Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments

In manufacturing systems, there are environments where the elaboration of a product requires a series of sequential operations, involving the configuration of machines by stages, intermediate buffer capacities, definition of assembly lines, and routing of parts. The objective of this research is to...

Full description

Autores:
Herrera Vidal, Germán
Coronado-Hernandez, Jairo R.
Minnaard, Claudia
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9193
Acceso en línea:
https://hdl.handle.net/11323/9193
https://doi.org/10.1007/s00170-021-08028-9
https://repositorio.cuc.edu.co/
Palabra clave:
Complexity
Manufacturing systems
Flow shop and hybrid
Modeling
Statistical analysis
Rights
embargoedAccess
License
Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
id RCUC2_a57ef285161b3fdd537c7ca9c051bc17
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9193
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments
title Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments
spellingShingle Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments
Complexity
Manufacturing systems
Flow shop and hybrid
Modeling
Statistical analysis
title_short Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments
title_full Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments
title_fullStr Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments
title_full_unstemmed Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments
title_sort Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments
dc.creator.fl_str_mv Herrera Vidal, Germán
Coronado-Hernandez, Jairo R.
Minnaard, Claudia
dc.contributor.author.spa.fl_str_mv Herrera Vidal, Germán
Coronado-Hernandez, Jairo R.
Minnaard, Claudia
dc.subject.proposal.eng.fl_str_mv Complexity
Manufacturing systems
Flow shop and hybrid
Modeling
Statistical analysis
topic Complexity
Manufacturing systems
Flow shop and hybrid
Modeling
Statistical analysis
description In manufacturing systems, there are environments where the elaboration of a product requires a series of sequential operations, involving the configuration of machines by stages, intermediate buffer capacities, definition of assembly lines, and routing of parts. The objective of this research is to develop a modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. The methodological approach starts with the structural modeling, then the measurement of the complexity in the systems is developed, the hypotheses are proposed, and finally an experimental and factorial statistical analysis is developed. The results obtained corroborate the hypotheses proposed, where statistically the structural design factors and the variation of production time per stage have a significant influence on the response variable associated to the total complexity. Similarly, there is evidence of correlation between the performance indicators and the variable studied, in which the incidence with production costs stands out.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-10-08
dc.date.accessioned.none.fl_str_mv 2022-05-25T22:24:26Z
dc.date.available.none.fl_str_mv 2022-10-10
2022-05-25T22:24:26Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
format http://purl.org/coar/resource_type/c_6501
dc.identifier.citation.spa.fl_str_mv Vidal, G.H., Hernández, J.R.C. & Minnaard, C. Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. Int J Adv Manuf Technol 118, 3049–3058 (2022). https://doi.org/10.1007/s00170-021-08028-9
dc.identifier.issn.spa.fl_str_mv 268-3768
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9193
dc.identifier.url.spa.fl_str_mv https://doi.org/10.1007/s00170-021-08028-9
dc.identifier.doi.spa.fl_str_mv 10.1007/s00170-021-08028-9
dc.identifier.eissn.spa.fl_str_mv 1433-3015
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Vidal, G.H., Hernández, J.R.C. & Minnaard, C. Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. Int J Adv Manuf Technol 118, 3049–3058 (2022). https://doi.org/10.1007/s00170-021-08028-9
268-3768
10.1007/s00170-021-08028-9
1433-3015
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9193
https://doi.org/10.1007/s00170-021-08028-9
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv International Journal of Advanced Manufacturing Technology
dc.relation.references.spa.fl_str_mv 1. Quirk M (1999) Manufacturing, teams, and improvement: the human art of manufacturing. Prentice-Hall
2. Sánchez GV (2006) Introducción a la teoría económica un enfoque latinoamericano. Pearson educación
3. Gaio L, Gino F, Zaninotto E (2002) I sistemi di produzione: manuale per la gestione operativa dell’impresa. Carocci
4. Frizelle G, Woodcock E (1995) Measuring complexity as an aid to developing operational strategy. Int J Oper Prod Manag. https://doi.org/10.1108/01443579510083640
5. Scholl A (1999) Balancing and sequencing of assembly lines (No. 10881). Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)
