Two multi-objective optimization approaches for solving a fuzzy bi-objective distributed hybrid flow shop scheduling problem under uncertainty
Efficient production and distribution planning across a network of customers and producers is crucial in today’s world, especially given the growing focus on energy use and CO2 emissions. This paper tackles the challenge by optimizing production scheduling at various production centers, followed by...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/431777 |
| Acceso en línea: | https://hdl.handle.net/2117/431777 https://dx.doi.org/10.1007/s13369-025-10238-2 |
| Access Level: | acceso abierto |
| Palabra clave: | Hybrid flow shop scheduling Vehicle routing problem Fuzzy uncertainty Expected value Fuzzy chance-constrained programming Goal attainment Àrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d'operacions::Sistemes productius |
| Sumario: | Efficient production and distribution planning across a network of customers and producers is crucial in today’s world, especially given the growing focus on energy use and CO2 emissions. This paper tackles the challenge by optimizing production scheduling at various production centers, followed by vehicle routing from these centers to customers and back. The model prioritizes two main objectives: minimizing the total flow time (i.e., cumulative delivery time) and reducing CO2 emissions generated by both production machines and transport vehicles. Recognizing the uncertainties in real-world data, the model incorporates fuzzy logic to account for unknown processing and travel times. Fuzzy chance-constrained programming (FCCP) and expected value (EV) approaches are used to manage these uncertainties, converting the fuzzy model into a deterministic form. To solve the bi-objective problem, LP-metric and goal attainment (GA) approaches are employed. The model is validated through case studies, with both visual and quantitative results showing that the LP-metric approach outperforms the GA approach. When comparing the solutions using the TOPSIS (technique for order of preference by similarity to ideal solution) method, the LP-metric approach proves to be more effective, especially in balancing delivery efficiency with reduced energy consumption and CO2 emissions. This research highlights the importance of integrating sustainability and efficiency into production and distribution planning, offering a robust model that addresses both operational and environmental goals. |
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