Modeling and Solving a Multi-objective Location-Routing Problem Considering the Evacuation of Casualties and Homeless People and Fuzzy Paths in Relief Logistics

Salehi, Mojtaba; Jabarpour, Efat

doi:10.30473/coam.2021.53682.1142

Document Type : Research Article

Authors

Mojtaba Salehi ¹
Efat Jabarpour ²

¹ Industrial Engineering Department‎, ‎Payame Noor University‎, ‎Tehran‎, ‎Iran

² ‎Faculty of Management‎, ‎University of Tehran‎, ‎Tehran‎, ‎Iran‎.

https://doi.org/10.30473/coam.2021.53682.1142

Abstract

The relief logistics and humanitarian supply chain in academic literature refer to the process of planning‎, ‎execution‎, ‎and effective controlling of the flow of costs and information and storage of necessary goods and materials from the point of origin to consumption with the primary purpose of reducing and relieving the affected people suffer. This paper discusses a multi-objective model for multi-period location-distribution-routing problems considering the evacuation of casualties and homeless people and fuzzy paths in relief logistics‎. ‎Firstly‎, ‎an uncertain multi-objective model of the problem was developed based on uncertain parameters of demand‎, ‎time‎, ‎and transport capacity‎, ‎and then‎, ‎using the fuzzy programming method‎, ‎uncertain parameters of the problem were controlled‎. ‎As the problem is NP-hard and GAMS software has not able to solve the model in larger sizes‎, meta-heuristic algorithms of NSGA-II and MOPSO were used to solve the problem.

Keywords

References

[1] Abounacer R., Rekik M., Renaud, J. 2014. “An exact solution approach for multi-objective location–transportation problem for disaster response”, Computers & Operations Research, 41, 83-93.

[2] An S., Cui N., Li X., Ouyang, Y. 2013. “Location planning for transit-based evacuation under the risk of service disruptions”, Transportation Research Part B: Methodological, 54, 1-16.

[3] Boonmee C., Arimura M., Asada T. 2017. “Facility location optimization model for emergency humanitarian logistics”, International Journal of Disaster Risk Reduction, 24, 485-498.

[4] Bozorgi-Amiri A., Jabalameli M. S., Mirzapour Al-e-Hashem S. M. J. 2011. “A multiobjective robust stochastic programming model for disaster relief logistics under uncertainty”, OR Spectrum DOI 10.1007/s00291-011-0268-x.

[5] Bozorgi-Amiri A., Khorsi M. 2016. “A dynamic multi-objective location–routing model for relief logistic planning under uncertainty on demand, travel time, and cost parameters”, The International Journal of Advanced Manufacturing Technology, 85(5-8), 1633-1648.

[6] Coello, C.C., Dehuri, S., Ghosh, S. 2009. “Swarm intelligence for multi-objective problems in data mining”, Vol. 242, Springer. https://doi.org/10.1007/978-3-642-03625-5 .

[7] Dotoli M., Epicoco N., Falagario M. 2017. “A fuzzy technique for supply chain network design with quantity discounts”, International Journal of Production Research, 55(7), 1862- 1884.

[8] Elçi Ö., Noyan N. 2018. “A chance-constrained two-stage stochastic programming model for humanitarian relief network design”, Transportation research part B: methodological, 108, 55-83.

[9] Hashemi Borzabadi A., Hasanabadi M., Sadjadi N. 2016. “Approximate Pareto optimal solutions of multi-objective optimal control problems by evolutionary algorithms”, Control and Optimization in Applied Mathematics, 1(1), 1-19.

[10] Hu S. L., Han C. F., Meng L. P. 2017. “Stochastic optimization for joint decision making of inventory and procurement in humanitarian relief”, Computers & Industrial Engineering, 111, 39-49.

[11] Khan S., Kamran M., Rehman O. U., Liu L., Yang S. 2018. “A modified PSO algorithm with dynamic parameters for solving complex engineering design problem”, International Journal of Computer Mathematics, 95(11), 2308-2329.

[12] Liang T. F. 2011. “Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains”, Information Sciences, 181(4), 842-854.

[13] Moore J., Chapman R. 1999. “Application of Particle Swarm to Multiobjective Optimization: Dept. Comput. Sci. Software Eng.”, Auburn Univ.

[14] Murali P., Ordóñez F., Dessouky M. M. 2012. “Facility location under demand uncertainty: Response to a large-scale bio-terror attack”, Socio-Economic Planning Sciences, 46(1), 78-87.

[15] Oh S., Haghani A. 1996. “Formulation and Solution of a Multi-Commodity”, Multi-Modal Network Flow Model for Disaster Relief Operations. Transport. Res., 30, 231–250.

[16] Ozdamar L. 2011. “Planning helicopter logistics in disaster relief. OR spectrum”, 33(3), 655-672.

[17] Rawls C. G., Turnquist M. A. 2010. “Pre-positioning of emergency supplies for disaster response”, Transportation research part B: Methodological, 44(4), 521-534.

[18] Rezaei-Malek M., Tavakkoli-Moghaddam R. 2016. “Robust humanitarian relief logistics network planning”, Uncertain Supply Chain Management, 2(2), 73-96.

[19] Torabi S. A., Shokr I., Tofighi S., Heydari J. 2018. “Integrated relief pre-positioning and procurement planning in humanitarian supply chains”, Transportation Research Part E: Logistics and Transportation Review, 113, 123-146.

[20] Yahyaei M., Bozorgi-Amiri A. 2018. “Robust reliable humanitarian relief network design: an integration of shelter and supply facility location”, Annals of Operations Research, 1-20.

[21] Zokaee Sh., Bozorgi-Amiri A., Sadjadi S. J. 2016. “A Robust Optimization Model for Humanitarian Relief Chain Design under Uncertainty”, Applied Mathematical Modelling, 1-43.

Modeling and Solving a Multi-objective Location-Routing Problem Considering the Evacuation of Casualties and Homeless People and Fuzzy Paths in Relief Logistics

References

References

Volume 5, Issue 1 Open Access PolicyWinter-Spring 2020January 2020Pages 41-65

Volume 5, Issue 1
Open Access Policy
Winter-Spring 2020
January 2020
Pages 41-65