Honey global supply chain network design using fuzzy optimization approach

Document Type: Research Paper

Authors

1 Iran University of Science and Technology,School of Industrial Engineering, Tehran, iran

2 iran university of science and technology

Abstract

From the past, honey has been known as a healthy product for human life. Iran has a suitable climate for beekeeping and is among the high-ranked countries in honey production. However, due to failure to comply with quality issues, export of honey from Iran is associated with many problems. According to this issue, this paper presents a robust possibilistic optimization network design model for honey global supply chain regarding global issues (e.g. Incoterms) and quality problems. The proposed network design model considers the product quality and its effect on the amount of demand. Numerical results from the robust model compared with the deterministic model show that the proposed robust model provides appropriate solutions with low risk for the decision makers. 

Keywords

Main Subjects


Badri, M. A. (1999). Combining the analytic hierarchy process and goal programming for global facility locationallocation problem. International Journal of Production Economics, 62(3), 237-248.

 

Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming, 88(3), 411-424.

 

Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.

 

Canel, C., & Khumawala, B. M. (1996). A mixed-integer programming approach for the international facilities location problem. International Journal of Operations & Production Management, 16(4), 49-68.

 

Caniato, F., Golini, R., & Kalchschmidt, M. (2013). The effect of global supply chain configuration on the Eq.ship between supply chain improvement programs and performance. International Journal of Production Economics, 143(2), 285-293.

 

Chang, C. T., & Chang, C. C. (2000). A linearization method for mixed 0–1 polynomial programs. Computers & Operations Research, 27(10), 1005-1016.

 

Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation (pp. 265-275).Gabler.

 

Dubois, D., & Prade, H. (1987). Linear programming with fuzzy data. Analysis of fuzzy information, 3, 241-263.

 

Farahani, R. Z., Asgari, N., & Davarzani, H. (Eds.). (2009). Supply chain and logistics in national, international and governmental environment: concepts and models. Springer Science & Business Media.

 

Goh, M., Lim, J. Y., & Meng, F. (2007). A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 182(1), 164-173.

 

Gui-xia, Q., Y.-p. ZHANG, W. Jian-guo and P. Yue-hong (2013). "Revenue sharing in dairy industry supply chain-a case study of Hohhot, China." Journal of Integrative Agriculture 12(12): 2300-2309.

 

Hammami, R., & Frein, Y. (2013). An optimisation model for the design of global multi-echelon

supply chainsunder lead time constraints. International Journal of Production Research, 51(9), 2760-2775.

 

Hammami, R., & Frein, Y. (2014). Redesign of global supply chains with integration of transfer pricing: Mathematical modeling and managerial insights. International Journal of Production Economics, 158, 267-277.

 

Heilpern, S. (1992). The expected value of a fuzzy number. Fuzzy sets and Systems, 47(1), 81-86.

 

Hodder, J. E., & Dincer, M. C. (1986). A multifactor model for international plant location and financing under uncertainty. Computers & Operations Research, 13(5), 601-609.

 

Hodder, J. E., & Jucker, J. V. (1982, December). Plant location modeling for the multinational firm. In Proceedings of the Academy of International Business Conference on the Asia-Pacific Dimension of International Business (pp. 248-258). Honolulu, HI: AIB.

 

 

Iakovou, E., Vlachos, D., Achillas, C., & Anastasiadis, F. (2012). A methodological framework for the design of green supply chains for the agrifood sector. Working paper.

Jamalnia, A., Mahdiraji, H. A., Sadeghi, M. R., Hajiagha, S. H. R., & Feili, A. (2014). An integrated fuzzy QFD and fuzzy goal programming approach for global facility location-allocation problem. International Journal of Information Technology & Decision Making, 13(02), 263-290.

 

Leung, S. C., Tsang, S. O., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multi-site production planning problem in an uncertain environment. European Journal of Operational Research, 181(1), 224-238.

 

Meixell, M. J., & Gargeya, V. B. (2005). Global supply chain design: A literature review and critique.Transportation Research Part E: Logistics and Transportation Review, 41(6), 531-550.

 

Meepetchdee, Y., & Shah, N. (2007). Logistical network design with robustness and complexity considerations. International Journal of Physical Distribution & Logistics Management, 37(3), 201-222.

 

Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281

 

Munson, C. L., & Rosenblatt, M. J. (1997). The impact of local content rules on global sourcing decisions. Production and Operations Management, 6(3), 277-290.

 

Perron, S., Hansen, P., Le Digabel, S., & Mladenović, N. (2010). Exact and heuristic solutions of the global supplychain problem with transfer pricing. European Journal of Operational Research, 202(3), 864-879.

 

Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.

 

Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37(1), 328-344.

 

Sheu, J. B., & Lin, A. Y. S. (2012). Hierarchical facility network planning model for global logistics network configurations. Applied Mathematical Modelling, 36(7), 3053-3066.

 

Shunko, M. A. S. H. A., & Gavirneni, S. R. I. N. A. G. E. S. H. (2007). Role of transfer prices in global supply chains with random demands. Journal of Industrial and Management Optimization, 3(1), 99.

 

Syam, S. S. (1997). A model for the capacitated p-facility location problem in global

environments. Computers & operations research, 24(11), 1005-1016.

 

 

Taylor, D. H. (1997). Global cases in logistics and supply chain management. Cengage Learning EMEA.

 

Tsolakis, N. K., Keramydas, C. A., Toka, A. K., Aidonis, D. A., & Iakovou, E. T. (2014). Agri-food supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosystems Engineering, 120, 47–64.

 

 

Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143-161.