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An Agent-Based System for the Design of New Products Using a Fuzzy Multicriteria Approach

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Abstract

The intense competition of global markets stimulates a significant change in the way products are designed, manufactured, and delivered. Such a situation is forcing companies to consider the use of new tools to support this decision process. This paper describes an agent-based system implementing a novel consumer-based fuzzy multicriteria methodology to support the design of new products. It argues that a combination of marketing decision support systems, multicriteria and multiobjective methodologies, fuzzy models, and agent technologies could be a valuable tool to assist marketing managers in new product design applications. In the multi-agent system architecture, software agents were classified into types and organized in teams. The first includes interface, task, and information agents. The second reflects Simon’s decision-making process, including intelligence, design, and choice teams. The communication between agents is carried out using an ontology. An example of system operation attempting to get the design of new corn oil is presented using sequence diagrams.

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References

  1. Alexouda, G.: A user-friendly marketing decision support system for the product line design using evolutionary algorithms. Decis. Support Syst. 38(4), 495–509 (2005)

    Article  Google Scholar 

  2. Baril, C., Yacout, S., Clément, B.: An interactive multi-objective algorithm for decentralized decision making in product design. Optim. Eng. 13(1), 121–150 (2012)

    Article  MathSciNet  Google Scholar 

  3. Dostatni E, Diakun J, Grajewski D, Wichniarek R, Karwasz A, editors. Multi-agent System to Support Decision-Making Process in Ecodesign. 10th International Conference on Soft Computing Models in Industrial and Environmental Applications; 2015: Springer

  4. Gastelum Chavira DA, Leyva Lopez JC, Larreta Ramírez EV. A multi-criteria and multi-objective approach for the market segmentation problem. In: J. L, Lu J, Xu Y, Martinez L, Kerre E, editors. Data Science and Knowledge Engineering for Sensing Decision Support. 11: World Scientific; 2018. p. 1003-9

  5. Leyva Lopez JC, León Santiesteban M, Ahumada Valenzuela O, O. Solano Noriega JJ. The new product design problem using a novel preference approach. In: J. L, Lu J, Xu Y, Martinez L, Kerre E, editors. Data Science and Knowledge Engineering for Sensing Decision Support. 11: World Scientific; 2018. p. 987-94

  6. Leyva Lopez. JC, León Santiesteban M, Ahumada Valenzuela O, Romero Serrano AM. A choice model for the product design problem based on the outranking approach. In: J. L, Lu J, Xu Y, Martinez L, Kerre E, editors. Data Science and Knowledge Engineering for Sensing Decision Support. 11: World Scientific; 2018. p. 1018-25

  7. Lei N, Moon SK. A Decision Support System for market-driven product positioning and design. Decision Support Systems. 2015

  8. Yang, S., Ong, S., Nee, A.: A decision support tool for product design for remanufacturing. Procedia CIRP. 40, 144–149 (2016)

    Article  Google Scholar 

  9. García-Diéguez, C., Herva, M., Roca, E.: A decision support system based on fuzzy reasoning and AHP–FPP for the ecodesign of products: application to footwear as case study. Appl. Comput. 26, 224–234 (2015)

    Google Scholar 

  10. Zhang C, Zhou G, Lu Q, editors. Decision support oriented ontological modeling of product knowledge. Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2017 IEEE 2nd Information; 2017: IEEE

  11. Yang, D., Li, X., Jiao, R.J., Wang, B.: Decision support to product configuration considering component replenishment uncertainty: a stochastic programming approach. Decis. Support Syst. 105, 108–118 (2018)

    Article  Google Scholar 

  12. Kłos, S.: Implementation of the AHP Method in ERP-Based Decision Support Systems for a New Product Development, pp. 199–207. Springer, Information and Software Technologies (2015)

    Google Scholar 

  13. Li, Y.-M., Chen, H.-M., Liou, J.-H., Lin, L.-F.: Creating social intelligence for product portfolio design. Decis. Support Syst. 66, 123–134 (2014)

    Article  Google Scholar 

  14. Lin, Y.-C., Chen, C.-C., Yeh, C.-H.: Intelligent decision support for new product development: a consumer-oriented approach. Appl. Math. 8(6), 2761–2768 (2014)

    Google Scholar 

  15. Yu TYT, Zhou JZJ, Xu FXF, Gong YGY, Wang WWW. Decision Support System of Product Development Based on Multi-agent. In: 2009 International Conference on Information Technology and Computer Science. 2009;2.

  16. Guillard, V., Buche, P., Destercke, S., Tamani, N., Croitoru, M., Menut, L., et al.: A Decision Support System to design modified atmosphere packaging for fresh produce based on a bipolar flexible querying approach. Comput. Electr. Agric. 111, 131–139 (2015)

    Article  Google Scholar 

  17. Starostka-Patyk, M.: New Products Design Decision Making Support by SimaPro Software on the Base of Defective Products Management. Procedia Computer Sci. 65, 1066–1074 (2015)

    Article  Google Scholar 

  18. Morales, V.L., Ortega, O.L.: Direct marketing based on a distributed intelligent system, pp. 255–271. Springer, Marketing Intelligent Systems Using Soft Computing (2010)

    Google Scholar 

  19. Guo, F., Ren, L., He, Z., Wang, H.: Decision support system for industrial designer based on Kansei engineering, pp. 47–54. Springer, Internationalization, Design and Global Development (2011)

    Google Scholar 

  20. Figueroa-Perez, J.F., Leyva-Lopez, J.C., Santillan, L.C., Contreras, E.O.P., Sánchez, P.J.: The use of marketing decision support systems for new product design: a review. Int. J. Comput. Intell. Syst. 12(2), 761–774 (2019)

    Google Scholar 

  21. Alvarez Carrillo P, Leyva López JC, Lopez Parra P. A new disaggregation preference method for new products design. In: J. L, Lu J, Xu Y, Martinez L, Kerre E, editors. Data Science and Knowledge Engineering for Sensing Decision Support: World Scientific; 2018. p. 1010-7

  22. Vahidov, R., Fazlollahi, R.: A multi-agent DSS for supporting e-commerce decisions. J. Comput. Inform. Syst. 44(2), 87–94 (2004)

    Google Scholar 

  23. Ai W-GAW-G, Sun JSJ, Li HLH. A distributed marketing decision support system based on multi-intelligent-agent. Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat No04EX826). 2004;1(August):26-9

  24. Sycara, K., Pannu, A., Willamson, M., Zeng, D., Decker, K.: Distributed intelligent agents. IEEE expert. 11(6), 36–46 (1996)

    Article  Google Scholar 

  25. Eriksson H-E, Penker M, Lyons B, Fado D. UML 2 toolkit: Wiley; 2003

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Correspondence to Juan C. Leyva-Lopez.

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Figueroa-Perez, J.F., Leyva-Lopez, J.C., Pérez-Contreras, E.O. et al. An Agent-Based System for the Design of New Products Using a Fuzzy Multicriteria Approach. Int. J. Fuzzy Syst. 22, 2691–2707 (2020). https://doi.org/10.1007/s40815-020-00934-6

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  • DOI: https://doi.org/10.1007/s40815-020-00934-6

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