Green Inventory Routing Problem using Hybrid Genetic Algorithm
Keywords:Green Inventory Routing Problem, Inventory Routing Problem, Hybrid Genetic Algorithm, Carbon emission, Genetic Algorithm
Carbon dioxide (CO2) is known as one of the largest sources of global warming. One of the ways to curb CO2 emissions is by considering the environmental aspect in the supply chain management. This paper analyses the influence of carbon emissions on the Inventory Routing Problem (IRP). The IRP network consists of a depot, an assembly plant and multiple suppliers. The deterministic demands vary and are determined by the assembly plant. Fixed transportation cost, fuel consumption cost and inventory holding cost are used to evaluate the system’s total cost in which fuel consumption cost is determined by fuel consumption rate, distance, and fuel price. Backordering and split pick-up are not allowed. The main purpose of this study is to analyze the distribution network especially the overall costs of the supply chain by considering the CO2 emissions as well. The problem is known as Green Inventory Routing Problem (GIRP). The mixed-integer linear programming of this problem is adopted from Cheng et al. wherein this study a different Hybrid Genetic Algorithm is proposed at mutation operator. As predicted, GIRP has a higher total cost as it considered fuel consumption cost together with the transportation and inventory costs. The results showed the algorithm led to different sequences of routings considering the carbon dioxide emission in the objective function.
Andersson, H., Hoff, A., Christiansen, M., Hasle, G., & Løkketangen, A. (2010). Industrial aspects and literature survey: Combined inventory management and routing. Computers & operations research, 37(9), 1515-1536. doi:10.1016/j.cor.2009.11.009
Archetti, C., Bertazzi, L., Hertz, A., & Speranza, M. G. (2012). A hybrid heuristic for an inventory routing problem. INFORMS Journal on Computing, 24(1), 101-116. doi:10.1287/ijoc.1100.0439
Bertazzi, L., & Speranza, M. G. (2012). Inventory routing problems: an introduction. EURO Journal on Transportation and Logistics, 1(4), 307-326. doi:10.1007/s13676-012-0016-7
Cachon, G. (2013). Retail Store Density and the Cost of Greenhouse Gas Emissions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2275350
Cheng, C., Qi, M., Wang, X., & Zhang, Y. (2016). Multi-period inventory routing problem under carbon emission regulations. International Journal of Production Economics, 182, 263–275. https://doi.org/10.1016/j.ijpe.2016.09.001
Cordeau, J.-F., Gendreau, M., Laporte, G., Potvin, J.-Y., & Semet, F. (2002). A guide to vehicle routing heuristics. Journal of the Operational Research society, 53(5), 512-522
Dekker, R., Bloemhof, J., & Mallidis, I. (2012). Operations Research for green logistics - An overview of aspects, issues, contributions and challenges. European Journal of Operational Research, 219(3), 671–679. https://doi.org/10.1016/j.ejor.2011.11.010
Hua, G., Qiao, H., & Li, J. (2011). Optimal order lot sizing and pricing with carbon trade. ICEIS 2011 - Proceedings of the 13th International Conference on Enterprise Information Systems, 3 ISAS, 533–536. https://doi.org/10.2139/ssrn.1796507
Lee, C.-G., Bozer, Y. A., & White III, C. (2003). A heuristic approach and properties of optimal solutions to the dynamic inventory routing problem. In: Working Paper.
Mitchell, M. (1998). An introduction to genetic algorithms.
Moin, N. H., & Salhi, S. (2007). Inventory routing problems: a logistical overview. Journal of the Operational Research society, 58(9), 1185-1194. doi:10.1057/palgrave.jors.2602264
Moin, N. H., Ab Halim, H. Z., & Yuliana, T. (2014). Metaheuristics for multi products inventory routing problem with time varying demand. AIP Conference Proceedings, 1605(February 2015), 3–9. https://doi.org/10.1063/1.4887556
Moin, N. H., Salhi, S., & Aziz, N. A. B. (2011). An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem. International Journal of Production Economics, 133(1), 334–343. https://doi.org/10.1016/j.ijpe.2010.06.012
Mustapa, S. I., & Bekhet, H. A. (2016). Analysis of CO2 emissions reduction in the Malaysian transportation sector: An optimisation approach. Energy Policy, 89(2016), 171–183. https://doi.org/10.1016/j.enpol.2015.11.016
Park, Y.-B., Yoo, J.-S., & Park, H.-S. (2016). A genetic algorithm for the vendor-managed inventory routing problem with lost sales. Expert systems with applications, 53, 149-159.
Ramkumar, N., Subramanian, P., Narendran, T. T., & Ganesh, K. (2012). Mixed integer linear programming model for multi-commodity multi-depot inventory routing problem. Opsearch, 49(4), 413–429. https://doi.org/10.1007/s12597-012-0087-0
Salim, A. S. M., Mounira, T., & Ouajdi, K. (2017, October). A Hybrid Genetic Algorithm for the Inventory Routing Problem. In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) (pp. 987-994). IEEE.
Wu, W., Zhou, W., Lin, Y., Xie, Y., & Jin, W. (2021). A hybrid metaheuristic algorithm for location inventory routing problem with time windows and fuel consumption. Expert systems with applications, 166, 114034.
Zhang, S., Lee, C. K. M., Choy, K. L., Ho, W., & Ip, W. H. (2014). Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem. Transportation Research Part D: Transport and Environment, 31, 85–99. https://doi.org/10.1016/j.trd.2014.05.015
How to Cite
Copyright (c) 2021 Journal of Computing Research and Innovation
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.