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A Fuzzy Order Promising Model With Non-Uniform Finished Goods

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A Fuzzy Order Promising Model With Non-Uniform Finished Goods

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dc.contributor.author Grillo-Espinoza, Hanzel es_ES
dc.contributor.author Alemany Díaz, María Del Mar es_ES
dc.contributor.author Ortiz Bas, Ángel es_ES
dc.contributor.author Mula, Josefa es_ES
dc.date.accessioned 2019-09-05T20:04:33Z
dc.date.available 2019-09-05T20:04:33Z
dc.date.issued 2018 es_ES
dc.identifier.issn 1562-2479 es_ES
dc.identifier.uri http://hdl.handle.net/10251/125105
dc.description.abstract [EN] In this paper, in order to reliably meet the homogeneity required by customers, a fuzzy model is proposed to support the promising process in LHP contexts after taking into account uncertainty in planned homoge- neous sublots. The fuzzy model is translated into an alpha- parametric equivalent crisp model. Here, it is important to highlight another important novelty of the paper related to the proposed methodology to analyse the suitability of the minimum degree of possibility (the a-cut), by an adapted TOPSIS-based fuzzy procedure. Finally, the experimental design, which is inspired in the ceramic sector, proves both the validity of the model and a better performance of the fuzzy model compared to the deterministic one, in several executions with forecasts of the real size of homogeneous sublots. es_ES
dc.description.sponsorship This research is partly supported by: The Ministry of Science, Technology and Telecommunications of the of Costa Rica Government (MICITT), through the Programme of Innovation and Human Capital for Competitiveness (PINN)(Contract No. PED-019-2015-1); and the Spanish Ministry of Economy and Competitiveness Projects "Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity'' (PLANGES-FHP) (Ref. DPI2011-23597) and "Operations design and Management of Global Supply Chains'' (GLOBOP) (Ref. DPI2012-38061-C02-01).
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof International Journal of Fuzzy Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Order promising es_ES
dc.subject Lack of homogeneity in the product es_ES
dc.subject Uncertainty es_ES
dc.subject Interdependent fuzzy coefficients es_ES
dc.subject Fuzzy TOPSIS es_ES
dc.subject Ceramic sector es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title A Fuzzy Order Promising Model With Non-Uniform Finished Goods es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s40815-017-0317-y es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2011-23597/ES/METODOS Y MODELOS PARA LA PLANIFICACION DE OPERACIONES Y GESTION DE PEDIDOS EN CADENAS DE SUMINISTRO CARACTERIZADAS POR LA FALTA DE HOMOGENEIDAD EN EL PRODUCTO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2012-38061-C02-01/ES/DISEÑO Y GESTION DE OPERACIONES EN CADENAS GLOBALES DE SUMINISTRO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICITT//PED-019-2015-1/
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Grillo-Espinoza, H.; Alemany Díaz, MDM.; Ortiz Bas, Á.; Mula, J. (2018). A Fuzzy Order Promising Model With Non-Uniform Finished Goods. International Journal of Fuzzy Systems. 20(1):187-208. https://doi.org/10.1007/s40815-017-0317-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.1007/s40815-017-0317-y es_ES
dc.description.upvformatpinicio 187 es_ES
dc.description.upvformatpfin 208 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\341706 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Ministerio de Ciencia Tecnología y Telecomunicaciones de Costa Rica
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.description.references Ahumada, O., Villalobos, J.R.: Operational model for planning the harvest and distribution of perishable agricultural products. Int. J. Prod. Econ. 133.2, 677–687 (2011). doi: 10.1016/j.ijpe.2011.05.015 es_ES
dc.description.references Davoli, G. et al.: A stochastic simulation approach for production scheduling and investment planning in the tile industry. Int. J. Eng. Sci. Technol. 2(9) (2010). doi: 10.4314/ijest.v2i9.64006 . es_ES
