Hybrid fuzzy-stochastic approach to multi-product, multi-period, and multi-resource master production scheduling problem: Case of a polyethylene pipe and Fitting manufacturer

Document Type : Research Article

Authors

1 Department of Management, Khatam University, Tehran, Iran.

2 Department of Management, Saramadan Andishe Avina Co. Tehran, Iran.

3 Industrial Management Group, Faculty of Management and Accounting, Allameh Tabatabaei University, Tehran, Iran.

Abstract

Master production scheduling is an effective phase of production planning which leads to scheduling and magnitude of different products production in a company. This problem requires investigating a wide range of parameters, regarding demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty, in which demands, as time-dependent variables, are considered as stochastic variables, while cost and utilization parameters, with cognitive ambiguity, are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a practical problem of a polyethylene pipe and fitting Co. in Iran. The result showed a high degree of applicability.

Keywords

Main Subjects

References

References:
1. Higgins, P. and Browne, J. "Master production scheduling: a concurrent planning approach", Production Planning and Control, 3(1), pp. 2-18 (1992).
2. Tubino, D.F., Manual of Production Planning and Control [Manual de planejamento e controle da producno], Sao Paulo, Atlas (2000).
3. Soares, M.M. and Vieira, G.E. "A new multi-objective optimization method for master production scheduling problems based on genetic algorithm", International Journal of Advanced Manufacturing Technology, 41(5), pp. 549-567 (2009).
4. Cox, J.F. and Blackstone, J.H., APICS Dictionary, Virginia: APICS (2001).
5. Houghton, E. and Portugal, V. "Optimum production planning: an analytic framework", International Journal of Operations and Production Management, 21(9), pp. 1205-1221 (2001).
6. Vasant, P.M. "Application of fuzzy liner programming in production planning", Fuzzy Optimization and Decision Making, 2(3), pp. 229-241 (2003).
7. Wang, H.F. and Wu, K.Y. "Modeling and analysis for multi-period, multi-product and multi-resource production scheduling", Journal of Intelligent Manufacturing, 14(3), pp. 297-309 (2003).
8. Emani Vieira, G. and Ribas, P.C. "A new multiobjective optimization method for master production scheduling problems using simulated annealing", International Journal of Production Research, 42(21), pp. 4609-4622 (2003).
9. Sawik, T. "Multi-objective master production scheduling in make-to-order manufacturing", International Journal of Production Research, 45(12), pp. 2629-2653 (2007).
10. Lei, D. "Multi-objective production scheduling: a survey", International Journal of Advanced Manufacturing Technology, 43, pp. 926-938 (2009).
11. Leu, J.D., Wen, T.C., and Chang, K.H. "Development and application of a decision model for the integrated production and material planning of color filter manufacturing industry: an empirical study", International Journal of Industrial Engineering: Theory, Application and Practice, 17(4), pp. 300-309 (2011).
12. Kelbel, J. and Hanzalek, Z. "Solving production scheduling with earliness/tardiness penalties by constraint programming", Journal of Intelligent Manufacturing, 22(4), pp. 553-562 (2011).
13. Alfieri, A., Tolio, T., and Urgo, M. "A project scheduling approach to production and material requirement planning in manufacturing-to-order environments", Journal of Intelligent Manufacturing, 23(3), pp. 575- 585 (2012).
14. Alfieri, A., Tolio, T., and Urgo, M. "A two-stage stochastic programming project scheduling approach to production planning", International Journal of Advanced Manufacturing Technology, 62(1), pp. 279-290 (2012).
