A mathematical programming model for sustainable agricultural supply chain network design under uncertainty

Document Type : Research Paper

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Associate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

10.22070/jqepo.2020.5666.1164

Abstract

Evaluating the sustainability in the crop supply chain plays an important role in creating an efficient supply chain and increasing food safety in developing countries. Therefore, in the present study, an efficient network of greenhouses, agricultural lands, processing plants, and agricultural distribution centers (farmer’s markets) have been designed with regards to the sustainability dimension and with the purpose of minimizing the costs, minimizing the greenhouse gas emissions, and maximizing employment. The uncertainty has been identified as an inevitable part of studies in some parameters such as Brix, deterioration rate, employment rate, ideal yield, and water consumption rate. Also, the use of agricultural methods and techniques such as crop rotation policy, the use of nurtured seedling, and the consideration of Brix are among the issues that have been considered to increase the efficiency of the products and consequently to increase the economic efficiency. The model proposed with augmented ε-constraint method has been solved, and its efficiency has been investigated by presenting a case study in Iran. The results of the model solution indicate that the use of different planting policies, the consideration of Brix index, and effective attention to crop rotation policy has led to higher productivity of agricultural lands, production of higher quality products by expending lower prices and preserving environmental resources such as soil and water; so that the water consumption is reduced by 65.258%. Furthermore, the proposed model increases employment in the region and reduces unemployment by 11.2%

Keywords

References

Allaoui, H., Guo, Y., Choudhary, A., & Bloemhof, J. (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computers & Operations Research, 89, 369-384.
Banasik, A., Kanellopoulos, A., Bloemhof-Ruwaard, J. M., & Claassen, G. D. H. (2019). Accounting for uncertainty in eco-efficient agri-food supply chains: A case study for mushroom production planning. Journal of cleaner production, 216, 249-256.‏
Blackburn, J., & Scudder, G. (2009). Supply chain strategies for perishable products: the case of fresh produce. Production and Operations Management, 18(2), 129-137.
Castro, N. R., & Swart, J. (2017). Building a roundtable for a sustainable hazelnut supply chain. Journal of Cleaner Production, 168, 1398-1412.
Catalá, L. P., Durand, G. A., Blanco, A. M., & Bandoni, J. A. (2013). Mathematical model for strategic planning optimization in the pome fruit industry. Agricultural Systems, 115, 63-71.
Donald, B. (2008). Food systems planning and sustainable cities and regions: the role of the firm in sustainable food capitalism. Regional Studies, 42(9), 1251-1262.
Derqui, B., Fayos, T., & Fernandez, V. (2016). Towards a more sustainable food supply chain: opening up invisible waste in food service. Sustainability, 8(7), 693.
Ghezavati, V. R., Hooshyar, S., & Tavakkoli-Moghaddam, R. (2017). A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato. Central European Journal of Operations Research, 25(1), 29-54.
Glover, F. (1975). Improved linear integer programming formulations of nonlinear integer problems. Management Science, 22(4), 455-460.
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., ... & Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. science, 327(5967), 812-818.
Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28.
Heilpern, S. (1992). The expected value of a fuzzy number. Fuzzy sets and Systems, 47(1), 81-86.
Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
Jiménez, M. (1996). Ranking fuzzy numbers through the comparison of its expected intervals. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 4(04), 379-388.
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194.
Krishnan, R., Agarwal, R., Bajada, C., & Arshinder, K. (2020). Redesigning a food supply chain for environmental sustainability–An analysis of resource use and recovery. Journal of Cleaner Production, 242, 118374.‏
Le, T., Pék, Z., Takács, S., Neményi, A., & Helyes, L. (2018). The effect of plant growth-promoting rhizobacteria on yield, water use efficiency and Brix Degree of processing tomato. Plant, Soil and Environment, 64(11), 523-529.
Li, M., Fu, Q., Singh, V. P., Liu, D., & Li, T. (2019). Stochastic multi-objective modeling for optimization of water-food-energy nexus of irrigated agriculture. Advances in water resources, 127, 209-224.‏
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.‏
Mavrotas, G. (2007). Generation of efficient solutions in Multiobjective Mathematical Programming problems using GAMS. Effective implementation of the ε-constraint method. Lecturer, Laboratory of Industrial and Energy Economics, School of Chemical Engineering. National Technical University of Athens.
Mogale, D. G., Cheikhrouhou, N., & Tiwari, M. K. (2019). Modelling of sustainable food grain supply chain distribution system: a bi-objective approach. International Journal of Production Research, 1-24.‏
Moghaddam, S. T., Javadi, M., & Molana, S. M. H. (2019). A reverse logistics chain mathematical model for a sustainable production system of perishable goods based on demand optimization. Journal of Industrial Engineering International, 15(4), 709-721.‏
Quadra, P. R. B., Rodriguez, J. M., Terol, M. C. R., & Cruz, D. E. (2009, December). A three level multi-period multi-location and multi-crop sustainable supply chain model. In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1414-1418). IEEE.‏
Ren, C., Li, Z., & Zhang, H. (2019). Integrated multi-objective stochastic fuzzy programming and AHP method for agricultural water and land optimization allocation under multiple uncertainties. Journal of cleaner production, 210, 12-24.‏
Rocco, C. D., & Morabito, R. (2016). Production and logistics planning in the tomato processing industry: A conceptual scheme and mathematical model. Computers and electronics in agriculture, 127, 763-774.‏
Rocco, C. D., & Morabito, R. (2016). Robust optimisation approach applied to the analysis of production/logistics and crop planning in the tomato processing industry. International Journal of Production Research, 54(19), 5842-5861.‏
Sazvar, Z., Mirzapour Al-e-Hashem, S. M. J., Baboli, A., & Jokar, M. A. (2014). A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products. International Journal of Production Economics, 150, 140-154.
Sinha, A. K., & Anand, A. (2020). Optimizing supply chain network for perishable products using improved bacteria foraging algorithm. Applied Soft Computing, 86, 105921.‏
Soto-Silva, W. E., González-Araya, M. C., Oliva-Fernández, M. A., & Plà-Aragonés, L. M. (2017). Optimizing fresh food logistics for processing: Application for a large Chilean apple supply chain. Computers and electronics in agriculture, 136, 42-57.‏
Van Der Vorst, J. G., Tromp, S. O., & Zee, D. J. V. D. (2009). Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics. International Journal of Production Research, 47(23), 6611-6631.‏
Wang, J., Yao, S., Sheng, J., & Yang, H. (2019b). Minimizing total carbon emissions in an integrated machine scheduling and vehicle routing problem. Journal of Cleaner Production, 229, 1004-1017.‏
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
 
Journal of Quality Engineering and Production Optimization
Volume 5, Issue 1
June 2020
Pages 189-220

Files

Share

How to cite

Statistics