A fuzzy chance-constrained programming model for integrated planning of the wheat supply chain considering wheat quality and sleep period: A case study

Document Type : Article

Authors

Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Wheat is a staple food in many countries, and as a result, the first most cultivated crop worldwide. Since wheat is a vital product in terms of food security, and its supply chain needs to be studied and planned carefully. This paper proposes a mixed-integer linear programming model for integrated planning of imported and domestically-produced wheat that addresses supplier selection, order planning, transportation, storage, and distribution problems at the same time. Specifically, this model focuses on the wheat quality and wheat sleep period. Moreover, differentiation of long-term and short-term storage facilities and consideration of intra-layer flows between storage facilities are other characteristics of this model. A fuzzy chance-constrained programming approach is employed to cope with the uncertainties associated with domestic supply, demand, and global wheat prices. Applicability and advantages of the developed model are demonstrated using real data from the wheat supply chain of Iran. Results show that the current status of the wheat supply chain of Iran is far from being optimal, and there are many opportunities for improvement.

Keywords

References

References:
1. Bedford, D., Claro, J., Giusti, A.M., et al., Food Outlook Biannual Report on Global Food Markets (2017).
2. Denicof, M.R., Prater, M.E., and Bahizi, P., Wheat Transportation Profile, United States Department of Agriculture (2014).
3. Ahumada, O. and Villalobos, J.R. "Application of planning models in the agri-food supply chain: A review", Eur. J. Oper. Res., 196(1), pp. 1-20 (2009).
4. Tsolakis, N.K., Keramydas, C.A., Toka, A.K., et al. "Agrifood supply chain management: A comprehensive hierarchical decision-making framework and a critical taxonomy", Biosyst. Eng., 120, pp. 47-64 (2014).
5. Handayati, Y., Simatupang, T.M., and Perdana, T. "Agri-food supply chain coordination: the state-of-theart and recent developments", Logist. Res., 8(1), p. 5 (2015).
6. Kusumastuti, R.D., Van Donk, D.P., and Teunter, R. "Crop-related harvesting and processing planning: A review", Int. J. Prod. Econ., 174, pp. 76-92 (2016).
7. Sheikhi, A. and Nazeman, H. "Developing a model for scheduling and distribution planning of Iran's wheat imports", Iran. J. Trade Stud. Q., 29(8), pp. 73-102 (2004).
8. Bilgen, B. and Ozkarahan, I. "A mixed-integer linear programming model for bulk grain blending and shipping", Int. J. Prod. Econ., 107(2), pp. 555-571 (2007).
9. O'Donnell, B., Goodchild, A., Cooper, J., et al. "The relative contribution of transportation to supply chain greenhouse gas emissions: A case study of American wheat", Transp. Res. Part Transp. Environ., 14(7), pp. 487-492 (2009).
10. Thakur, M., Wang, L., and Hurburgh, C.R. "A multiobjective optimization approach to balancing cost and traceability in bulk grain handling", J. Food Eng., 101(2), pp. 193-200 (2010).
11. Asgari, N., Farahani, R.Z., Rashidi-Bajgan, H., et al. "Developing model-based software to optimise wheat storage and transportation: A real-world application", Appl. Soft Comput., 13(2), pp. 1074-1084 (2013).
12. Casals, L.C. and Garcia, B.A. "Wheat interchanges in Europe: Transport optimization reduces emissions", Transp. Res. Part Transp. Environ., 41, pp. 416-422 (2015).
13. An, K. and Ouyang, Y. "Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium", Transp. Res. Part E Logist. Transp. Rev., 88, pp. 110-128 (2016).
