TY - JOUR
T1 - Multi-objective optimization model for sustainable production planning in textile MSMEs
AU - Flores-Siguenza, Pablo
AU - Marmolejo-Saucedo, Jose Antonio
AU - Guamán, Rodrigo
N1 - Publisher Copyright:
© 2023 Flores-Siguenza et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
PY - 2023
Y1 - 2023
N2 - Textile micro, small and medium-sized enterprises (MSMEs) are characterized by their great influence on the economy of the countries, both for their contribution to the gross domestic product as well as for the generation of employment. In recent years, the complexity of their operations, instability and lack of balance between economic, environmental and social factors, axes of sustainable development, stand out. Therefore, it is necessary to implement approaches such as sustainable manufacturing and production planning, which seeks the creation of products with minimal environmental impact, under safe conditions for workers, and economically robust. In this context, this study aims to develop a multi-objective optimization model that enhances sustainable production planning in textile MSMEs. The methodology is based on two phases, the first one focused on the acquisition of information and the second one dedicated to the mathematical formulation of the model, where three objective functions focused on economic, environmental and social factors are proposed. The model is validated with real data from a textile MSME in Ecuador and different production alternatives are generated by proposing the implementation and use of photovoltaic energy as well as a greater use of personal protective equipment. One of the relevant outcomes of the study is a sustainable decision support tool aimed at the textile industry, where different scenarios for production planning and their respective economic, environmental and social consequences are shown.
AB - Textile micro, small and medium-sized enterprises (MSMEs) are characterized by their great influence on the economy of the countries, both for their contribution to the gross domestic product as well as for the generation of employment. In recent years, the complexity of their operations, instability and lack of balance between economic, environmental and social factors, axes of sustainable development, stand out. Therefore, it is necessary to implement approaches such as sustainable manufacturing and production planning, which seeks the creation of products with minimal environmental impact, under safe conditions for workers, and economically robust. In this context, this study aims to develop a multi-objective optimization model that enhances sustainable production planning in textile MSMEs. The methodology is based on two phases, the first one focused on the acquisition of information and the second one dedicated to the mathematical formulation of the model, where three objective functions focused on economic, environmental and social factors are proposed. The model is validated with real data from a textile MSME in Ecuador and different production alternatives are generated by proposing the implementation and use of photovoltaic energy as well as a greater use of personal protective equipment. One of the relevant outcomes of the study is a sustainable decision support tool aimed at the textile industry, where different scenarios for production planning and their respective economic, environmental and social consequences are shown.
KW - MSMEs
KW - Multi-objective Optimization
KW - Sustainable Production Planning
KW - Textile Industry
UR - https://www.scopus.com/pages/publications/85174737623
U2 - 10.4108/eetinis.v10i3.3752
DO - 10.4108/eetinis.v10i3.3752
M3 - Artículo
AN - SCOPUS:85174737623
SN - 2410-0218
VL - 10
JO - EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
JF - EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
IS - 3
ER -