Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation
This research addresses complexity in manufacturing systems from an entropic perspective for production improvement. The main objective is to develop and validate a methodology that develops an entropic metric of complexity in an integral way in production environments, through simulation and progra...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/50 |
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| author | Germán Herrera-Vidal Jairo R. Coronado-Hernández Ivan Derpich-Contreras Breezy P. Martínez Paredes Gustavo Gatica |
| author_facet | Germán Herrera-Vidal Jairo R. Coronado-Hernández Ivan Derpich-Contreras Breezy P. Martínez Paredes Gustavo Gatica |
| author_sort | Germán Herrera-Vidal |
| collection | DOAJ |
| description | This research addresses complexity in manufacturing systems from an entropic perspective for production improvement. The main objective is to develop and validate a methodology that develops an entropic metric of complexity in an integral way in production environments, through simulation and programming techniques. The methodological proposal is composed of six stages: (i) Case study, (ii) Hypothesis formulation, (iii) Discrete event simulation, (iv) Measurement of entropic complexity by applying Shannon’s information theory, (v) Entropy analysis, and (vi) Statistical analysis by ANOVA. The results confirm that factors such as production sequence and product volume significantly influence the structural complexity of the workstations, with station A being less complex (0.4154 to 0.9913 bits) compared to stations B and C, which reached up to 2.2084 bits. This analysis has shown that optimizing production scheduling can reduce bottlenecks and improve system efficiency. Furthermore, the developed methodology, validated in a case study of the metalworking sector, provides a quantitative framework that combines discrete event simulation and robust statistical analysis, offering an effective tool to anticipate and manage complexity in production. In synthesis, this research presents an innovative methodology to measure static and dynamic complexity in manufacturing systems, with practical application to improve efficiency and competitiveness in the industrial sector. |
| format | Article |
| id | doaj-art-b8e1c03245aa48b28dfe010632c3cba6 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-b8e1c03245aa48b28dfe010632c3cba62025-01-24T13:31:49ZengMDPI AGEntropy1099-43002025-01-012715010.3390/e27010050Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and SimulationGermán Herrera-Vidal0Jairo R. Coronado-Hernández1Ivan Derpich-Contreras2Breezy P. Martínez Paredes3Gustavo Gatica4Industrial Engineering School, Universidad del Sinú, Cartagena 130001, ColombiaDepartment of Productivity and Innovation, Universidad de la Costa, Barranquilla 080016, ColombiaFaculty of Engineering, Universidad de Santiago de Chile, Santiago 8370003, ChileFaculty of Engineering, Universidad Nacional Mayor de San Marcos, Lima 15081, PeruFaculty of Engineering, Universidad Andres Bello, Santiago 8370146, ChileThis research addresses complexity in manufacturing systems from an entropic perspective for production improvement. The main objective is to develop and validate a methodology that develops an entropic metric of complexity in an integral way in production environments, through simulation and programming techniques. The methodological proposal is composed of six stages: (i) Case study, (ii) Hypothesis formulation, (iii) Discrete event simulation, (iv) Measurement of entropic complexity by applying Shannon’s information theory, (v) Entropy analysis, and (vi) Statistical analysis by ANOVA. The results confirm that factors such as production sequence and product volume significantly influence the structural complexity of the workstations, with station A being less complex (0.4154 to 0.9913 bits) compared to stations B and C, which reached up to 2.2084 bits. This analysis has shown that optimizing production scheduling can reduce bottlenecks and improve system efficiency. Furthermore, the developed methodology, validated in a case study of the metalworking sector, provides a quantitative framework that combines discrete event simulation and robust statistical analysis, offering an effective tool to anticipate and manage complexity in production. In synthesis, this research presents an innovative methodology to measure static and dynamic complexity in manufacturing systems, with practical application to improve efficiency and competitiveness in the industrial sector.https://www.mdpi.com/1099-4300/27/1/50complexitymethodologyentropicmeasurementmanufacturing systems |
| spellingShingle | Germán Herrera-Vidal Jairo R. Coronado-Hernández Ivan Derpich-Contreras Breezy P. Martínez Paredes Gustavo Gatica Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation Entropy complexity methodology entropic measurement manufacturing systems |
| title | Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation |
| title_full | Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation |
| title_fullStr | Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation |
| title_full_unstemmed | Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation |
| title_short | Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation |
| title_sort | measuring complexity in manufacturing integrating entropic methods programming and simulation |
| topic | complexity methodology entropic measurement manufacturing systems |
| url | https://www.mdpi.com/1099-4300/27/1/50 |
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