Robust resilient-responsive approach for multi-objective healthcare supply chain network design problem

i

This article has been updated. A Correction to this article was published on .

i

The Original Article was published on

i

The Original Article was published on

i

This article has been updated

i

This article was retracted on

i

This article has been updated

Abstract

Healthcare supply chains face significant challenges during epidemic outbreaks due to rapidly changing demand, supply disruptions, and limited healthcare resources. These challenges are particularly pronounced for vector-borne diseases, where healthcare resource allocation must be coordinated with vector control operations. This study develops a tri-objective mixed-integer linear programming (MILP) model for healthcare supply chain network design that jointly considers material distribution, patient transportation, resilience, and responsiveness. The proposed network consists of primary suppliers, backup suppliers, distribution centers, VCUs, communities, and hospitals. To address uncertainty in the demand, facility capacity, and transportation costs, the established robust possibilistic programming (RPP) framework with trapezoidal fuzzy numbers is incorporated into the proposed optimization model. The resulting formulation simultaneously minimizes total system cost, critical-node penalties, and response deviations while accounting for both optimality robustness and feasibility robustness through heterogeneous penalty coefficients. The augmented ε-constraint (AEC) method is used to generate the Pareto-optimal frontier and analyze the trade-offs among the robust total objective, resilience, and responsiveness. The proposed model is validated using a real-world case based on the 2025 Chikungunya fever outbreak in Shunde District, Foshan, Guangdong Province, China. The results show that the proposed framework effectively supports healthcare resource allocation under uncertainty, while the RPP model with confidence levels of α = β = 0.5 exhibits the lowest cross-scenario variability among the tested configurations. The Pareto analysis further provides quantitative guidance for balancing operational cost, resilience, and responsiveness under different epidemic response scenarios. The proposed framework offers a practical decision support tool for healthcare administrators and emergency planners facing uncertain public health emergencies.

Keywords

healthcare supply chain / pandemic / resilience / responsiveness / RPP

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org

References

[1] Ivanov D (2020) Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transport Res E-Log 136: 101922. http://dx.doi.org/10.1016/j.tre.2020.101922

[2] Singh G, Aggarwal N (2025) Integrating federated learning and deep Q-networks for resilient healthcare supply chain optimization amidst uncertain demand, In: 2025 2nd International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), 1-6. http://dx.doi.org/10.1109/ICAIIHI67124.2025.11403491

[3] Chen J, Shi R, Ye Q, et al. (2026) New progress in the global expansion and prevention of chikungunya fever. Acta Trop 276: 108017. http://dx.doi.org/10.1016/j.actatropica.2026.108017

[4] Thiboutot MM, Kannan S, Kawalekar OU, et al. (2010) Chikungunya: A potentially emerging epidemic? PLoS Neglect Trop D 4: e623. http://dx.doi.org/10.1371/journal.pntd.0000623

[5] Yu XL, Zhou RY, Xie XY, et al. (2026) Chikungunya fever-what we know and have done in the face of a new global public health threat. J Integr Med. http://dx.doi.org/10.1016/j.joim.2026.04.001

[6] Entezari S, Abdolazimi O, Fakhrzad MB, et al. (2024) A bi-objective stochastic blood type supply chain configuration and optimization considering time-dependent routing in post-disaster relief logistics. Comput Ind Eng 188: 109899. http://dx.doi.org/10.1016/j.cie.2024.109899

[7] Nagurney A (2021) Optimization of supply chain networks with inclusion of labor: Applications to COVID-19 pandemic disruptions. Int J Prod Econ 235: 108080. http://dx.doi.org/10.1016/j.ijpe.2021.108080

[8] Mamashli Z, Bozorgi-Amiri A, Dadashpour I, et al. (2021) A heuristic-based multi-choice goal programming for the stochastic sustainable-resilient routing-allocation problem in relief logistics. Neural Comput Appl 33: 14283-14309. http://dx.doi.org/10.1007/s00521-021-06074-8

[9] Fazel SJ, Rostamkhani M, Rashidnejad M (2023) Proposing a bi-objective model for the problem of designing a resilient supply chain network of pharmaceutical-health relief items under disruption conditions by considering lateral transshipment. EURO J Decis Process 11: 100037. http://dx.doi.org/10.1016/j.ejdp.2023.100037

[10] Shekarabi H, Mozdgir A (2026) Robust, resilient, and responsive food supply chain redesign considering uncertainty and pandemic disruptions. Ann Oper Res 361: 1225-1268. http://dx.doi.org/10.1007/s10479-025-06465-3