6. Vidal GH, Hernández JRC (2021) Complexity in manufacturing systems: a literature review. Production Engineering, 1–13
7. Deshmukh AV, Talavage JJ, Barash MM (1998) Complexity in manufacturing systems, Part 1: Analysis of static complexity. IIE Trans 30(7):645–655
8. Manuj I, Sahin F (2011) A model of supply chain and supply chain decision-making complexity. International Journal of Physical Distribution & Logistics Management
9. Papakostas N, Papachatzakis P, Xanthakis V, Mourtzis D, Chryssolouris G (2010) An approach to operational aircraft maintenance planning. Decis Support Syst 48(4):604–612
10. Herbert S (1962) The architecture of complexity. Proc Am Philos Soc 106(6):467–482
11. Flynn BB, Flynn EJ (1999) Information-processing alternatives for coping with manufacturing environment complexity. Decis Sci 30(4):1021–1052
12. Garbie IH, Shikdar A (2011) Analysis and estimation of complexity level in industrial firms. Int J Ind Syst Eng 8(2):175–197
13. Calinescu A, Efstathiou J, Bermejo J, Schirn J (1997) Modelling and simulation of a real complex process-based manufacturing system. In Proceedings of the Thirty-Second International Matador Conference (pp. 137–142). Palgrave, London
14. Calinescu A, Efstathiou J, Bermejo J, Schirn J (1997) Assessing decision-making and process complexity in a manufacturer through simulation. IFAC Proceedings Volumes 30(24):149–152
15. Almasarwah N, Süer G (2019) Flexible flowshop design in cellular manufacturing systems. Procedia Manufacturing 39:991–1001
16. Kurz ME, Askin RG (2003) Comparing scheduling rules for flexible flow lines. Int J Prod Econ 85(3):371–388
17. Quadt D, Kuhn H (2007) A taxonomy of flexible flow line scheduling procedures. Eur J Oper Res 178(3):686–698
18. Bouras A, Masmoudi M, Saadani NEH, Bahroun Z (2017) A three-stage appointment scheduling for an outpatient chemotherapy unit using integer programming. In 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 0916–0921). IEEE
19. Guinet AGP, Solomon M (1996) Scheduling hybrid flowshops to minimize maximum tardiness or maximum completion time. Int J Prod Res 34(6):1643–1654
20. Ho MH, Hnaien F, Dugardin F (2021) Electricity cost minimisation for optimal makespan solution in flow shop scheduling under time-of-use tariffs. Int J Prod Res 59(4):1041–1067
21. Yan J, Li L, Zhao F, Zhang F, Zhao Q (2016) A multi-level optimization approach for energy-efficient flexible flow shop scheduling. J Clean Prod 137:1543–1552
22. Dai M, Tang D, Giret A, Salido MA, Li WD (2013) Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing 29(5):418–429
23. Marichelvam MK, Geetha M, Tosun Ö (2020) An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors-a case study. Comput Oper Res 114:104812
24. Agnetis A, Pacifici A, Rossi F, Lucertini M, Nicoletti S, Nicolo F, Oriolo G, Pacciarelli D, Pesaro E (1997) Scheduling of flexible flow lines in an automobile assembly plant. Eur J Oper Res 97:348–362
25. Tsubone H, Ohba M, Takamuki H, Miyake Y (1993) A production scheduling system for a hybrid flow shop-a case study. Omega 21(2):205–214
26. Alisantoso D, Khoo LP, Jiang PY (2003) An immune algorithm approach to the scheduling of a flexible PCB flow shop. Int J Advanced Manufacturing Technol 22(11):819–827
27. Piramuthu S, Raman N, Shaw MJ (1994) Learning-based scheduling in a flexible manufacturing flow line. IEEE Trans Eng Manage 41(2):172–182
28. Wang S, Wang X, Chu F, Yu J (2020) An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production. Int J Prod Res 58(8):2283–2314
29. Liu M, Yang X, Zhang J, Chu C (2017) Scheduling a tempered glass manufacturing system: a three-stage hybrid flow shop model. Int J Prod Res 55(20):6084–6107
30. Leon VJ, Ramamoorthy B (1997) An adaptive problemspace-based search method for flexible flow line scheduling. IIE Trans 29:115–125
31. Rahmani D, Ramezanian R (2016) A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: a case study. Comput Ind Eng 98:360–372
32. Riane F (1998) Scheduling hybrid flowshops: algorithms and applications. Ph.D. Thesis, Faculte’s Universitaires Catholiques de Mons
33. Salvador MS (1973) A solution to a special class of flow shop scheduling problems. In: Elmaghraby SE (ed) Symposium on the Theory of Scheduling and Its Applications. Springer, Berlin, pp 83–91
34. Quadt D, Kuhn H (2005) A conceptual framework for lotsizing and scheduling of flexible flow lines. Int J Prod Res 43(11):2291–2308
35. Wittrock RJ (1988) An adaptive scheduling algorithm for flexible flow lines. Oper Res 36(4):445–453
36. Shannon CE (1948) A mathematical theory of communication. Bell syst technic J 27(3):379–423
37. Calinescu A (2000) Complexity in manufacturing: an information theoretic approach. In Conference on complexity and complex systems in industry, 19–20 Sept 2000 (pp. 19–20). University of Warwick
38. Chedid JA, Vidal GH (2012) Análisis del Problema de Planificación de la Producción en Cadenas de Suministro Colaborativas: Una Revisión de la Literatura en el Enfoque de Teoría de Juegos.