14. Hyland, M.F., Mahmassani, H.S., and Mjahed, L.B. "Analytical models of rail transportation service in the grain supply chain: Deconstructing the operational and economic advantages of shuttle train service", Transp. Res. Part E Logist. Transp. Rev., 93, pp. 294- 315 (2016).
15. Mahmoudinia, M., Soleimani Sedehi, M., and Karimi, B. "Wheat supply chain network design using a hublocation approach and considering rail and road transportation modes", Q. J. Transp. Eng., 8(1), pp. 125- 140 (2016).
16. Nourbakhsh, S.M., Bai, Y., Maia, G.D., et al. "Grain supply chain network design and logistics planning for reducing post-harvest loss", Biosyst. Eng., 151, pp. 105-115 (2016).
17. Gholamian, M.R. and Taghanzadeh, A.H. "Integrated network design of wheat supply chain: A real case of Iran", Comput. Electron. Agric., 140, pp. 139-147 (2017).
18. Mogale, D.G., Kumar, S.K., Marquez, F.P.G., et al. "Bulk wheat transportation and storage problem of public distribution system", Comput. Ind. Eng., 104, pp. 80-97 (2017).
19. Teimoury, E., Pourmohammadi, F., and Seyyed Jifroudi, S.A. "An integrated location-allocation model for redesigning Iran's wheat supply chain network: a robust optimization approach", In The First International Conference on Systems Optimization and Business Management, Babol, Iran (2017).
20. Essien, E., Dzisi, K.A., and Addo, A. "Decision support system for designing sustainable multistakeholder networks of grain storage facilities in developing countries", Comput. Electron. Agric., 147, pp. 126-130 (2018).
21. Hajikhani, A., Khalilzadeh, M., and Sadjadi, S.J. "A fuzzy multi-objective multi-product supplier selection and order allocation problem in supply chain under coverage and price considerations: An urban agricultural case study", Sci. Iran., 25(1), pp. 431-449 (2018).
22. Mogale, D.G., Kumar, M., Kumar, S.K., et al. "Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network", Transp. Res. Part E Logist. Transp. Rev., 111, pp. 40-69 (2018).
23. Maiyar, L.M. and Thakkar, J.J. "Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability", Int. J. Prod. Econ., International Journal of Production Economics, 217, pp. 281-297 (2019).
24. Mula, J., Poler, R., and Garcia, J.P. "MRP with flexible constraints: A fuzzy mathematical programming approach", Fuzzy Sets Syst., 157(1), pp. 74-97 (2006).
25. Azadeh, A., Kokabi, R., and Hallaj, D. "Credibilitybased fuzzy mathematical programming for biobjective capacitated partial facility interdiction with fortification and demand outsourcing model", Sci. Iran. Trans. E Ind. Eng., 24(2), p. 778 (2017).
26. Liu, B. and Iwamura, K. "Chance constrained programming with fuzzy parameters", Fuzzy Sets Syst., 94(2), pp. 227-237 (1998).
27. Pishvaee, M.S., Razmi, J., and Torabi, S.A. "Robust possibilistic programming for socially responsible supply chain network design: A new approach", Fuzzy Sets Syst., 206, pp. 1-20 (2012).
28. Hajiagha, R., Hossein, S., Hashemi, S.S., et al. "Hybrid fuzzy-stochastic approach for multi-product, multi-period, and multi-resource master production scheduling problem: case of a polyethylene pipe and fitting manufacturer", Sci. Iran., 26(3), pp. 1809-1823 (2019).
29. Dubois, D. and Prade, H. "The mean value of a fuzzy number", Fuzzy Sets Syst., 24(3), pp. 279-300 (1987).
30. Heilpern, S. "The expected value of a fuzzy number", Fuzzy Sets Syst., 47(1), pp. 81-86 (1992).
31. Zadeh, L.A. "Fuzzy sets as a basis for a theory of possibility", Fuzzy Sets Syst., 1(1), pp. 3-28 (1978).
Scientia Iranica
Volume 29, Issue 5
Transactions on Industrial Engineering (E)
September and October 2022
Pages 2593-2609

Files

History

  • Receive Date: 13 June 2019
  • Revise Date: 28 June 2020
  • Accept Date: 18 October 2020

Share

How to cite

Statistics