dc.description.references Grillo, H., Alemany, M.M.E., Ortiz, A.: A review of mathematical models for supporting the order promising process under Lack of Homogeneity in Product and other sources of uncertainty. Comput. Ind. Eng. 91, 239–261 (2016). doi: 10.1016/j.cie.2015.11.013 es_ES
dc.description.references Alemany, M.M.E., et al.: Order promising process for extended collaborative selling chain. Prod. Plann. Control 19.2, 105–131 (2008). doi: 10.1080/09537280801896011 es_ES
dc.description.references Alemany, M.M.E., et al.: A model driven decision support system for reallocation of supply to orders under uncertainty in ceramic companies. Technol. Econ. Dev. Econ. 21.4, 596–625 (2015). doi: 10.3846/20294913.2015.1055613 es_ES
dc.description.references Alarcón, F., Alemany, M.M.E., Ortiz, A.: Conceptual framework for the characterization of the order promising process in a collaborative selling network context. Int. J. Prod. Econ. 120.1, 100–114 (2009). doi: 10.1016/j.ijpe.2008.07.031 es_ES
dc.description.references Bui, T., Sebastian, H.-J.: IEEE. Integration of multi-criteria decision analysis and negotiation in order promising’. In: 43rd Hawaii International Conference on Systems Sciences vol 1–5. Proceedings of the Annual Hawaii International Conference on System Sciences. pp. 1115–1124 (2010). doi: 10.1109/HICSS.2010.237 es_ES
dc.description.references Ball, M.O., Chen, C.-Y., Zhao, Z.-Y.: In: Simchi-Levi, D., Wu, S.D., Shen, Z.-J. (eds.) Handbook of Quantitative Supply Chain Analysis: Modeling in the E-Business Era”. Chap. Available to Promise, pp. 447–483. Springer, Boston (2004). doi: 10.1007/978-1-4020-7953-5_11 es_ES
dc.description.references Alemany, M.M.E., et al.: Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Appl. Math. Model. 37.5, 3380–3398 (2013). doi: 10.1016/j.apm.2012.07.022 es_ES
dc.description.references Jiménez, M., et al.: Linear programming with fuzzy parameters: an interactive method resolution. Eur. J. Oper. Res. 177.3, 1599–1609 (2007). doi: 10.1016/j.ejor.2005.10.002 es_ES
dc.description.references Peidro, D., et al.: A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. Eur. J. Oper. Res. 205.1, 65–80 (2010). doi: 10.1016/j.ejor.2009.11.031 es_ES
dc.description.references Yong, D.: Plant location selection based on fuzzy TOPSIS. Int. J. Adv. Manuf. Technol. 28.7–8, 839–844 (2006). doi: 10.1007/s00170-004-2436-5 es_ES
dc.description.references Chen, C.-T.: A fuzzy approach to select the location of the distribution center. In: Fuzzy Sets and Systems 118.1, pp. 65–73 (2001) es_ES
dc.description.references Chen, C.-T.: Extensions of the TOPSIS for group decision-making under fuzzy environment. In: Fuzzy Sets and Systems 114.1, pp. 1–9 (2000). es_ES
dc.description.references Wang, Y.-M., Elhag, T.M.: Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. In: Expert Systems with Applications 31.2, pp. 309–319 (2006) es_ES
dc.description.references Wang, T.-C., Chang, T.-H.: Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. In: Expert Systems with Applications 33.4, pp. 870–880 (2007) es_ES
dc.description.references Gupta, A., Maranas, C.D.: Managing demand uncertainty in supply chain planning. In: 2nd Pan American Workshop in Process Systems Engineering 27.8–9, pp. 1219–1227 (Sept. 2003). doi: 10.1016/S0098-1354(03)00048-6 es_ES
dc.description.references Lababidi, H.M.S., et al.: Optimizing the supply chain of a petrochemical company under uncertain operating and economic conditions. Ind. Eng. Chem. Res. 43.1, 63–73 (2004). doi: 10.1021/ie030555d es_ES
dc.description.references Santoso, T., et al.: A stochastic programming approach for supply chain network design under uncertainty. Eur. J. Oper. Res. 167.1, 96–115 (2005). doi: 10.1016/j.ejor.2004.01.046 es_ES
dc.description.references Sodhi, M.S.: Managing demand risk in tactical supply chain planning for a global consumer electronics company. Prod. Oper. Manag. 14.1, 69–79 (2009). doi: 10.1111/j.1937-5956.2005.tb00010.x es_ES