39. Bozarth CC, Warsing DP, Flynn BB, Flynn EJ (2009) The impact of supply chain complexity on manufacturing plant performance. J Oper Manag 27(1):78–93
40. MacDuffie JP, Sethuraman K, Fisher ML (1996) Product variety and manufacturing performance: evidence from the international automotive assembly plant study. Manage Sci 42(3):350–369
41. Wu Y, Frizelle G, Efstathiou J (2007) A study on the cost of operational complexity in customer–supplier systems. Int J Prod Econ 106(1):217–229
42. Sivadasan S, Efstathiou J, Calinescu A, Huatuco LH (2006) Advances on measuring the operational complexity of supplier–customer systems. Eur J Oper Res 171(1):208–226
43. Orfi N, Terpenny J, Sahin-Sariisik A (2011) “Harnessing product complexity: Step 1 - establishing product complexity dimensions and indicators”. Eng Econ:9–79
44. Coronado Hernández JR (2016) Análisis del efecto de algunos factores de complejidad e incertidumbre en el rendimiento de las Cadenas de Suministro. Propuesta de una herramienta de valoración basada en simulación (Doctoral dissertation)
45. Efthymiou K, Pagoropoulos A, Papakostas N, Mourtzis D, Chryssolouris G (2014) Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability. CIRP J Manuf Sci Technol 7(4):324–334
dc.relation.citationendpage.spa.fl_str_mv 3058
dc.relation.citationstartpage.spa.fl_str_mv 3049
dc.rights.spa.fl_str_mv Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
© 2022 Springer Nature Switzerland AG. Part of Springer Nature.
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/embargoedAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_f1cf
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
© 2022 Springer Nature Switzerland AG. Part of Springer Nature.
https://creativecommons.org/licenses/by-nc-sa/4.0/
http://purl.org/coar/access_right/c_f1cf
eu_rights_str_mv embargoedAccess
dc.format.extent.spa.fl_str_mv 1 página
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Springer London
dc.publisher.place.spa.fl_str_mv United Kingdom
institution Corporación Universidad de la Costa
dc.source.url.spa.fl_str_mv https://link.springer.com/article/10.1007/s00170-021-08028-9
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/37f84a7a-f1f9-4b92-8fbd-48165dc4a5c4/download
https://repositorio.cuc.edu.co/bitstreams/059c0e92-004a-45fe-b7aa-904275e2543b/download
https://repositorio.cuc.edu.co/bitstreams/e14f5ad3-ba7b-4a99-ae35-c7c90006d9d3/download
https://repositorio.cuc.edu.co/bitstreams/7fa1be72-4038-4ede-b25f-409aadde2122/download
bitstream.checksum.fl_str_mv e30e9215131d99561d40d6b0abbe9bad
a9c5ed1db320b729ceb3a4640d741db4
df0cd3cd1f5201ea23acb9b5f26f9dbd
f2a73fd7894c048b4f0127c6f5447255
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1828166868523810816
spelling Herrera Vidal, GermánCoronado-Hernandez, Jairo R.Minnaard, Claudia 2022-05-25T22:24:26Z2022-10-102022-05-25T22:24:26Z2021-10-08Vidal, G.H., Hernández, J.R.C. & Minnaard, C. Modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. Int J Adv Manuf Technol 118, 3049–3058 (2022). https://doi.org/10.1007/s00170-021-08028-9268-3768https://hdl.handle.net/11323/9193https://doi.org/10.1007/s00170-021-08028-910.1007/s00170-021-08028-91433-3015Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In manufacturing systems, there are environments where the elaboration of a product requires a series of sequential operations, involving the configuration of machines by stages, intermediate buffer capacities, definition of assembly lines, and routing of parts. The objective of this research is to develop a modeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments. The methodological approach starts with the structural modeling, then the measurement of the complexity in the systems is developed, the hypotheses are proposed, and finally an experimental and factorial statistical analysis is developed. The results obtained corroborate the hypotheses proposed, where statistically the structural design factors and the variation of production time per stage have a significant influence on the response variable associated to the total complexity. Similarly, there is evidence of correlation between the performance indicators and the variable studied, in which the incidence with production costs stands out.1 páginaapplication/pdfengSpringer LondonUnited KingdomAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)© 