A comparison among optimization software to solve bi-objective sectorization problem
In this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023058103 |
| _version_ | 1797733025540210688 |
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| author | Aydin Teymourifar |
| author_facet | Aydin Teymourifar |
| author_sort | Aydin Teymourifar |
| collection | DOAJ |
| description | In this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single- and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models; of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems. |
| first_indexed | 2024-03-12T12:23:05Z |
| format | Article |
| id | doaj.art-d81eb0fa0c274b62a9f5705751b71f29 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-12T12:23:05Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-d81eb0fa0c274b62a9f5705751b71f292023-08-30T05:51:54ZengElsevierHeliyon2405-84402023-08-0198e18602A comparison among optimization software to solve bi-objective sectorization problemAydin Teymourifar0Universidade Católica Portuguesa, Católica Porto Business School, Centro de Estudos em Gestão e Economia Porto, PortugalIn this study, we compare the performance of optimization software to solve the bi-objective sectorization problem. The used solution method is based on an approach that has not been used before in the literature on sectorization, in which, the bi-objective model is transformed into single-objective ones, whose results are regarded as ideal points for the objective functions in the bi-objective model. Anti-ideal points are also searched similarly. Then, using the ideal and anti-ideal points, the bi-objective model is redefined as a single-objective one and solved. The difficulties of solving the models, which are basically non-linear, are discussed. Furthermore, the models are linearized, in which case how the number of variables and constraints changes is discussed. Mathematical models are implemented in Python's Pulp library, Lingo, IBM ILOG CPLEX Optimization Studio, and GAMS software, and the obtained results are presented. Furthermore, metaheuristics available in Python's Pymoo library are utilized to solve the models' single- and bi-objective versions. In the experimental results section, benchmarks of different sizes are derived for the problem, and the results are presented. It is observed that the solvers do not perform satisfactorily in solving models; of all of them, GAMS achieves the best results. The utilized metaheuristics from the Pymoo library gain feasible results in reasonable times. In the conclusion section, suggestions are given for solving similar problems. Furthermore, this article summarizes the managerial applications of the sectorization problems.http://www.sciencedirect.com/science/article/pii/S2405844023058103Multi-objective optimizationSectorizationMixed integer non-linear programmingGAMSCPLEXLingo |
| spellingShingle | Aydin Teymourifar A comparison among optimization software to solve bi-objective sectorization problem Heliyon Multi-objective optimization Sectorization Mixed integer non-linear programming GAMS CPLEX Lingo |
| title | A comparison among optimization software to solve bi-objective sectorization problem |
| title_full | A comparison among optimization software to solve bi-objective sectorization problem |
| title_fullStr | A comparison among optimization software to solve bi-objective sectorization problem |
| title_full_unstemmed | A comparison among optimization software to solve bi-objective sectorization problem |
| title_short | A comparison among optimization software to solve bi-objective sectorization problem |
| title_sort | comparison among optimization software to solve bi objective sectorization problem |
| topic | Multi-objective optimization Sectorization Mixed integer non-linear programming GAMS CPLEX Lingo |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023058103 |
| work_keys_str_mv | AT aydinteymourifar acomparisonamongoptimizationsoftwaretosolvebiobjectivesectorizationproblem AT aydinteymourifar comparisonamongoptimizationsoftwaretosolvebiobjectivesectorizationproblem |