A Monte Carlo simulation-based approach to solve dynamic sectorization problem

Aydin  Teymourifar; Ana Maria Rodrigues; José Soeiro  Ferreira

doi:10.33544/mjmie.v5i2.179

A Monte Carlo simulation-based approach to solve dynamic sectorization problem

Aydin Teymourifar^*, Ana Maria Rodrigues, José Soeiro Ferreira

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

In this study, two novel stochastic models are introduced to solve the dynamic sectorization problem, in which sectors are created by assigning points to service centres. The objective function of the first model is defined based on the equilibration of the distance in the sectors, while in the second one, it is based on the equilibration of the demands of the sectors. Both models impose constraints on assignments and compactness of sectors. In the problem, the coordinates of the points and their demand change over time, hence it is called a dynamic problem. A new solution method is used to solve the models, in which expected values of the coordinates of the points and their demand are assessed by using the Monte Carlo simulation. Thus, the problem is converted into a deterministic one. The linear and deterministic type of the model, which is originally non-linear is implemented in Python's Pulp library and in this way the generated benchmarks are solved. Information about how benchmarks are derived and the obtained solutions are presented.

Original language	English
Pages (from-to)	45-51
Number of pages	7
Journal	Mapta Journal of Mechanical and Industrial Engineering
Volume	5
Issue number	2
DOIs	https://doi.org/10.33544/mjmie.v5i2.179
Publication status	Published - 30 Sept 2021

Keywords

Sectorization
Dynamic problems
Stochastic modelling
Monte Carlo simulation
Optimization

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10.33544/mjmie.v5i2.179Licence: CC BY-NC

40015554Final published version, 417 KBLicence: CC BY-NC

http://hdl.handle.net/10400.14/36289Licence: CC BY-NC

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CEGE: Research Center in Management and Economics
Silva, M. (Full Professor), Sousa, R. (Full Professor), Alves, P. (Assistant Professor), Madsen, A. (Invited Assistant Professor), Lourenço, A. (Associate Professor), Carvalho, A. S. (Associate Professor), Andrade, A. (Assistant Professor), Silva, P. D. (Associate Professor), Vlačić, B. (Invited Assistant Professor), Martins, C. (Invited Assistant Professor), Gomes, C. (Auxiliary Researcher), Gevrek, Z. E. (Invited Assistant Professor), Oliveira, F. G. D. (Associate Professor), Faria, G. (Invited Associate Professor), Marreiros, H. (Scholarship holder), Pinho, J. (Invited Assistant Professor), Machado, J. (Associate Professor), Araújo, J. (Auxiliary Researcher), Pinto, J. (Assistant Professor), Rego, A. (Full Professor), Gaspar, J. M. (Auxiliary Researcher), Corbo, L. (Invited Assistant Professor), Costa, L. (Associate Professor), Fernandes, L. (Assistant Professor), Sottomayor, M. (Assistant Professor), Martins, N. (Full Professor), Hernández-Marrero, P. (Auxiliary Researcher), Gonçalves, R. (Associate Professor), Ribeiro, R. (PI), Coelho, S. L. (Assistant Teacher), Pereira, S. M. (Auxiliary Researcher), Silva, S. (Associate Professor), Rodrigues, V. (Associate Professor), Sotiros, D. G. (Auxiliary Researcher), Valverde, C. (Assistant Professor), Leitão, A. (Assistant Professor), Julião, J. (Assistant Professor), Tavares, M. (Assistant Professor), Lages, C. R. (Auxiliary Researcher), Elmashhara, M. G. (Auxiliary Researcher) & Teymourifar, A. (Auxiliary Researcher)
1/01/20 → 31/12/25
Project: Other
CEECINST2018 - A. Teymourifar: Simulation and Analytical Techniques Applied to Management and Economics
Teymourifar, A. (PI)
5/01/21 → 30/04/25
Project: Research

Cite this

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title = "A Monte Carlo simulation-based approach to solve dynamic sectorization problem",

abstract = "In this study, two novel stochastic models are introduced to solve the dynamic sectorization problem, in which sectors are created by assigning points to service centres. The objective function of the first model is defined based on the equilibration of the distance in the sectors, while in the second one, it is based on the equilibration of the demands of the sectors. Both models impose constraints on assignments and compactness of sectors. In the problem, the coordinates of the points and their demand change over time, hence it is called a dynamic problem. A new solution method is used to solve the models, in which expected values of the coordinates of the points and their demand are assessed by using the Monte Carlo simulation. Thus, the problem is converted into a deterministic one. The linear and deterministic type of the model, which is originally non-linear is implemented in Python's Pulp library and in this way the generated benchmarks are solved. Information about how benchmarks are derived and the obtained solutions are presented.",

keywords = "Sectorization, Dynamic problems, Stochastic modelling, Monte Carlo simulation, Optimization",

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

year = "2021",

month = sep,

day = "30",

doi = "10.33544/mjmie.v5i2.179",

language = "English",

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journal = "Mapta Journal of Mechanical and Industrial Engineering",

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TY - JOUR

T1 - A Monte Carlo simulation-based approach to solve dynamic sectorization problem

AU - Teymourifar, Aydin

AU - Rodrigues, Ana Maria

AU - Ferreira, José Soeiro

PY - 2021/9/30

Y1 - 2021/9/30

N2 - In this study, two novel stochastic models are introduced to solve the dynamic sectorization problem, in which sectors are created by assigning points to service centres. The objective function of the first model is defined based on the equilibration of the distance in the sectors, while in the second one, it is based on the equilibration of the demands of the sectors. Both models impose constraints on assignments and compactness of sectors. In the problem, the coordinates of the points and their demand change over time, hence it is called a dynamic problem. A new solution method is used to solve the models, in which expected values of the coordinates of the points and their demand are assessed by using the Monte Carlo simulation. Thus, the problem is converted into a deterministic one. The linear and deterministic type of the model, which is originally non-linear is implemented in Python's Pulp library and in this way the generated benchmarks are solved. Information about how benchmarks are derived and the obtained solutions are presented.

AB - In this study, two novel stochastic models are introduced to solve the dynamic sectorization problem, in which sectors are created by assigning points to service centres. The objective function of the first model is defined based on the equilibration of the distance in the sectors, while in the second one, it is based on the equilibration of the demands of the sectors. Both models impose constraints on assignments and compactness of sectors. In the problem, the coordinates of the points and their demand change over time, hence it is called a dynamic problem. A new solution method is used to solve the models, in which expected values of the coordinates of the points and their demand are assessed by using the Monte Carlo simulation. Thus, the problem is converted into a deterministic one. The linear and deterministic type of the model, which is originally non-linear is implemented in Python's Pulp library and in this way the generated benchmarks are solved. Information about how benchmarks are derived and the obtained solutions are presented.

KW - Sectorization

KW - Dynamic problems

KW - Stochastic modelling

KW - Monte Carlo simulation

KW - Optimization

U2 - 10.33544/mjmie.v5i2.179

DO - 10.33544/mjmie.v5i2.179

M3 - Article

SN - 2517-4258

VL - 5

SP - 45

EP - 51

JO - Mapta Journal of Mechanical and Industrial Engineering

JF - Mapta Journal of Mechanical and Industrial Engineering

IS - 2

ER -

A Monte Carlo simulation-based approach to solve dynamic sectorization problem

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Keywords

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Projects

CEGE: Research Center in Management and Economics

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

Cite this