A scenario-based stochastic programming approach for designing and planning wheat supply chain (A case study)

Pourmohammadi, F.; Teimoury, E.; Gholamian, M.

Growing Science » Decision Science Letters » A scenario-based stochastic programming approach for designing and planning wheat supply chain (A case study)

Journals

DSL Volumes

Volume 1 (10)
- Issue 1 (5)
- Issue 2 (5)

Volume 2 (30)
- Issue 1 (5)
- Issue 2 (6)
- Issue 3 (9)
- Issue 4 (10)

Volume 3 (53)
- Issue 1 (15)
- Issue 2 (10)
- Issue 3 (19)
- Issue 4 (9)

Volume 4 (48)
- Issue 1 (10)
- Issue 2 (12)
- Issue 3 (14)
- Issue 4 (12)

Volume 5 (39)
- Issue 1 (12)
- Issue 2 (10)
- Issue 3 (8)
- Issue 4 (9)

Volume 6 (30)
- Issue 1 (8)
- Issue 2 (6)
- Issue 3 (9)
- Issue 4 (7)

Volume 7 (41)
- Issue 1 (8)
- Issue 2 (8)
- Issue 3 (8)
- Issue 4 (17)

Volume 8 (38)
- Issue 1 (8)
- Issue 2 (6)
- Issue 3 (14)
- Issue 4 (10)

Volume 9 (39)
- Issue 1 (8)
- Issue 2 (9)
- Issue 3 (14)
- Issue 4 (8)

Volume 10 (43)
- Issue 1 (7)
- Issue 2 (8)
- Issue 3 (20)
- Issue 4 (8)

Volume 11 (49)
- Issue 1 (9)
- Issue 2 (9)
- Issue 3 (14)
- Issue 4 (17)

Volume 12 (64)
- Issue 1 (12)
- Issue 2 (24)
- Issue 3 (13)
- Issue 4 (15)

Volume 13 (78)
- Issue 1 (21)
- Issue 2 (18)
- Issue 3 (19)
- Issue 4 (20)

Countries

Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print) Quarterly Publication Volume 9 Issue 4 pp. 537-546 , 2020

A scenario-based stochastic programming approach for designing and planning wheat supply chain (A case study) Pages 537-546 Download PDF

Authors: Fahimeh Pourmohammadi, Ebrahim Teimoury, Mohammad Reza Gholamian

DOI: 10.5267/j.dsl.2020.8.004

Keywords: Agri-food supply chain, Wheat supply chain, Supply chain network design, Blending, Stochastic scenario-based programming approach

Abstract: Agri-food supply chains have received the attention of many researchers in recent years for various reasons, including food security and health-related issues. Wheat, as a staple food in many countries, is the most cultivated crop in the world. Due to the importance of wheat, this paper proposes a mixed-integer linear mathematical model for redesigning and planning of the wheat supply chain. The proposed model determines the location and capacity of new storage facilities while addressing supplier selection, ordering, storing, transportation, and distribution problems. This model considers the differences between long-term and short-term storage facilities and the quality of wheat. Moreover, the proposed model addresses the uncertainties associated with the quantity of domestic supply and demand through a stochastic scenario-based programming approach. Applicability of this model is investigated using real data from the wheat supply chain of Iran. Results show that seven new long-term storage facilities should be opened, which decreases total costs by 3.45 percent.

How to cite this paper

 Pourmohammadi, F., Teimoury, E & Gholamian, M. (2020). A scenario-based stochastic programming approach for designing and planning wheat supply chain (A case study).Decision Science Letters , 9(4), 537-546.