2022 Springer Nature Switzerland AG. Part of Springer Nature.https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfModeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environmentsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_b1a7d7d4d402bccehttps://link.springer.com/article/10.1007/s00170-021-08028-9International Journal of Advanced Manufacturing Technology1. Quirk M (1999) Manufacturing, teams, and improvement: the human art of manufacturing. Prentice-Hall2. Sánchez GV (2006) Introducción a la teoría económica un enfoque latinoamericano. Pearson educación3. Gaio L, Gino F, Zaninotto E (2002) I sistemi di produzione: manuale per la gestione operativa dell’impresa. Carocci4. Frizelle G, Woodcock E (1995) Measuring complexity as an aid to developing operational strategy. Int J Oper Prod Manag. https://doi.org/10.1108/014435795100836405. Scholl A (1999) Balancing and sequencing of assembly lines (No. 10881). Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)6. Vidal GH, Hernández JRC (2021) Complexity in manufacturing systems: a literature review. Production Engineering, 1–137. Deshmukh AV, Talavage JJ, Barash MM (1998) Complexity in manufacturing systems, Part 1: Analysis of static complexity. IIE Trans 30(7):645–6558. Manuj I, Sahin F (2011) A model of supply chain and supply chain decision-making complexity. International Journal of Physical Distribution & Logistics Management9. Papakostas N, Papachatzakis P, Xanthakis V, Mourtzis D, Chryssolouris G (2010) An approach to operational aircraft maintenance planning. Decis Support Syst 48(4):604–61210. Herbert S (1962) The architecture of complexity. Proc Am Philos Soc 106(6):467–48211. Flynn BB, Flynn EJ (1999) Information-processing alternatives for coping with manufacturing environment complexity. Decis Sci 30(4):1021–105212. Garbie IH, Shikdar A (2011) Analysis and estimation of complexity level in industrial firms. Int J Ind Syst Eng 8(2):175–19713. Calinescu A, Efstathiou J, Bermejo J, Schirn J (1997) Modelling and simulation of a real complex process-based manufacturing system. In Proceedings of the Thirty-Second International Matador Conference (pp. 137–142). Palgrave, London14. Calinescu A, Efstathiou J, Bermejo J, Schirn J (1997) Assessing decision-making and process complexity in a manufacturer through simulation. IFAC Proceedings Volumes 30(24):149–15215. Almasarwah N, Süer G (2019) Flexible flowshop design in cellular manufacturing systems. Procedia Manufacturing 39:991–100116. Kurz ME, Askin RG (2003) Comparing scheduling rules for flexible flow lines. Int J Prod Econ 85(3):371–38817. Quadt D, Kuhn H (2007) A taxonomy of flexible flow line scheduling procedures. Eur J Oper Res 178(3):686–69818. Bouras A, Masmoudi M, Saadani NEH, Bahroun Z (2017) A three-stage appointment scheduling for an outpatient chemotherapy unit using integer programming. In 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 0916–0921). IEEE19. Guinet AGP, Solomon M (1996) Scheduling hybrid flowshops to minimize maximum tardiness or maximum completion time. Int J Prod Res 34(6):1643–165420. Ho MH, Hnaien F, Dugardin F (2021) Electricity cost minimisation for optimal makespan solution in flow shop scheduling under time-of-use tariffs. Int J Prod Res 59(4):1041–106721. Yan J, Li L, Zhao F, Zhang F, Zhao Q (2016) A multi-level optimization approach for energy-efficient flexible flow shop scheduling. J Clean Prod 137:1543–155222. Dai M, Tang D, Giret A, Salido MA, Li WD (2013) Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing 29(5):418–42923. Marichelvam MK, Geetha M, Tosun Ö (2020) An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors-a case study. Comput Oper Res 114:10481224. Agnetis A, Pacifici A, Rossi F, Lucertini M, Nicoletti S, Nicolo F, Oriolo G, Pacciarelli D, Pesaro E (1997) Scheduling of flexible flow lines in an automobile assembly plant. Eur J Oper Res 97:348–36225. Tsubone H, Ohba M, Takamuki H, Miyake Y (1993) A production scheduling system for a hybrid flow shop-a case study. Omega 21(2):205–21426. Alisantoso D, Khoo LP, Jiang PY (2003) An immune algorithm approach to the scheduling of a flexible PCB flow shop. Int J Advanced Manufacturing Technol 22(11):819–82727. Piramuthu S, Raman N, Shaw MJ (1994) Learning-based scheduling in a flexible manufacturing flow line. IEEE