@book{
 title = {Optimization of the Master Production Scheduling in a Textile Industry Using Genetic Algorithm},
 type = {book},
 year = {2019},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 keywords = {Forecasting,Genetic algorithm,Master Production Scheduling,Optimization,Production planning,Textile industry},
 volume = {11734 LNAI},
 id = {32c6ca5b-c107-35c3-a55d-8a0bb60ba13a},
 created = {2019-10-12T23:59:00.000Z},
 file_attached = {false},
 profile_id = {f01ceea9-1014-347a-b89d-aa69782ea2ee},
 last_modified = {2021-01-13T16:00:19.521Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2019, Springer Nature Switzerland AG. In a competitive environment, an industry’s success is directly related to the level of optimization of its processes, how production is planned and developed. In this area, the master production scheduling (MPS) is the key action for success. The object of study arises from the need to optimize the medium-term production planning system in a textile company, through genetic algorithms. This research begins with the analysis of the constraints, mainly determined by the installed capacity and the number of workers. The aggregate production planning is carried out for the T-shirts families. Due to such complexity, the application of bioinspired optimization techniques demonstrates their best performance, before industries that normally employ exact and simple methods that provide an empirical MPS but can compromise efficiency and costs. The products are then disaggregated for each of the items in which the MPS is determined, based on the analysis of the demand forecast, and the orders made by customers. From this, with the use of genetic algorithms, the MPS is optimized to carry out production planning, with an improvement of up to 96% of the level of service provided.},
 bibtype = {book},
 author = {Lorente-Leyva, L.L. and Murillo-Valle, J.R. and Montero-Santos, Y. and Herrera-Granda, I.D. and Herrera-Granda, E.P. and Rosero-Montalvo, P.D. and Peluffo-Ordóñez, D.H. and Blanco-Valencia, X.P.},
 doi = {10.1007/978-3-030-29859-3_57}
}

{"_id":"NvBaRdbFYWhTwQDCJ","bibbaseid":"lorenteleyva-murillovalle-monterosantos-herreragranda-herreragranda-roseromontalvo-peluffoordez-blancovalencia-optimizationofthemasterproductionschedulinginatextileindustryusinggeneticalgorithm-2019","authorIDs":["5e473f4cd8f94bde0100001b","5e474350d8f94bde01000053","5e4748edd8f94bde01000095","5e47627f45a735de0100005c","5e47896527a0c8de010000d7","5e4887d586dc17de010000bd","5e489f536ac344de010000d2","5e49de91b63120f201000112","5e4a14cccc11a8df01000137","5e4b5d5bcf8e89df0100003a","5e4c6cb6f2c6ddde010000a7","5e4c8af55cc521f20100006d","5e4dff4bcc196bde01000139","5e4ea65364b624de010000e7","5e4ee2c12962cadf0100014a","5e4f76dca01931de010000cf","5e50810a7e0827df0100004e","5e52108dbba759e801000075","5e5258dd2eb8f4de0100001e","5e55ef91c2c8a2df010000c2","5e59f31c6ef27ddf01000058","AWdnrMsEAL4dCtwaa","YNcYvua5iyqmYjaBk"],"author_short":["Lorente-Leyva, L.","Murillo-Valle, J.","Montero-Santos, Y.","Herrera-Granda, I.","Herrera-Granda, E.","Rosero-Montalvo, P.","Peluffo-Ordóñez, D.","Blanco-Valencia, X."],"bibdata":{"title":"Optimization of the Master Production Scheduling in a Textile Industry Using Genetic Algorithm","type":"book","year":"2019","source":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","keywords":"Forecasting,Genetic algorithm,Master Production Scheduling,Optimization,Production planning,Textile industry","volume":"11734 LNAI","id":"32c6ca5b-c107-35c3-a55d-8a0bb60ba13a","created":"2019-10-12T23:59:00.000Z","file_attached":false,"profile_id":"f01ceea9-1014-347a-b89d-aa69782ea2ee","last_modified":"2021-01-13T16:00:19.521Z","read":false,"starred":false,"authored":"true","confirmed":false,"hidden":false,"private_publication":false,"abstract":"© 2019, Springer Nature Switzerland AG. In a competitive environment, an industry’s success is directly related to the level of optimization of its processes, how production is planned and developed. In this area, the master production scheduling (MPS) is the key action for success. The object of study arises from the need to optimize the medium-term production planning system in a textile company, through genetic algorithms. This research begins with the analysis of the constraints, mainly determined by the installed capacity and the number of workers. The aggregate production planning is carried out for the T-shirts families. Due