Green Inventory Routing Problem using Hybrid Genetic Algorithm

Green Inventory Routing Problem using Hybrid Genetic Algorithm

Authors

  • Huda Zuhrah Ab Halim Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nureffa Natasha Mohd Azliana Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nuridawati Baharom Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nur Fatihah Fauzi Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nurizatul Syarfinas Ahmad Bakhtiar Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nur Izzati Khairudin Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v6i4.254

Keywords:

Green Inventory Routing Problem, Inventory Routing Problem, Hybrid Genetic Algorithm, Carbon emission, Genetic Algorithm

Abstract

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.

Downloads

Download data is not yet available.

References

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

Downloads

Published

2021-10-01

How to Cite

Ab Halim, H. Z., Mohd Azliana, N. N. ., Baharom, N., Fauzi, N. F. ., Ahmad Bakhtiar, N. S. ., & Khairudin, N. I. . (2021). Green Inventory Routing Problem using Hybrid Genetic Algorithm. Journal of Computing Research and Innovation, 6(4), 10–20. https://doi.org/10.24191/jcrinn.v6i4.254

Issue

Section

General Computing

Most read articles by the same author(s)

<< < 1 2 

Similar Articles

You may also start an advanced similarity search for this article.

Loading...