15. Ballestin, F., Mallor, F., and Mateo, P.M. "Production scheduling in a market-driven foundry: a mathematical programming approach versus a project scheduling metaheuristic algorithm", Optimization and Engineering, 13(4), pp. 663-687 (2012).
16. Moon, J.Y., Shin, K., and Park, J. "Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency", International Journal of Advanced Manufacturing Technology, 68(1), pp. 523-535 (2010).
17. Sun, L.B., Gao, S.S., Tao, S.Q., Li, Y.B., and Du, B.G. "A master production schedule warning approach for cement equipment manufacturing enterprises", Scientia Iranica Transactions E: Industrial Engineering, 21(3), pp. 1120-1127 (2014).
18. Sahebjamnia, N., Jolai, F., Torabi, S.A., and Aghabeiglo, M. "A novel fuzzy stochastic multi-objective linear programming for multi-level capacitated lot-sizing problem: a real case study of a furniture company", The International Journal of Advanced Manufacturing Technology, 84(1-4), pp. 749-767 (2016).
19. Kim, S.H. and Lee, Y.H. "Synchronized production planning and scheduling in semiconductor fabrication", Computers and Industrial Engineering, 96, pp. 72-85 (2016).
20. Menezes, G.C., Mateus, G.R., and Ravetti, M.G. "A hierarchical approach to solve a production planning and scheduling problem in bulk cargo terminal", Computers and Industrial Engineering, 97, pp. 1-14 (2016).
21. Martinez, K.Y.P., Morabito, R., and Toso, E.A.V. "Production planning in the molded pulp packaging industry", Computers and Industrial Engineering, 98, pp. 554-566 (2016).
22. Radhika, S., Srinivasa Rao, Ch., Neha Krishna, D., and Karteeka Pavan, K. "Multi-objective optimization of master production scheduling problems using Jaya algorithm", Proceeding of 6th International & 27th All India Manufacturing Technology, Design and Research Conference (AIMTDR-2016), pp. 1729-1732 (2016).
23. Cho, H.M. and Jeong, C.J. "A two-level method of production planning and scheduling for bi-objective reentrant hybrid  ow shops", Computers & Industrial Engineering, 107, pp. 174-181 (2017).
24. Farrokh, M., Azar, A., Jandaghi, G., and Ahmadi, E. "A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty", Fuzzy Sets and Systems, 341, pp. 69-91 (2018).
25. Gramani, M.C.N., Franca, P.M., and Arenales, M.N. "A linear optimization approach to the combined production planning model", Journal of the Franklin Institute, 348(7), pp. 1523-1536 (2011).
26. Yovits, M.C., Advances in Computers, 23, Academic, Gainesville, FL (1984).
27. Pedrycz, W. and Gomide, F., An Introduction to Fuzzy Sets: Analysis and Design, Massachusetts: Massachusetts Institute of Technology (1998).
28. Tang, O. and Grubbstrom, R.W. "Planning and replanning the master production schedule under demand uncertainty", International Journal of Production Economics, 78(3), pp. 323-334 (2002).
29. Fleten, S.E. and Kristoffersen, T.K. "Short-term hydropower production planning by stochastic programming", Computers and Operation Research, 35(8), pp. 2656-2671 (2008).
30. Feng, K., Rao, U.S., and Raturi, A. "Setting planned orders in master production scheduling under demand uncertainty", International Journal of Production Research, 49(13), pp. 4007-4025 (2011).
31. Liang, T.F. "Fuzzy multi-objective production/ distribution planning decisions with multi-product and multi-time period in supply chain", Computers and Industrial Engineering, 55(3), pp. 676-694 (2008).
32. Supriyanto, I. and Noche, B. "Fuzzy multi-objective linear programming and simulation approach to the development of valid and realistic master production schedule", Logistics Journal: Proceeding, 7 pp. 1-14 (2011).
33. Korpeoglu, E., Yaman, H., and SelimAkturk, M. "A multi-stage stochastic programming approach in master production scheduling", European Journal of Operational Research, 213(1), pp. 166-179 (2011).
34. Mula, J., Poler, R., Garcia-Sabater, J.P., and Lario, F.C. "Models for production planning under uncertainty: a review", International Journal of Production Economics, 103(1), pp. 271-285 (2006).
