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Modarres S, Javadian N. A Stochastic Optimization Model for Designing a Humanitarian Relief Chain Considering Operational and Disruption Risk. sjamao 2022; 4 (2) :1-16
URL: http://sjamao.srpub.org/article-7-154-en.html
URL: http://sjamao.srpub.org/article-7-154-en.html
Mazandaran University of Science & Technology, Mazandaran, Babol , Iran.
Abstract: (294 Views)
Due to the increasing the number of natural disasters such as earthquakes and floods and unnatural disasters such as war and terrorist attacks, Humanitarian Relief Chain (HRC) is taken into consideration of most countries. Besides, this paper aims to contribute humanitarian relief chains under uncertainty. In this paper, we address a humanitarian logistics network design problem including local distribution centers (LDCs) and multiple central warehouses (CWs) and develop a scenario-based stochastic programming (SBSP) approach. Also, the uncertainty associated with demand and supply information as well as the availability of the transportation network's routes level after an earthquake are considered by employing stochastic optimization. While the proposed model attempts to minimize the total costs of the relief chain, it implicitly minimizes the maximum travel time between each pair of facility and the demand point of the items. Additionally, a data set derived from a real disaster case study in the Iran area, and to solve the proposed model a exact method called ɛ-constraint in low dimension along with some well-known evolutionary algorithms are applied. Also, to achieve good performance, the parameters of these algorithms are tuned by using Taguchi method. In addition, the proposed algorithms are compared via four multiobjective metrics and statistically method. Based on the results it was shown that: NSGA-II shows better performances in terms of SNS and CPU time, meanwhile, for NPS and MID, MRGA has better performances. Finally, some comments for future researches are suggested.
Keywords: Humanitarian supply chain, Stochastic programming, Evolutionary
algorithms, Disruption risk
Type of Study: Research |
Subject:
Management of Technology and Innovation
Received: 2022/01/15 | Revised: 2022/02/26 | Accepted: 2022/03/10 | Published: 2022/04/30
Received: 2022/01/15 | Revised: 2022/02/26 | Accepted: 2022/03/10 | Published: 2022/04/30
References
1. Nayeri S, Tavakkoli-Moghaddam R, Sazvar Z, Heydari J. A heuristic-based simulated annealing algorithm for the scheduling of relief teams in natural disasters. Soft Comput. 2021; 1-19. [DOI:10.1007/s00500-021-06425-6]
2. Amali A, Ranjbar A, Heydari J, Nayeri S. A multi-objective stochastic programming model to configure a sustainable humanitarian logistics considering deprivation cost and patient severity. Ann Oper Res. 2021; 1-36. [DOI:10.1007/s10479-021-04014-2]
3. Mamashli Z, Bozorgi-Amiri A, Dadashpour I, et al. A heuristic-based multi-choice goal programming for the stochastic sustainable-resilient routing-allocation problem in relief logistics. Neural Comput Appl. 2021; 1-27. [DOI:10.1007/s00521-021-06074-8]
4. Nayeri S, Asadi-Gangraj E, Emami S, Rezaeian J. Designing a bi-objective decision support model for the disaster management. RAIRO-Operations Res. 2021;
https://doi.org/10.1051/ro/2021144 [DOI:https://doi.org/10.1051/ro/2021144]
5. Hasani A, Mokhtari H. An integrated relief network design model under uncertainty: A case of Iran. Saf Sci. 2019; 111: 22-36. [DOI:10.1016/j.ssci.2018.09.004]
6. Abazari SR, Aghsami A, Rabbani M. Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters. Socioecon Plann Sci. 2021; 74: 100933.
https://doi.org/10.1016/j.seps.2020.100933 [DOI:https://doi.org/10.1016/j.seps.2020.100933]
7. Caunhye AM, Nie X, Pokharel S. Optimization models in emergency logistics: A literature review. Socioecon Plann Sci. 2012; 46: 4-13. [DOI:10.1016/j.seps.2011.04.004]
8. Özdamar L, Ertem MA. Models, solutions and enabling technologies in humanitarian logistics. Eur J Oper Res. 2015; 244: 55-65. [DOI:10.1016/j.ejor.2014.11.030]
9. Zokaee S, Bozorgi-Amiri A, Sadjadi SJ. A robust optimization model for humanitarian relief chain design under uncertainty. Appl Math Model. 2016; 40: 7996-8016. [DOI:10.1016/j.apm.2016.04.005]
10. Sahebjamnia N, Torabi SA, Mansouri SA. A hybrid decision support system for managing humanitarian relief chains. Decis Support Syst. 2016; [DOI:10.1016/j.dss.2016.11.006]
11. Santana Robles F, Hernández-Gress ES, Hernández-Gress N, Granillo Macias R. Metaheuristics in the humanitarian supply chain. Algorithms. 2021; 14: 364. [DOI:10.3390/a14120364]
12. Malmir B, Zobel CW. An applied approach to multi-criteria humanitarian supply chain planning for pandemic response. J Humanit Logist Supply Chain Manag. 2021. [DOI:10.1108/JHLSCM-08-2020-0064]
13. Rad MD, Sadeh E, Sabegh ZA, Rasi RE. Developing a fuzzy goal programming model for optimizing humanitarian supply chain operations. Int J Supply Oper Manag. 2021; 8: 439-457.
14. Ismail I. A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains. Comput Ind Eng. 2021; 157: 107305. [DOI:10.1016/j.cie.2021.107305]
15. Cheraghalipour A, Hajiaghaei-keshteli M. Tree Growth Algorithm (TGA): An effective metaheuristic algorithm inspired by trees behavior. In: 13th International Conference on Industrial Engineering. Scientific Information Databases, Babolsar, 2017; 1-8.
16. Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput. 2002; 6: 182-197. [DOI:10.1109/4235.996017]
17. Al Jadaan O, Rao CR, Rajamani L. Non-dominated ranked genetic Algorithm for solving multi-objective optimization problems: NRGA. J Theor Appl Inf Technol. 2008; 4: 60-67.
18. Karimi N, Zandieh M, Karamooz HR. Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach. Expert Syst Appl. 2010; 37: 4024-4032. [DOI:10.1016/j.eswa.2009.09.005]
19. Taguchi G. Introduction to quality engineering: designing quality into products and processes. 1986.
20. Maghsoudlou H, Kahag MR, Niaki STA, Pourvaziri H. Bi-objective optimization of a three-echelon multi-server supply-chain problem in congested systems: Modeling and solution. Comput Ind Eng. 2016; 99: 41-62. [DOI:10.1016/j.cie.2016.07.008]
21. Sarrafha K, Rahmati SHA, Niaki STA, Zaretalab A. A bi-objective integrated procurement, production, and distribution problem of a multi-echelon supply chain
network design: A new tuned MOEA. Comput Oper Res. 2015; 54: 35-51. [DOI:10.1016/j.cor.2014.08.010]
22. Rahmati SHA, Hajipour V, Niaki STA. A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Appl Soft Comput. 2013; 13: 1728-1740. [DOI:10.1016/j.asoc.2012.12.016]
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