Trans Eng Manage 41(2):172–18228. Wang S, Wang X, Chu F, Yu J (2020) An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production. Int J Prod Res 58(8):2283–231429. Liu M, Yang X, Zhang J, Chu C (2017) Scheduling a tempered glass manufacturing system: a three-stage hybrid flow shop model. Int J Prod Res 55(20):6084–610730. Leon VJ, Ramamoorthy B (1997) An adaptive problemspace-based search method for flexible flow line scheduling. IIE Trans 29:115–12531. Rahmani D, Ramezanian R (2016) A stable reactive approach in dynamic flexible flow shop scheduling with unexpected disruptions: a case study. Comput Ind Eng 98:360–37232. Riane F (1998) Scheduling hybrid flowshops: algorithms and applications. Ph.D. Thesis, Faculte’s Universitaires Catholiques de Mons33. Salvador MS (1973) A solution to a special class of flow shop scheduling problems. In: Elmaghraby SE (ed) Symposium on the Theory of Scheduling and Its Applications. Springer, Berlin, pp 83–9134. Quadt D, Kuhn H (2005) A conceptual framework for lotsizing and scheduling of flexible flow lines. Int J Prod Res 43(11):2291–230835. Wittrock RJ (1988) An adaptive scheduling algorithm for flexible flow lines. Oper Res 36(4):445–45336. Shannon CE (1948) A mathematical theory of communication. Bell syst technic J 27(3):379–42337. Calinescu A (2000) Complexity in manufacturing: an information theoretic approach. In Conference on complexity and complex systems in industry, 19–20 Sept 2000 (pp. 19–20). University of Warwick38. Chedid JA, Vidal GH (2012) Análisis del Problema de Planificación de la Producción en Cadenas de Suministro Colaborativas: Una Revisión de la Literatura en el Enfoque de Teoría de Juegos.39. Bozarth CC, Warsing DP, Flynn BB, Flynn EJ (2009) The impact of supply chain complexity on manufacturing plant performance. J Oper Manag 27(1):78–9340. MacDuffie JP, Sethuraman K, Fisher ML (1996) Product variety and manufacturing performance: evidence from the international automotive assembly plant study. Manage Sci 42(3):350–36941. Wu Y, Frizelle G, Efstathiou J (2007) A study on the cost of operational complexity in customer–supplier systems. Int J Prod Econ 106(1):217–22942. Sivadasan S, Efstathiou J, Calinescu A, Huatuco LH (2006) Advances on measuring the operational complexity of supplier–customer systems. Eur J Oper Res 171(1):208–22643. Orfi N, Terpenny J, Sahin-Sariisik A (2011) “Harnessing product complexity: Step 1 - establishing product complexity dimensions and indicators”. Eng Econ:9–7944. Coronado Hernández JR (2016) Análisis del efecto de algunos factores de complejidad e incertidumbre en el rendimiento de las Cadenas de Suministro. Propuesta de una herramienta de valoración basada en simulación (Doctoral dissertation)45. Efthymiou K, Pagoropoulos A, Papakostas N, Mourtzis D, Chryssolouris G (2014) Manufacturing systems complexity: An assessment of manufacturing performance indicators unpredictability. CIRP J Manuf Sci Technol 7(4):324–33430583049ComplexityManufacturing systemsFlow shop and hybridModelingStatistical analysisPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/37f84a7a-f1f9-4b92-8fbd-48165dc4a5c4/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTModeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments.pdf.txtModeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments.pdf.txttext/plain1387https://repositorio.cuc.edu.co/bitstreams/059c0e92-004a-45fe-b7aa-904275e2543b/downloada9c5ed1db320b729ceb3a4640d741db4MD53THUMBNAILModeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments.pdf.jpgModeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments.pdf.jpgimage/jpeg12513https://repositorio.cuc.edu.co/bitstreams/e14f5ad3-ba7b-4a99-ae35-c7c90006d9d3/downloaddf0cd3cd1f5201ea23acb9b5f26f9dbdMD54ORIGINALModeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments.pdfModeling and statistical analysis of complexity in manufacturing systems under flow shop and hybrid environments.pdfapplication/pdf1668467https://repositorio.cuc.edu.co/bitstreams/7fa1be72-4038-4ede-b25f-409aadde2122/downloadf2a73fd7894c048b4f0127c6f5447255MD5511323/9193oai:repositorio.cuc.edu.co:11323/91932024-09-17 14:19:58.539https://creativecommons.org/licenses/by-nc-sa/4.0/Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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

Publicaciones similares