Refrences

Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1–20.
Asgari, N., Farahani, R. Z., Rashidi-Bajgan, H., & Sajadieh, M. S. (2013). Developing model-based software to optimise wheat storage and transportation: A real-world application. Applied Soft Computing, 13(2), 1074–1084.
Bilgen, B., & Ozkarahan, I. (2007). A mixed-integer linear programming model for bulk grain blending and shipping. International Journal of Production Economics, 107(2), 555–571.
Casals, L. C., & García, B. A. (2015). Wheat interchanges in Europe: Transport optimization reduces emissions. Transportation Research Part D: Transport and Environment, 41, 416–422.
Curtis, B. C. (2002). Wheat in the world. Food and Agriculture Organization.
FAO. (2017). Food outlook biannual report on global food markets. Food and Agriculture Organization.
Gholamian, M. R., & Taghanzadeh, A. H. (2017). Integrated network design of wheat supply chain: A real case of Iran. Computers and Electronics in Agriculture, 140, 139–147.
Hajikhani, A., Khalilzadeh, M., & Sadjadi, S. J. (2018). 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. Scientia Iranica, 25(1), 431–449.
Handayati, Y., Simatupang, T. M., & Perdana, T. (2015). Agri-food supply chain coordination: The state-of-the-art and recent developments. Logistics Research, 8(1), 5.
Irtyshcheva, I., Stehnei, M., Popadynets, N., Danylo, S., Rogatina, L., Bogatyrev, K., Boiko, Y., Hryshyna, N., Ishchenko, O., & Voit, O. (2020). Business process management in the food industry under the conditions of economic transformations. Management Science Letters, 10(14), 3243–3252.
Jifroudi, S., Teimoury, E., & Barzinpour, F. (2020). Designing and planning a rice supply chain: A case study for Iran farmlands. Decision Science Letters, 9(2), 163–180.
Kusumastuti, R. D., van Donk, D. P., & Teunter, R. (2016). Crop-related harvesting and processing planning: A review. International Journal of Production Economics, 174, 76–92.
Mahmoudinia, M., Soleimani Sedehi, M., & Karimi, B. (2016). Wheat supply chain network design using a hub-location approach and considering rail and road transportation modes. Quarterly Journal of Transportation Engineering, 8(1), 125–140.
Maiyar, L. M., & Thakkar, J. J. (2018). Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability. International Journal of Production Economics.
Mogale, D. G., Kumar, M., Kumar, S. K., & Tiwari, M. K. (2018). Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network. Transportation Research Part E: Logistics and Transportation Review, 111, 40–69.
Mogale, D. G., Kumar, S. K., Márquez, F. P. G., & Tiwari, M. K. (2017). Bulk wheat transportation and storage problem of public distribution system. Computers & Industrial Engineering, 104, 80–97.
Mohammadi, Z., Barzinpour, F., & Teimoury, E. (2020). Designing sustainable supply chain network by considering direct and indirect shipment: Evidence from food industry. Decision Science Letters, 9(3), 323–336.
Nguyen, T. G., de Kok, J.-L., & Titus, M. J. (2007). A new approach to testing an integrated water systems model using qualitative scenarios. Environmental Modelling & Software, 22(11), 1557–1571.
Nourbakhsh, S. M., Bai, Y., Maia, G. D., Ouyang, Y., & Rodriguez, L. (2016). Grain supply chain network design and logistics planning for reducing post-harvest loss. Biosystems Engineering, 151, 105–115.
O’Donnell, B., Goodchild, A., Cooper, J., & Ozawa, T. (2009). The relative contribution of transportation to supply chain greenhouse gas emissions: A case study of American wheat. Transportation Research Part D: Transport and Environment, 14(7), 487–492.
Pishvaee, M. S., Fathi, M., & Jolai, F. (2008). A fuzzy clustering-based method for scenario analysis in strategic planning: The case of an Asian pharmaceutical company. South African Journal of Business Management, 39(3), 21–31.
Schwab, P., Cerutti, F., & von Reibnitz, U. H. (2003). Foresight–using scenarios to shape the future of agricultural research. Foresight.
Sheikhi, A., & Nazeman, H. (2004). Developing a model for scheduling and distribution planning of Iran’s wheat imports. Iranian Journal of trade studies (Quarterly), 29(8), 73–102.
Thakur, M., Wang, L., & Hurburgh, C. R. (2010). A multi-objective optimization approach to balancing cost and traceability in bulk grain handling. Journal of Food Engineering, 101(2), 193–200.
Tsolakis, N. K., Keramydas, C. A., Toka, A. K., Aidonis, D. A., & Iakovou, E. T. (2014). Agrifood supply chain management: A comprehensive hierarchical decision-making framework and a critical taxonomy. Biosystems Engineering, 120, 47–64.