Optimizing disaster relief goods distribution and transportation: a mathematical model and metaheuristic algorithms

References

• 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
   Web of Science ®Google Scholar
• Kabak Ö, Ülengin F. Possibilistic linear-programming approach for supply chain networking decisions. Eur J Oper Res. 2011;209(3):253–264. doi:10.1016/j.ejor.2010.09.025
   Web of Science ®Google Scholar
• Bisri MBF, Beniya S. Analyzing the national disaster response framework and inter-organizational network of the 2015 Nepal/Gorkha earthquake. Proc Eng. 2016;159:19–26. doi:10.1016/j.proeng.2016.08.059
   Google Scholar
• Ergun Ö, Gui L, Stamm JLH, et al. Improving humanitarian operations through technology-enabled collaboration. Prod Oper Manage. 2014;23(6):1002–1014. doi:10.1111/poms.12107
   Web of Science ®Google Scholar
• Altay N, Green WG. OR/MS research in disaster operations management. Eur J Oper Res. 2006;175(1):475–493. doi:10.1016/j.ejor.2005.05.016
   Web of Science ®Google Scholar
• Kumar S, Chauhan RP, Momani S, et al. Numerical investigations on COVID-19 model through singular and non-singular fractional operators. Numer Methods Partial Differ Equat. 2020;1(1):1–27. doi:10.1002/num.22707
   Google Scholar
• Khan MA, Ullah S, Kumar S. A robust study on 2019-nCOV outbreaks through non-singular derivative. Eur Phys J Plus. 2021;136(2):168. doi:10.1140/epjp/s13360-021-01159-8
   PubMed Web of Science ®Google Scholar
• Kumar S, Kumar A, Samet B, et al. A study on fractional host–parasitoid population dynamical model to describe insect species. Numer Methods Partial Differ Equat. 2021;37(2):1673–1692. doi:10.1002/num.22603
   Web of Science ®Google Scholar
• Kumar S, Kumar R, Osman MS, et al. A wavelet based numerical scheme for fractional order SEIR epidemic of measles by using Genocchi polynomials. Numer Methods Partial Differ Equat. 2021;37(2):1250–1268. doi:10.1002/num.22577
   Web of Science ®Google Scholar
• Mohammadi H, Kumar S, Rezapour S, et al. A theoretical study of the Caputo–Fabrizio fractional modeling for hearing loss due to Mumps virus with optimal control. Chaos, Solitons Fractals. 2021;144:110668. doi:10.1016/j.chaos.2021.110668
   Web of Science ®Google Scholar
• Kumar S, Kumar R, Cattani C, et al. Chaotic behaviour of fractional predator-prey dynamical system. Chaos, Solitons Fractals. 2020;135:109811. doi:10.1016/j.chaos.2020.109811
   Web of Science ®Google Scholar
• Zahedi A, Salehi-Amiri A, Smith NR, et al. Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic. Appl Soft Comput. 2021;104:107210. doi:10.1016/j.asoc.2021.107210
   PubMed Web of Science ®Google Scholar
• Torabi SA, Shokr I, Tofighi S, et al. Integrated relief pre-positioning and procurement planning in humanitarian supply chains. Transp Res Part E Logistics Transp Rev. 2018;113:123–146. doi:10.1016/j.tre.2018.03.012
   Web of Science ®Google Scholar
• Chen Y. Relief goods distribution problem: considering donation strategies, fairness, and interventions. Progr Disaster Sci. 2021;12:100198. doi:10.1016/j.pdisas.2021.100198
   Google Scholar
• Zheng Y-J, Ling H-F. Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach. Soft Comput. 2013;17(7):1301–1314. doi:10.1007/s00500-012-0968-4
   Web of Science ®Google Scholar
• Ruan JH, Wang XP, Chan FTS, et al. Optimizing the intermodal transportation of emergency medical supplies using balanced fuzzy clustering. Int J Prod Res. 2016;54(14):4368–4386. doi:10.1080/00207543.2016.1174344
   Web of Science ®Google Scholar
• Ben Othman S, Zgaya H, Dotoli M, et al. An agent-based decision support system for resources’ scheduling in emergency supply chains. Control Eng Pract. 2017;59:27–43. doi:10.1016/j.conengprac.2016.11.014
