Enfoques de programación matemática fuzzy multiobjetivo para la planicación operativa del transporte en una cadena de suministro del sector del automóvil // Fuzzy Multiobjective Mathematical Programming Approaches for Operational Transport Planning in an Automobile Supply Chain

Autores/as

  • Manuel Díaz-Madroñero Centro de Investigación Gestión e Ingeniería de Producción (CIGIP) Universidad Politécnica de Valencia
  • David Peidro Centro de Investigación Gestión e Ingeniería de Producción (CIGIP) Universidad Politécnica de Valencia
  • Josefa Mula Centro de Investigación Gestión e Ingeniería de Producción (CIGIP) Universidad Politécnica de Valencia
  • Francisco J. Ferriols Departamento de Organización de Empresas Universidad Politécnica de Valencia

Palabras clave:

Planificación de la cadena de suministro, planificación del transporte, programación lineal fuzzy multiobjetivo, incertidumbre, supply chain planning, transport planning, fuzzy multiobjective linear programming, uncertainty

Resumen

En este trabajo se presenta un modelo de programación matemática fuzzy multiobjetivo para la planificación del transporte a nivel operativo en una cadena de suministro. Los objetivos del modelo propuesto son la minimización del número de camiones utilizados y del inventario total, considerando como parámetro borroso las capacidades de los vehículos empleados. Se propone una metodología de resolución para transformar el modelo original en un modelo de programación lineal entera mixta con un único objetivo, aplicando diferentes enfoques recogidos en la literatura. El modelo propuesto se valida con datos pertenecientes a una cadena de suministro real del sector del automóvil. Por último, los resultados obtenidos para cada uno de los enfoques empleados muestran la mejora aportada por el modelo propuesto respecto al procedimiento heurístico para la toma de decisiones empleado en la cadena de suministro de estudio.

------------------------------------

In this paper, a fuzzy multiobjective mathematical programming model for operational transport planning in a supply chain is presented. The objectives of the proposed model are the minimization of the number of used trucks and the total inventory level, by considering vehicle capacities as a fuzzy parameter. We propose a solution methodology to transform the original model into a mixed integer linear programming model with a single objective by using different approaches in the literature. The proposed model is validated with data from a real-world automobile supply chain. Finally, the results for each of the approaches show the improvement obtained by the proposed model in comparison to the heuristic procedure for decision making used in the supply chain under study.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Allen, W.B. y Liu, D., 1995. Service Quality and Motor Carrier Costs: An Empirical Analysis. The Review of Economics and Statistics, 77(3), 499–510.

Arunachalam, R. y Sadeh, N.M., 2005. The supply chain trading agent competition. Electronic Commerce Research and Applications, 4(1), 66–84.

Bellman, R.E. y Zadeh, L.A., 1970. Decision-Making in a Fuzzy Environment. Management Science, 17(4), 141–164.

Bilgen, B., 2007. Possibilistic Linear Programming in Blending and Transportation Planning Problem. In Applications of Fuzzy Sets Theory. 20–27.

Bit, A.K., 2005. Fuzzy programming with hyperbolic membership functions for multi-objective capacitated solid transportation problem. The Journal of Fuzzy Mathematics, 13(2), 373–385.

Bit, A.K., Biswal, M.P. y Alam, S.S., 1993a. An additive fuzzy programming model for multiobjective transportation problem. Fuzzy Sets and Systems, 57(3), 313–319.

Bit, A.K., Biswal, M.P. y Alam, S.S., 1993b. Fuzzy programming approach to multiobjective solid transportation problem. Fuzzy Sets and Systems, 57(2), 183–194.

Cisheng, C., Ying, W. y Qichao, H., 2008. Study on Truck Stowage Planning of Cargo Distribution Center in a Town. En: Proceedings International Conference on Intelligent Computation Technology and Automation (ICICTA), 2008, 509–512.

Coyle, J., Edward, J. y Langley, C., 2003. The Management of Business Logistics: A Supply Chain Perspective 7th ed., Western/Thompson Learning.

Czyzyk, J., Mesnier, M. y More, J., 1998. The NEOS Server. Computational Science & Engineering, IEEE, 5(3), 68–75.

Chen, C. y Lee, W., 2004. Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6-7), 1131–1144.

Chen, S. y Chang, P., 2006. A mathematical programming approach to supply chain models with fuzzy parameters. Engineering Optimization, 38(6), 647–669.

Christopher, M. y Towill, D., 2001. An integrated model for the design of agile supply chains. International Journal of Physical Distribution & Logistics Management, 31(4), 235–246.

Deb, K., 2001. Multi-objective optimization using evolutionary algorithms, John Wiley and Sons.

Ellram, L.M., 1991. Supply-Chain Management: The Industrial Organisation Perspective. International Journal of Physical Distribution & Logistics Management, 21(1), 13–22.

Ertogral, K., 2008. Multi-item single source ordering problem with transportation cost: A Lagrangian decomposition approach. European Journal of Operational Research, 191(1), 156–165.

Evans, K., Feldman, H. y Foster, J., 1990. Purchasing motor carrier service: an investigation of the criteria used by small manufacturing firms. Journal of Small Business Management, 28(1), 39–47.

Fleischmann, B., 2005. Distribution and Transport Planning. In Supply Chain Management and Advanced Planning. 229–244.

Gropp, W. y Moré, J., 1997. Optimization Environments and the NEOS Server. In Approximation Theory and Optimization. Cambridge University Press, 167–182.

Hernández, J.E, Mula, J., Ferriols, F.J. y Poler, R., 2008. A conceptual model for the production and transport planning process: An application to the automobile sector. Computers in Industry, 59(8), 842–852.

