham Dinh. Springer, 2022. p. 40-50 (Lecture Notes in Networks and Systems; Vol. 363 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A comparison between optimization tools to solve sectorization problem

AU - Teymourifar, Aydin

AU - Rodrigues, Ana Maria

AU - Ferreira, José Soeiro

AU - Lopes, Cristina

N1 - Funding Information: This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Funda¸cão para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-031671. Funding Information: Financial support from Funda??o para a Ci?ncia e Tecnologia (through project UIDB/00731/2020) is gratefully acknowledged. This work is financed by the ERDF-European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation-COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT-Funda??o para a Ci?ncia e a Tecnologia within project POCI-01-0145-FEDER-031671. Funding Information: Acknowledgements. Financial support from Funda¸cão para a Ciência e Tecnologia (through project UIDB/00731/2020) is gratefully acknowledged. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Gurobi

KW - Optimization

KW - Pulp

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KW - Pyomo

KW - Sectorization

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SN - 9783030926656

T3 - Lecture Notes in Networks and Systems

SP - 40

EP - 50

BT - Modelling, computation and optimization in information systems and management sciences

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A2 - Le, Hoai Minh

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Teymourifar A, Rodrigues AM, Ferreira JS, Lopes C. A comparison between optimization tools to solve sectorization problem. In Le Thi HA, Le HM, Le Thi HA, Pham Dinh T, editors, Modelling, computation and optimization in information systems and management sciences: proceedings of the 4th international conference on modelling, computation and optimization in information systems and management sciences - MCO 2021. Springer. 2022. p. 40-50. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-92666-3_4

A comparison between optimization tools to solve sectorization problem

Abstract

Publication series

Conference

Keywords

Access to Document

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CEECINST2018 - A. Teymourifar: Simulation and Analytical Techniques Applied to Management and Economics

CEGE: Research Center in Management and Economics

Cite this

A comparison between optimization tools to solve sectorization problem

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

CEECINST2018 - A. Teymourifar: Simulation and Analytical Techniques Applied to Management and Economics

CEGE: Research Center in Management and Economics

Cite this