   Web of Science ®Google Scholar
• Manopiniwes W, Irohara T. Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response. Int J Prod Res. 2017;55(4):979–996. doi:10.1080/00207543.2016.1211340
   Web of Science ®Google Scholar
• Condeixa LD, Leiras A, Oliveira F, et al. Disaster relief supply pre-positioning optimization: A risk analysis via shortage mitigation. Int J Disaster Risk Reduct. 2017;25:238–247. doi:10.1016/j.ijdrr.2017.09.007
   Web of Science ®Google Scholar
• Loree N, Aros-Vera F. Points of distribution location and inventory management model for Post-Disaster Humanitarian Logistics. Transp Res Part E Logistics Transp Rev. 2018;116:1–24. doi:10.1016/j.tre.2018.05.003
   Web of Science ®Google Scholar
• Sabouhi F, Bozorgi-Amiri A, Moshref-Javadi M, et al. An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study. Ann Oper Res. 2019;283(1):643–677. doi:10.1007/s10479-018-2807-1
   Web of Science ®Google Scholar
• Shavarani SM. Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution. J Humanitarian Logistics Supply Chain Manage. 2019;9(1):70–81. doi:10.1108/JHLSCM-05-2018-0036
   Web of Science ®Google Scholar
• Liu X, Song X. Emergency operations scheduling for a blood supply network in disaster reliefs. IFAC-PapersOnLine. 2019;52(13):778–783. doi:10.1016/j.ifacol.2019.11.210
   Google Scholar
• Sakiani R, Seifi A, Khorshiddoust RR. Inventory routing and dynamic redistribution of relief goods in post-disaster operations. Comput Ind Eng. 2020;140:106219. doi:10.1016/j.cie.2019.106219
   Web of Science ®Google Scholar
• Ghaffari Z, Nasiri MM, Bozorgi-Amiri A, et al. Emergency supply chain scheduling problem with multiple resources in disaster relief operations. Transportmetrica A Transp Sci. 2020;16(3):930–956. doi:10.1080/23249935.2020.1720858
   Google Scholar
• Wang C, Chen S. A distributionally robust optimization for blood supply network considering disasters. Transp Res Part E Logistics Transp Rev. 2020;134:101840. doi:10.1016/j.tre.2020.101840
   Web of Science ®Google Scholar
• Zhong S, Cheng R, Jiang Y, et al. Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand. Transp Res Part E Logistics Transp Rev. 2020;141:102015. doi:10.1016/j.tre.2020.102015
   Web of Science ®Google Scholar
• Ransikarbum K, Mason SJ. A bi-objective optimisation of post-disaster relief distribution and short-term network restoration using hybrid NSGA-II algorithm. Int J Prod Res. 2021: 1–25. doi:10.1080/00207543.2021.1970846
   Web of Science ®Google Scholar
• Mosallanezhad B, Chouhan VK, Paydar MM, et al. Disaster relief supply chain design for personal protection equipment during the COVID-19 pandemic. Appl Soft Comput. 2021;112:107809. doi:10.1016/j.asoc.2021.107809
   PubMed Web of Science ®Google Scholar
• Cao C, Liu Y, Tang O, et al. A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. Int J Prod Econom. 2021;235:108081. doi:10.1016/j.ijpe.2021.108081
   Web of Science ®Google Scholar
• Yuan Y, Wang F. An emergency supplies scheduling for chemical industry park: based on super network theory. Environ Sci Pollut Res Int. 2022;29(26):39345–39358. doi:10.1007/s11356-021-18182-y
   PubMed Web of Science ®Google Scholar
• Beheshtinia MA, Ghasemi A. A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system. Eng Optim. 2018;50(9):1415–1433. doi:10.1080/0305215X.2017.1400546
   Web of Science ®Google Scholar
• Najian MH, Beheshtinia MA. Supply chain scheduling using a transportation system composed of vehicle routing problem and cross-docking approaches. Int J Transp Eng. 2019;7(1):1–19. doi:10.22119/ijte.2018.116845.1388
   Google Scholar
• Beheshtinia MA, Fathi M. Energy-efficient and sustainable supply chain in the manufacturing industry. Energy Sci Eng. 2023;11(1):357–382. doi:10.1002/ese3.1337