to such complexity, the application of bioinspired optimization techniques demonstrates their best performance, before industries that normally employ exact and simple methods that provide an empirical MPS but can compromise efficiency and costs. The products are then disaggregated for each of the items in which the MPS is determined, based on the analysis of the demand forecast, and the orders made by customers. From this, with the use of genetic algorithms, the MPS is optimized to carry out production planning, with an improvement of up to 96% of the level of service provided.","bibtype":"book","author":"Lorente-Leyva, L.L. and Murillo-Valle, J.R. and Montero-Santos, Y. and Herrera-Granda, I.D. and Herrera-Granda, E.P. and Rosero-Montalvo, P.D. and Peluffo-Ordóñez, D.H. and Blanco-Valencia, X.P.","doi":"10.1007/978-3-030-29859-3_57","bibtex":"@book{\n title = {Optimization of the Master Production Scheduling in a Textile Industry Using Genetic Algorithm},\n type = {book},\n year = {2019},\n source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n keywords = {Forecasting,Genetic algorithm,Master Production Scheduling,Optimization,Production planning,Textile industry},\n volume = {11734 LNAI},\n id = {32c6ca5b-c107-35c3-a55d-8a0bb60ba13a},\n created = {2019-10-12T23:59:00.000Z},\n file_attached = {false},\n profile_id = {f01ceea9-1014-347a-b89d-aa69782ea2ee},\n last_modified = {2021-01-13T16:00:19.521Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2019, Springer Nature Switzerland AG. In a competitive environment, an industry’s success is directly related to the level of optimization of its processes, how production is planned and developed. In this area, the master production scheduling (MPS) is the key action for success. The object of study arises from the need to optimize the medium-term production planning system in a textile company, through genetic algorithms. This research begins with the analysis of the constraints, mainly determined by the installed capacity and the number of workers. The aggregate production planning is carried out for the T-shirts families. Due to such complexity, the application of bioinspired optimization techniques demonstrates their best performance, before industries that normally employ exact and simple methods that provide an empirical MPS but can compromise efficiency and costs. The products are then disaggregated for each of the items in which the MPS is determined, based on the analysis of the demand forecast, and the orders made by customers. From this, with the use of genetic algorithms, the MPS is optimized to carry out production planning, with an improvement of up to 96% of the level of service provided.},\n bibtype = {book},\n author = {Lorente-Leyva, L.L. and Murillo-Valle, J.R. and Montero-Santos, Y. and Herrera-Granda, I.D. and Herrera-Granda, E.P. and Rosero-Montalvo, P.D. and Peluffo-Ordóñez, D.H. and Blanco-Valencia, X.P.},\n doi = {10.1007/978-3-030-29859-3_57}\n}","author_short":["Lorente-Leyva, L.","Murillo-Valle, J.","Montero-Santos, Y.","Herrera-Granda, I.","Herrera-Granda, E.","Rosero-Montalvo, P.","Peluffo-Ordóñez, D.","Blanco-Valencia, X."],"biburl":"https://bibbase.org/service/mendeley/f01ceea9-1014-347a-b89d-aa69782ea2ee","bibbaseid":"lorenteleyva-murillovalle-monterosantos-herreragranda-herreragranda-roseromontalvo-peluffoordez-blancovalencia-optimizationofthemasterproductionschedulinginatextileindustryusinggeneticalgorithm-2019","role":"author","urls":{},"keyword":["Forecasting","Genetic algorithm","Master Production Scheduling","Optimization","Production planning","Textile industry"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"book","biburl":"https://bibbase.org/service/mendeley/f01ceea9-1014-347a-b89d-aa69782ea2ee","creationDate":"2020-02-15T00:33:47.582Z","downloads":0,"keywords":["forecasting","genetic algorithm","master production scheduling","optimization","production planning","textile industry"],"search_terms":["optimization","master","production","scheduling","textile","industry","using","genetic","algorithm","lorente-leyva","murillo-valle","montero-santos","herrera-granda","herrera-granda","rosero-montalvo","peluffo-ordóñez","blanco-valencia"],"title":"Optimization of the Master Production Scheduling in a Textile Industry Using Genetic Algorithm","year":2019,"dataSources":["ntXyXv2964fDt3myF","bf4Wvyy9Na82zPs7u","ya2CyA73rpZseyrZ8","mL2WrmsYpxN4FsHKg","2252seNhipfTmjEBQ"]}