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A Fuzzy Order Promising Model With Non-Uniform Finished Goods

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Abstract

Traditionally, the homogeneity of available units of the same finished good (FG\(_i\)) to be promised to customers has been assumed. However, contexts with lack of homogeneity in the product (LHP) are characterised by units of the same FG\(_i\), which differ in some characteristics that are relevant for customers and give rise to different subtypes. For instance, in the ceramic industry, tiles are classified based on quality, tone and gage, because of functional and aesthetical reasons related to their joint installation. LHP imposes new constraints in the order promising process because customers need homogeneous units. However, the final amount of the homogeneous units in planned lots is uncertain when promising orders, because they will only be known once produced and classified. In this sense, we introduce homogeneity constraints including fuzzy sets; specifically, we address the interaction among fuzzy homogeneity coefficients that represent the fraction of each homogeneous sublot. Therefore, modelling uncertainty in interdependent technological coefficients in a dynamic context is one of the main novelties of our proposal. Thus, 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 homogeneous 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 \(\alpha\)-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.

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References

  1. 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

    Article  Google Scholar 

  2. 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.

  3. 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

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

  8. 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

  9. 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

    Article  MATH  Google Scholar 

  10. 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

    Article  MathSciNet  MATH  Google Scholar 

  11. 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

    Article  MathSciNet  MATH  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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)

  14. 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).

  15. 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)

  16. 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)

  17. 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

  18. 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

    Article  Google Scholar 

  19. 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

    Article  MathSciNet  MATH  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    MATH  Google Scholar 

  22. 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

    Article  MathSciNet  MATH  Google Scholar 

  23. 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

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

    Book  Google Scholar 

  25. 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

    MathSciNet  MATH  Google Scholar 

  26. 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

    Article  MathSciNet  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. 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

  30. 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

    Article  MathSciNet  MATH  Google Scholar 

  31. 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

    Article  MathSciNet  MATH  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. 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

    Article  MathSciNet  Google Scholar 

  35. 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)

  36. 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)

  37. 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

  38. 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

    Article  Google Scholar 

  39. 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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Acknowledgements

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).

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Authors and Affiliations

  1. Doctoral School, Universitat Politècnica de València, Camí de Vera s/n, 46022, València, Spain

    H. Grillo

  2. Research Centre of Production Management and Engineering (CIGIP), Universitat Politècnica de València, Camí de Vera s/n, 46022, València, Spain

    M. M. E. Alemany & A. Ortiz

  3. Research Centre of Production Management and Engineering (CIGIP), Universitat Politècnica de València, Plaza Ferrándiz y Carbonell, 2, 03801, Alcoy, Spain

    J. Mula

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  1. H. Grillo
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Correspondence to H. Grillo.

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Grillo, H., Alemany, M.M.E., Ortiz, A. et al. A Fuzzy Order Promising Model With Non-Uniform Finished Goods. Int. J. Fuzzy Syst. 20, 187–208 (2018). https://doi.org/10.1007/s40815-017-0317-y

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