dc.description.references Mula, J., Peidro, D., Poler, R.: The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. Integr. Global Supply Chain 128.1, 136–143 (2010). doi: 10.1016/j.ijpe.2010.06.007 es_ES
dc.description.references Wang, J., Shu, Y.-F.: Fuzzy decision modeling for supply chain management. Fuzzy Sets Syst. 150.1, 107–127 (2005). doi: 10.1016/j.fss.2004.07.005 es_ES
dc.description.references Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. In: Management Science 17.4 (Dec. 1970). doi: 10.1287/mnsc.17.4.B141 es_ES
dc.description.references Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Springer Science & Business Media, New York (2012) es_ES
dc.description.references Dubois, D., Fargier, H., Fortemps, P.: Fuzzy scheduling: modelling flexible constraints vs. coping with incomplete knowledge. Fuzzy Sets Sched. Plann. 147.2, 231–252 (2003). doi: 10.1016/S0377-2217(02)00558-1 es_ES
dc.description.references Alemany, M.M.E., et al.: A fuzzy model for shortage planning under uncertainty due to lack of homogeneity in planned production lots. Appl. Math. Model. 39.15, 4463–4481 (2015). doi: 10.1016/j.apm.2014.12.057 es_ES
dc.description.references Gen, M., Tsujimura, Y., Ida, K.: Method for solving multiobjective aggregate production planning problem with fuzzy parameters. Comput. Ind. Eng. 23.1–4, 117–120 (1992). doi: 10.1016/0360-8352(92)90077-W es_ES
dc.description.references Peidro, D., Vasant, P.: Transportation planning with modified S-curve membership functions using an interactive fuzzy multi-objective approach. Appl. Soft Comput. 11.2, 2656–2663 (2011). doi: 10.1016/j.asoc.2010.10.014 es_ES
dc.description.references Cadenas, J., Verdegay, J.: Using fuzzy numbers in linear programming. In: IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 27.6, pp. 1016–1022 (Dec. 1997). doi: 10.1109/3477.650062 es_ES
dc.description.references Peidro, D., et al.: Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets Syst. 160.18, 2640–2657 (2009). doi: 10.1016/j.fss.2009.02.021 es_ES
dc.description.references Chu, T.-C.: Facility location selection using fuzzy TOPSIS under group decisions. Int. J Uncertain. Fuzziness Knowledgebased Syst. 10.06, 687–701 (2002). doi: 10.1142/S0218488502001739 es_ES
dc.description.references Chamodrakas, I., Alexopoulou, N., Martakos, D.: Customer evaluation for order acceptance using a novel class of fuzzy methods based on TOPSIS. Exp. Syst. Appl. 36.4, 7409–7415 (2009). doi: 10.1016/j.eswa.2008.09.050 es_ES
dc.description.references Nakhaeinejad, M., Nahavandi, N.: An interactive algorithm for multiobjective flow shop scheduling with fuzzy processing time through resolution method and TOPSIS. Int. J. Adv. Manuf. Technol. 66.5–8, 1047–1064 (2013). doi: 10.1007/s00170-012-4388-5 es_ES
dc.description.references Shekarian, E., et al.: A fuzzy reverse logistics inventory system integrating economic order/production quantity models. Int. J. Fuzzy Syst. 18.6, 1141–1161 (2016). doi: 10.1007/s40815-015-0129-x es_ES
dc.description.references Büyüközkan, G., Parlak, I.B., Tolga, A.C.: Evaluation of knowledge management tools by using an interval type-2 fuzzy TOPSIS method. Int. J. Comput. Intell. Syst. 9.5, 812–826 (2016) es_ES
dc.description.references Saradhi, B. Pardha., Shankar, N. R., Suryanarayana, C.: Novel distance measure in fuzzy TOPSIS for supply chain strategy based supplier selection. Math. Probl. Eng. 2016 (2016) es_ES
dc.description.references Senvar, O., Turanoglu, E., Kahraman, C.: Usage of metaheuristics in engineering: a literature review. In: Meta–Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, pp. 484–528 (2013). doi: 10.4018/978-1-4666-2086-5.ch016 es_ES
dc.description.references Grillo, H., et al.: Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. Int. J. Bio-Inspired Comput. 7.3, 157–169 (2015). doi: 10.1504/IJBIC.2015.069557 es_ES
dc.description.references Rajavel, R., Thangarathanam, M.: Adaptive probabilistic behavioural learning system for the effective behavioural decision in cloud trading negotiation market. Futur. Gener. Comput. Syst. 58, 29–41 (2016) es_ES


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