Designing a Multi-Objective Mathematical Model to Locate Four-Echelon Supply Chain Using Meta-Heuristic Algorithms

Subject Areas : Industrial Management

hamid Reza  Mohammadi ¹ (Ph.D. Student, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.)
Reza  Ehtesham Rasi ² (industrial Management,)
Ali  Mohtashami ³ (Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran)

Received: 2020-04-16 Accepted : 2020-11-07 Published : 2020-11-15

Keywords: Four-echelon supply chain, Meta-heuristic Algorithm, multi-objective mathematical model, Perishable goods,

Abstract :

The purpose of this paper is to design a multi-objective mathematical model in order to optimize the four-echelon supply chain of perishable goods using a hybrid algorithm with regard to procurement time, cost and customer satisfaction. Perishable four-echelon food supply chains are considered as different supply chains due to continuous and significant changes in the quality of food products throughout the chain until the end of consumption. In this research, a mathematical model for the location-routing facility in a four-echelon supply chain for perishable products with a simultaneous optimization approach of total supply chain costs, order delivery time, emissions and customer satisfaction is presented. To assess the validity of the research, the mathematical model in Behshahr food industry has been studied and the research problem is presented in the form of a multi-objective nonlinear programming model of mixed integer and to solve it, a hybrid of two refrigeration and red deer algorithms has been used. The results of the proposed algorithm are solved in a case study and the results of the algorithm performance are reviewed based on standard indicators and finally the computational results indicate the efficiency of the algorithm for a wide range of problems of different sizes.

References:

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11. Mousavi, M., & Rayat, F. (2017). A Bi-Objective Green Truck Routing and Scheduling Problem in a Cross Dock with the Learning Effect. Iranian Journal of Operations Research, 8(1), 2-14.
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13. Pishvaee, M. S., & Rabbani, M. (2011). A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment. Advances in Engineering Software, 42(3), 57-63.
14. Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.
15. Sun, S., & Wang, X. (2019). Promoting traceability for food supply chain with certification. Journal of Cleaner Production, 217, 658-665.
16. Yoo, S. H., Kim, D., & Park, M.-S. (2012). Lot sizing and quality investment with quality cost analyses for imperfect production and inspection processes with commercial return. International Journal of Production Economics, 140(2), 922–933.
17. Vahdani, Behnam & Taherverdi, Mohammad Hossein.(2019). Provide a multi-objective planning model for the location-inventory-routing problem in a multi-level supply chain network with a view to maximizing demand coverage. Industrial Management Studies,17(52), 239-286.
18. Wang, X., Guo, H., Yan, R., & Wang, X. (2018). Achieving optimal performance of supply chain under cost information asymmetry. Applied Mathematical Modelling, 53(4): 523-539.
19. 20-Wu, T., Zhang, L. G., & Ge, T. (2019). Managing financing risk in capacity investment under green supply chain competition. Technological Forecasting and Social Change, 143, 37-44.
1. Altiparmak, F., Gen, M., Lin, L. and Paksoy, T. (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, 51:196-215.
2. Asim, Z., Jalil, S. A., & Javaid, S. (2019). An uncertain model for integrated production-transportation closed-loop supply chain network with cost reliability. Sustainable Production and Consumption, 17(3), 298-310.
3. Dai, Z., Aqlan, F., Zheng, X., & Gao, K. (2018). A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & Industrial Engineering, 5(8),15-33.
4. De Keizer, M., Akkerman, R., Grunow, M., Bloemhof, J. M., Haijema, R., & van der Vorst, J. G. (2017). Logistics network design for perishable products with heterogeneous quality decay. European Journal of Operational Research, 262(2), 535-549.
5. Garai, A., Roy, T. K.(2020).Multi-objective optimization of cost-effective and customer-centric closed-loop supply chain management model in T-environment. Soft Computing, 24(1), 155-178.
6. Khodaparasti, S., Bruni, M. E., Beraldi, P., Maleki, H. R., & Jahedi, S. (2018). A multi-period location-allocation model for nursing home network planning under uncertainty. Operations Research for Health Care, 18(2),4-15.
7. Katsaliaki, K., Mustafee, N., & Kumar, S. (2014). A game-based approach towards facilitating decision making for perishable products: An example of blood supply chain. Expert Systems with Applications, 41(9), 4043-4059.
8. Khabooshani,Azam.Yousefi,Ommolbanin.Fadaee,Mahdi.Soltani,Iraj.(2020). Optimization of Closed-Loop Supply Chain with Stability Approach Using Multi- Objective Decision Making DANP Method in Food Industry Case Study in Dairy Industry. Industrial Management, 15(52), 105-126.
9. Kovaˇci´c D, Bogataj Ml. (2013). Reverse logistics facility location using cyclical model of extended MRP theory. Cent Eur J Oper Res, 21(1), 41–57.
10. 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. Morganti, E., & Gonzalez-Feliu, J. (2015). City logistics for perishable products. The case of the Parma's Food Hub. Case Studies on Transport Policy, 3(2), 120-128.
11. Mousavi, M., & Rayat, F. (2017). A Bi-Objective Green Truck Routing and Scheduling Problem in a Cross Dock with the Learning Effect. Iranian Journal of Operations Research, 8(1), 2-14.
12. Rasi, Ehtesham, R. (2018). A Cuckoo Search Algorithm Approach for Multi Objective Optimization in Reverse Logistics Network under Uncertainty Condition. International Journal of Supply and Operations Management (IJSOM), 5(1), 66-80.
13. Pishvaee, M. S., & Rabbani, M. (2011). A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment. Advances in Engineering Software, 42(3), 57-63.
14. Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.
15. Sun, S., & Wang, X. (2019). Promoting traceability for food supply chain with certification. Journal of Cleaner Production, 217, 658-665.
16. Yoo, S. H., Kim, D., & Park, M.-S. (2012). Lot sizing and quality investment with quality cost analyses for imperfect production and inspection processes with commercial return. International Journal of Production Economics, 140(2), 922–933.
17. Vahdani, Behnam & Taherverdi, Mohammad Hossein.(2019). Provide a multi-objective planning model for the location-inventory-routing problem in a multi-level supply chain network with a view to maximizing demand coverage. Industrial Management Studies,17(52), 239-286.
18. Wang, X., Guo, H., Yan, R., & Wang, X. (2018). Achieving optimal performance of supply chain under cost information asymmetry. Applied Mathematical Modelling, 53(4): 523-539.
19. 20-Wu, T., Zhang, L. G., & Ge, T. (2019). Managing financing risk in capacity investment under green supply chain competition. Technological Forecasting and Social Change, 143, 37-44.