Huang, G., Lau, J. y Mak, K., 2003. The impacts of sharing production information on supply chain dynamics: a review of the literature. International Journal of Production Research, 41, 1483–1517.

Jiménez, F. y Verdegay, J.L., 1999. Solving fuzzy solid transportation problems by an evolutionary algorithm based parametric approach. European Journal of Operational Research, 117(3), 485–510.

Jiménez, F. y Verdegay, J.L., 1998. Uncertain solid transportation problems. Fuzzy Sets and Systems, 100(1-3), 45–57.

Lai, Y. y Hwang, C., 1994a. Fuzzy multiple objective decision making: methods and applications, Berlin: Springer.

Lai, Y. y Hwang, C., 1994b. Interactive fuzzy multiple objective decision making. In Fuzzy Optimization. Berlin/Heidelberg: Springer-Verlag, 179–198.

Lai, Y. y Hwang, C., 1992. A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems , 49(2), 121–133.

Lai, Y. y Hwang, C., 1993. Possibilistic linear programming for managing interest rate risk. Fuzzy Sets and Systems, 54(2), 135–146.

Lee, E.S. y Li, R.J., 1993. Fuzzy multiple objective programming and compromise programming with Pareto optimum. Fuzzy Sets and Systems, 53(3), 275–288.

Li, L. y Lai, K.K., 2000. A fuzzy approach to the multiobjective transportation problem. Computers & Operations Research, 27(1), 43–57.

Li, X., Zhang, B. y Li, H., 2006. Computing efficient solutions to fuzzy multiple objective linear programming problems. Fuzzy Sets and Systems, 157(10), 1328–1332.

Liang, T., 2006. Distribution planning decisions using interactive fuzzy multi-objective linear programming. Fuzzy Sets and Systems, 157(10), 1303–1316.

Liang, T., 2008a. Interactive multi-objective transportation planning decisions using fuzzy linear programming. Asia-Pacific Journal of Operational Research, 25(1), 11–31.

Liang, T., 2008b. Fuzzy multi-objective production/distribution planning decisions with multiproduct and multi-time period in a supply chain. Computers & Industrial Engineering, 55(3), 676–694.

Liang, T. y Cheng, H., 2009. Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains. Expert Systems with Applications, 36(2), 3367–3377.

Lu, Q. y Dessouky, M., 2004. An Exact Algorithm for the Multiple Vehicle Pickup and Delivery Problem. Transportation Science, 38(4), 503–514.

Pan, Z., Tang, J. y Fung, R.Y., 2009. Synchronization of inventory and transportation under flexible vehicle constraint: A heuristics approach using sliding windows and hierarchical tree structure. European Journal of Operational Research, 192(3), 824–836.

Peidro, D., 2006. Modelos para la planificación táctica centralizada en una cadena de suministro bajo incertidumbre. Aplicación en una cadena de suministro del sector del automóvil. Tesis Doctoral. Universidad Politécnica de Valencia.

Peidro, D., Diaz-Madroñero, M. y Mula, J., 2009a. Operational transport planning in an automobile supply chain: an interactive fuzzy multi-objective approach. En Recent advances in computational intelligence, man-machine systems and cybernetics. WSEAS, 121–127.

Peidro, D., Mula, J., Poler, R. y Lario, F., 2009b. Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3), 400–420.

Peidro, D., Mula, J., Poler, R. y Verdegay, J., 2009c. Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640–2657.

Peidro, D. y Vasant, P., 2009. Fuzzy Multi-Objective Transportation Planning with Modified S-Curve Membership Function. En Proceedings 2nd Global Conference Power Control and Optimization, 101–110.

Selim, H. y Ozkarahan, I., 2008. A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3), 401–418.

Shih, L., 1999. Cement transportation planning via fuzzy linear programming. International Journal of Production Economics, 58(3), 277–287.

Tanaka, H., Ichihashi, H. y Asai, K., 1984. A formulation of fuzzy linear programming problem bases on comparision of fuzzy numbers. Control and Cybernetics, 13, 185–194.

Torabi, S. y Hassini, E., 2008. An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193–214.

Wang, R. y Liang, T., 2005. Applying possibilistic linear programming to aggregate production planning. International Journal of Production Economics, 98(3), 328–341.

Werners, B., 1988. Aggregation models in mathematical programming. En Mathematical Models for Decision Support. Springer, 295–305.

Werners, B., 1987a. An interactive fuzzy programming system. Fuzzy Sets and Systems., 23(1), 131–147.

Werners, B., 1987b. Interactive multiple objective programming subject to flexible constraints. European Journal of Operational Research, 31(3), 342–349.

Wu, Y. y Guu, S., 2001. A compromise model for solving fuzzy multiple objective linear programming problems. Journal of the Chinese Institute of Industrial Engineers, 18(5), 87–93.

Zheng, Y. y Liu, B., 2006. Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm. Applied Mathematics and Computation, 176(2), 673–683.

Zimmermann, H., 1978. Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45–46.

Zimmermann, H., 1975. Description and optimization of fuzzy systems. International Journal of General Systems, 2(1), 209.

Publicado

2016-11-04

Cómo citar

Díaz-Madroñero, M., Peidro, D., Mula, J., & Ferriols, F. J. (2016). Enfoques de programación matemática fuzzy multiobjetivo para la planicación operativa del transporte en una cadena de suministro del sector del automóvil // Fuzzy Multiobjective Mathematical Programming Approaches for Operational Transport Planning in an Automobile Supply Chain. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 9, Páginas 44 a 68. Recuperado a partir de https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2148

Número

Sección

Artículos