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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/125105

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Title: A Fuzzy Order Promising Model With Non-Uniform Finished Goods
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses
Issued date:
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 ...[+]
Subjects: Order promising , Lack of homogeneity in the product , Uncertainty , Interdependent fuzzy coefficients , Fuzzy TOPSIS , Ceramic sector
Copyrigths: Cerrado
Source:
International Journal of Fuzzy Systems. (issn: 1562-2479 )
DOI: 10.1007/s40815-017-0317-y
Publisher:
Springer-Verlag
Publisher version: http://doi.org/10.1007/s40815-017-0317-y
Thanks:
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 ...[+]
Type: Artículo

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

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 .

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 [+]
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

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 .

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

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

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

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

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

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

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

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

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

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

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)

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).

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)

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)

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

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

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

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

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

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

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

Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Springer Science & Business Media, New York (2012)

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

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

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

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

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

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

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

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

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

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

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)

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)

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

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

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)

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