35. Wang, C. and Qiu, Z. "Hybrid uncertain analysis for temperature field prediction with random, fuzzy and interval parameters", International Journal of Thermal Sciences, 98, pp. 124-134 (2015).
36. Wang, C., Qiua, Z., and He, Y. "Fuzzy stochastic finite element method for the hybrid uncertain temperature field prediction", International Journal of Heat and Mass Transfer, 91, pp. 512-519 (2015).
37. Wang, C., Qiu, Z., and He, Y. "Fuzzy interval perturbation method for uncertain heat conduction problem with interval and fuzzy parameters", International Journal for Numerical Methods in Engineering, 104(5), pp. 330-346 (2015).
38. Wang, C., Qiu, Z., Xu, M., and Li, Y. "Novel reliability-based optimization method for thermal structure with hybrid random, interval and fuzzy parameters", Applied Mathematical Modelling, 47, pp. 576-586 (2017).
39. Charnes, A. and Cooper, W.W. "Chance-constrained programming", Management Science, 6(1), pp. 73-79 (1959).
40. Zhu, M., Taylor, D.B., Sarin, S.C., and Kramer, R.A. "Chance constrained programming models for riskbased economic and policy analysis of soil conservation", Agricultural and Resource Economics Review, 23(1), pp. 58-65 (1994).
41. Zadeh, L. "Fuzzy sets", Information Control, 8, pp. 338-353 (1965).
42. Kaufmann, A. and Gupta, M.M., Introduction to Fuzzy Arithmetic: Theory and Applications, New York: Van Nostrand Reinhold (1991).
43. Cao, B.Y., Wang, G.J., Guo, S.Z., and Chen, S.L., Fuzzy Information and Engineering, Heidelberg: Springer-Verlag (2010).
44. Lee, K.H., First Course on Fuzzy Theory and Applications, Berlin, Heidelberg: Springer-Verlag (2005).
45. Chandra, S. and Aggarwal, A. "On solving linear programming problems: A revisit to Zimmermann's approach", Journal of Intelligent Fuzzy Systems, 27(5), pp. 2603-2610 (2014).
46. Guua, S.M. and Wu, Y.K. "Two-phase approach for solving the fuzzy linear programming problems", Fuzzy Set Systems, 107(2), pp. 191-195 (1999).
47. Jimenez, M., Arenas, M., Bilbao, A., and Rodriguez, M.V. "Linear programming with fuzzy parameters: an interactive method resolution", European Journal of Operational Research, 177(3), pp. 1599-1609 (2007).
48. Kaur, J. and Kumar, A. "A new method to find the unique fuzzy optimal value of fuzzy linear programming problems", Journal of Optimization Theory and Applications, 156(2), pp. 529-534 (2013).
49. Kumar, A., Kaur, J., and Singh, P. "A new method for solving fully fuzzy linear programming problems", Applied Mathematical Modelling, 35(2), pp. 817-823 (2011).
50. Li, B., Theory and Practice of Uncertain Programming, Berlin - Heidelberg (2009).
51. Ouarda, T.B.M.J. and Labadie, J.W. "Chanceconstrained optimal control for multireservoir system optimization and risk analysis", Stochastic Environmental Research and Risk Assessment, 15(3), pp. 185- 204 (2001).
52. Ding, X. and Wang, C. "A novel algorithm of stochastic chance-constrained linear programming and its application", Mathematical Problems in Engineering, 2012, pp. 1-17 (2012).
53. Ishibuchi, H. and Tanaka, H. "Multi objective programming in optimization of the interval objective function", European Journal of Operational Research, 48(2), pp. 219-225 (1990).
54. Kaufmann, A. and Gil Aluja, J., Tecnicas de Gestion de Empresa, Madrid: Piramide (1992).
55. Yager, R.R. "Ranking fuzzy subsets over the unit interval", In Proceedings of 17th IEEE International Conference on Decision and Control, San Diego, CA, pp. 1435-1437 (1979).
Scientia Iranica
Volume 26, Issue 3
Transactions on Industrial Engineering (E)
May and June 2019
Pages 1809-1823

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History

  • Receive Date: 22 November 2016
  • Revise Date: 16 October 2017
  • Accept Date: 23 April 2018

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