Application of Genetic Algorithm for Optimization of Greenhouse Gas Emissions from Transport and cooling Supply Chain Costs

Subject Areas : Industrial Management

rasoul rezaei ¹ , Davood Gharakhani ² , Reza Ehtesham Rasi ³

1 - Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 - industrial management

Received: 2018-09-06 Accepted : 2020-11-07 Published : 2020-11-14

Keywords: Greenhouse Gases, Genetic Algorithm, Cool Supply Chain Management, Multi-objective Decision Model,

Abstract :

The cooling supply chain, due to its high energy consumption and refrigerant emissions, has high levels of greenhouse gas emissions and is one of the largest carbon emitters. In the cold supply chain, products should be stored at low and near or below freezing points. For this purpose, refrigerated warehouses and refrigerated trucks are essential. Therefore, this research aims to design a linear multi-objective decision-making model for supply chain management Which aims to reduce the overall supply chain cost, including the cost of capacity, transportation, inventory as well as costs associated with the effects of global warming due to greenhouse gas emissions. To analyze the research problem, a mathematical model for optimizing the supply chain has been designed and genetic algorithm has been used to solve this problem. The results of the first function test indicate that the model is high in the number of customers, and when the distributor's number is equal to the number of producers, the best one is possible. The second function analysis concludes that reducing the restoration time of the facility is effective in minimizing the first function, reducing costs and reducing greenhouse gas emissions. Therefore, according to the stated contents and the results obtained in this research, it can be pointed out that by optimizing the vehicles and also the proper use of the optimal number of means of transport, it can be expected that the pollution and proliferation of gases The greenhouse is at least possible.

References:

1. Apte, A. (2010).  Supply Chain Networks for Perishable and Essential Commodities: Design and Vulnerabilities. Operations and Supply ChainManagement, 3(2), 26-43.


2. Benjaafar, S., Li, Y., Daskin, M. (2013). Carbon footprint and the management of supply chains: insights from simple models. IEEE Trans. Autom. Sci. Eng. 10 (1), 99-116.


3. Bojarski .A. D., J. Lainez .M, Espun˜a .A, Puigjaner, L. (2009). Incorporating environmental impacts and regulations in a holistic supply chains modeling: An LCA approach. Comp. & Chem. Engg, 33(10), 1747– 1759.


4. Bouchery .Y, Ghaffari .A, Jemai. Z, Tan, T. (2017). Impact of coordination on costs and carbon emissions for a two-echelon serial economic order quantity problem. European Journal of Operational Research, 260(2),520-533.


5. Bozorgi, A., Zabinski, J., Pazour, J., Nazzal, D. (2016). Cold Supply Chains and Carbon Emissions: RecentWorks and Recommendations.Working paper, accessed Sept.


6. Bradford. J, E.D.G. Fraser. (2008). Local authorities, climate change and small and medium enterprises: Identifying effective policy instruments to reduce energy use and Carbon Emissions. Corporate Social Responsibility and Environmental Management, 15(3),156–172.


7. Chen, X., Benjaafar, S., Elomri, A. (2013). the carbon-constrained EOQ. Oper. Res. Lett., 41 (2), 172–179.


8. Diabat. A, Al-Salem. M. (2015). An integrated supply chain problem with environmental considerations. International Journal of Production Economics, 164, 330-338.


9. Govindan, K., Jafarian, A., Khodaverdi, R., Devika, K., (2014).  Twoechelon  multiple-vehicle  location–routing  problem  with  time  windows for  optimization  of  sustainable  supply  chain  network  of  perishable food. Production Economics, 152, 9-28.


10. Hariga M. As'ad, R. Shamayleh, A. (2017). Integrated economic and environmental models for a multi stage cold supply chain under carbon tax regulation. Journal of Cleaner Production, 166 (2017), 1357-1371.


11. Hoen, K., Tan, T., Fransoo, J., Van Houtum, G. (2014). Effect of carbon emission regulations on transport mode selection under stochastic demand. Flex Serv. Manuf. J., 26, 170-195.


12. Jaber, M., Glock, C., El Saadany, A. (2013). Supply chain coordination with emissions reduction incentives. Int. J. Prod. Res., 51, 69-82.


13. James, S.J., James, C. (2010). The food cold-chain and climate change. Food Res. Int., 43(7), 1944-1956.


14. Kuo J. C, Chen M.C. (2010). Developing an advanced Multi-Temperature Joint Distribution System for the food cold chain. Food Control, 21(4): 559-566.


15. Lan, W., Ya, Z. Z. (2008). A Research on Related Questions of Chinese Food Cold Chain Development. International Conference on Management of e-Commerce and e-Government, Jiangxi, China, pp.18 – 21.


16. Mallidis, I., Dekker, R., Vlachos, D., (2012). The impact of greening on supply  chain  design  and  cost:  a  case  for  a  developing  region. Transport Geography, 22, 118–128.


17. Modak N.M, Ghosh D.K, Panda S, Sana S.S, (2017).  Managing  greenhouse  gas  emission  cost  and  pricing  policies  in  a  two-echelon  supply chain,  NULL, http://dx.doi.org/10.1016/j.cirpj.2017.08.001.


18. Montanari. R., (2008). Cold chain tracking: a managerial perspective. Trends in Food Science & Technology, 19(8), 425-431.


19. Qi. Q, Wang .J, Bai .Q. (2017). Pricing decision of a two-echelon supply chain with one supplier and two retailers under a carbon cap regulation. Journal of Cleaner Production, 151, 286-302.


20. Ramudhin. A, Chaabane .A, Kharoune. M, Paquet .M, (2008). Carbon market sensitive green supply chain network design. IEEE Int. Conf. Indust. Engg. And Engg. Mgmt., pp. 1093–1097.


21. Rong, A., Akkerman, R., Grunow, M. (2011). An optimization approach for managing fresh food quality throughout the supply chain. ProductionEconomics, 131, 421-429.


22. Saif, A., Elhedhli, S. (2016). Cold supply chain design with environmental considerations: a simulation-optimization approach. Eur. J. Oper. Res. 251, 274-287.


23. Salin V, Nayga R. M. (2003). A cold chain network for food exports to developing countrie. International Journal of Physical Distribution& Lgistics Management, 33(10), 918-933.


24. Shabani, A., Torabipour, S.M.R., Farzipoor Saen, R. (2011). Container Selection in the Presence of Partial Dual-Role Factors. International Journal of Physical Distribution & Logistics Management, 41(10), 991 - 1008.


25. Wang .F, Lai .X, Shi .N, (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51 (2) 262 – 269.


26. Xin H., Ruhe X., Guanghai L. (2009). Analysis on Temperature Field of Refrigerated Car in Cold Chain Logistics.Measuring Technology and Mechatronics Automation International Conference, Zhangjiajie, pp.673-676.


27. Xu. J, Chen .Y, Bai. Q. (2016). A two-echelon sustainable supply chain coordination under cap-and-trade regulation. Journal of Cleaner Production, 135 (1), 42-56


28. Yakavenka, V. Mallidis, I. Siamas, I. Vlachos, D. (2016). A Decision Support System for Cold SupplyChain Network Design. 11^th MIBES Conference – Heraklion, Crete, Greece, 22-24 June 2016.


29. Yang. L, Zhang. Q, Ji .J. (2017). Pricing and carbon emission reduction decisions in supply chains with vertical and horizontal cooperation. International Journal of Production Economics, 191, 286-297.