   Web of Science ®Google Scholar
• Osaba E, Diaz F, Onieva E. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl Intelligence. 2014;41(1):145–166. doi:10.1007/s10489-013-0512-y
   Web of Science ®Google Scholar
• Moosavian N, Kasaee Roodsari B. Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm Evol Comput. 2014;17:14–24. doi:10.1016/j.swevo.2014.02.002
   Web of Science ®Google Scholar
• Razmjooy N, Estrela VV, Loschi HJ. Entropy-based breast cancer detection in digital mammograms using world cup optimization algorithm. Int J Swarm Intelligence Res (IJSIR). 2020;11(3):1–18. doi:10.4018/IJSIR.2020070101
   Web of Science ®Google Scholar
• Purnomo HD, Wee H-M. Soccer game optimization with substitute players. J Comput Appl Math. 2015;283:79–90. doi:10.1016/j.cam.2015.01.008
   Web of Science ®Google Scholar
• Venkatesan Y, Srilakshmi K, Palanivelu A. Modified soccer game optimization and its application on power flow and distribution generation placement problems of distribution systems. Evol Intelligence. 2023;16(2):539–552. doi:10.1007/s12065-021-00677-9
   Web of Science ®Google Scholar
• Husseinzadeh Kashan A. League Championship Algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl Soft Comput. 2014;16:171–200. doi:10.1016/j.asoc.2013.12.005
   Web of Science ®Google Scholar
• Maharana D., Maheshka S., Kotecha P. Multi-objective league championship algorithms and its applications to optimal control problems. 2019. Singapore: Springer Singapore. doi:10.1007/978-981-10-8968-8_4
   Google Scholar
• Alizadeh N, Husseinzadeh Kashan A. Enhanced grouping league championship and optics inspired optimization algorithms for scheduling a batch processing machine with job conflicts and non-identical job sizes. Appl Soft Comput. 2019;83:105657. doi:10.1016/j.asoc.2019.105657
   Web of Science ®Google Scholar
• Anup G, Bhat MN, Thakare N. Task pattern identification and scheduling using equal opportunity model for minimization of makespan and task diversity in cloud computing. Pattern Recogn Image Anal. 2022;32(1):67–77. doi:10.1134/S1054661821040088
   Web of Science ®Google Scholar
• Elfring J., Molengraft M.J.G.v.d., Janssen R.J.M., Steinbuch M. Two level world modeling for cooperating robots using a multiple hypotheses filter. In 2011 IEEE International Conference on Robotics and Automation. 2011. doi:10.1109/ICRA.2011.5980219
   Google Scholar
• Husseinzadeh Kashan A. An efficient algorithm for constrained global optimization and application to mechanical engineering design: League Championship Algorithm (LCA). Computer-Aided Des. 2011;43(12):1769–1792. doi:10.1016/j.cad.2011.07.003
   Web of Science ®Google Scholar
• Guchhait A, Maji B, Kandar D. A hybrid V2 V system for collision-free high-speed internet access in intelligent transportation system. Trans Emerg Telecommun Technol. 2018;29(3):e3282. doi:10.1002/ett.3282
   Web of Science ®Google Scholar
• Nourmohammadi A, Fathi M, Ng AHC. Balancing and scheduling assembly lines with human-robot collaboration tasks. Comput Oper Res. 2022;140:105674. doi:10.1016/j.cor.2021.105674
   Web of Science ®Google Scholar
• Fathi M, Nourmohammadi A, Ng HCA, et al. An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem. Eng Comput (Swansea). 2019;37(2):501–521. doi:10.1108/EC-02-2019-0053
   Web of Science ®Google Scholar
• Beheshtinia MA, Feizollahy P, Fathi M. Supply chain optimization considering sustainability aspects. Sustainability. 2021;13(21):11873. doi:10.3390/su132111873
   Web of Science ®Google Scholar
• Taguchi G, Chowdhury S, Wu Y. Taguchi’s quality engineering handbook. 1st ed. Wiley-Interscience; 2004. doi:10.1002/9780470258354
   Google Scholar