Optimization of the Master Production Scheduling in a Textile Industry Using Genetic Algorithm

Lorente-Leyva, Leandro L.; Murillo-Valle, Jefferson R.; Montero-Santos, Yakcleem; Herrera-Granda, Israel David; Herrera-Granda, Erick P.; Rosero-Montalvo, Paúl D.; Peluffo-Ordóñez, Diego H.; Valencia, Xiomara Patricia Blanco

doi:10.1007/978-3-030-29859-3_57

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…The authors of [2] used GP approach to implement an optimal resource allocation algorithm, that minimizes the average execution time of the critical path of workflow under constraints. The authors of [3] show that Master Production Scheduling (MPS) is the key action for success of a textile manufacturing plant. First, they have analyzed the constraints, mainly the installed capacity and the number of workers.…”

Section: Related Workmentioning

confidence: 99%

“…We have used an approximation (heuristic) based approach: Genetic Programming (GP), to optimize the identified work flows. The GP approach is a well-known method in the planning and scheduling context [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16].Moreover, they have been widely used for research works related to manufacturing planning systems.…”

Section: Introductionmentioning

confidence: 99%

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Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

Liyanage¹,

Nuwanga²,

Anjana³

et al. 2020

GJCST

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The main goal of manufacturing industry is to produce the end products on time with good quality and keep the resource wastage low. However, manufacturing industry face several challenges such as bottle necks in the workflow, unsynchronized production, and sudden increase in product demands. In this paper, we are proposing a management platform for textile manufacturing plants with following modules: (1) sewing workflow optimization (2) quality assurance workflow optimization and (3) finishing workflow optimizations. We have used Genetic Programming (GP) approach, to optimize the workflows, considering different factors that affect each workflow. Our results show that, using our proposed platform, the manufacturing workflows can be optimized and reduce the bottle necks in the workflows and resource wastage in the manufacturing plant.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

Liyanage¹,

Nuwanga²,

Anjana³

et al. 2020

GJCST

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show abstract

“…In Ecuador, the textile industry is one of the sectors with the highest participation in manufacturing, representing 29% of the total of these companies. For these textile and clothing industries, effective planning will facilitate their survival in an increasingly demanding market [2].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

Lorente-Leyva

Alemany

Peluffo-Ordóñez

et al. 2020

Machine Learning, Optimization, and Data Science

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This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance.

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Demand Forecasting for Textile Products Using Statistical Analysis and Machine Learning Algorithms

Lorente-Leyva

Alemany

Peluffo-Ordóñez³

et al. 2021

Intelligent Information and Database Systems

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Optimization of the Master Production Scheduling in a Textile Industry Using Genetic Algorithm

Cited by 8 publications

References 20 publications

Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

Demand Forecasting for Textile Products Using Statistical Analysis and Machine Learning Algorithms

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