[11] Mondal A, Roy SK (2021) Multi-objective sustainable opened- and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation. Comput Ind Eng 159: 107453. http://dx.doi.org/10.1016/j.cie.2021.107453

[12] Pishvaee MS, Razmi J, Torabi SA (2012) Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Set Syst 206: 1-20. http://dx.doi.org/10.1016/j.fss.2012.04.010

[13] Balcik B, Beamon BM, Krejci CC, et al. (2010) Coordination in humanitarian relief chains: Practices, challenges and opportunities. Int J Prod Econ 126: 22-34. http://dx.doi.org/10.1016/j.ijpe.2009.09.008

[14] Nazari-Shirkouhi S, Samadi S (2025) Enhancing healthcare supply chains: A comprehensive evaluation of lean, agile, resilient and green paradigms. Eng Appl Artif Intel 145: 110204. http://dx.doi.org/10.1016/j.engappai.2025.110204

[15] Avinash B, Joseph G (2024) Reimagining healthcare supply chains: A systematic review on digital transformation with specific focus on efficiency, transparency and responsiveness. J Health Organ Manag 38: 1255-1279. http://dx.doi.org/10.1108/JHOM-03-2024-0076

[16] Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli-Moghaddam R, et al. (2022) Bi-level programming for home health care supply chain considering outsourcing. J Ind Inf Integr 25: 100246. http://dx.doi.org/10.1016/j.jii.2021.100246

[17] Fathollahi-Fard AM, Ahmadi A, Karimi B (2022) Sustainable and robust home healthcare logistics: A response to the COVID-19 pandemic. Symmetry 14: 193. http://dx.doi.org/10.3390/sym14020193

[18] Taghvaei D, Ghods T, Rabbani M (2025) A bi-objective two-stage stochastic optimization for designing a resilient humanitarian relief chain considering hybrid contracts, public donations, and item perishability. Comput Ind Eng 205: 111147. http://dx.doi.org/10.1016/j.cie.2025.111147

[19] Huang S, Lin Y, Zhang J, et al. (2023) Chance-constrained approach for decentralized supply chain network under uncertain cost. AIMS Math 8: 12217-12238. http://dx.doi.org/10.3934/math.2023616

[20] Chen J, Wu Y, Huang S, et al. (2023) Multi-objective optimization for AGV energy efficient scheduling problem with customer satisfaction. AIMS Math 8: 20097-20124. http://dx.doi.org/10.3934/math.20231024

[21] Peidro D, Mula J, Poler R, et al. (2009) Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Set Syst 160: 2640-2657. http://dx.doi.org/10.1016/j.fss.2009.02.021

[22] Pishvaee MS, Rabbani M, Torabi SA (2011) A robust optimization approach to closed- loop supply chain network design under uncertainty. Appl Math Model 35: 637-649. http://dx.doi.org/10.1016/j.apm.2010.07.013

[23] Azarkish M, Hajiali Z (2024) Presenting and solving a sustainable multi-objective model of the hospital waste management supply chain during pandemic under fuzzy condition. Asian J Basic Sci Res 6: 65-92. http://dx.doi.org/10.38177/AJBSR.2024.6109

[24] Zhong S, Cheng R, Jiang Y, et al. (2020) Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand. Transport Res E-Log 141: 102015. http://dx.doi.org/10.1016/j.tre.2020.102015

[25] Shahparvari S, Soleimani H, Govindan K, et al. (2021) Closing the loop: Redesigning sustainable reverse logistics network in uncertain supply chains. Comput Ind Eng 157: 107093. http://dx.doi.org/10.1016/j.cie.2020.107093

[26] Yılmaz OF, Guan Y, Gürsoy Yılmaz B (2025) Designing a resilient humanitarian supply chain by considering viability under uncertainty: A machine learning embedded approach. Transport Res E-Log 194: 103943. http://dx.doi.org/10.1016/j.tre.2024.103943

[27] Ala A, Simic V, Bacanin N, et al. (2024) Blood supply chain network design with lateral freight: A robust possibilistic optimization model. Eng Appl Artif Intel 133: 108053. http://dx.doi.org/10.1016/j.engappai.2024.108053

[28] Hosseini S, Ivanov D, Dolgui A (2019) Review of quantitative methods for supply chain resilience analysis. Transport Res E-Log 125: 285-307. http://dx.doi.org/10.1016/j.tre.2019.03.001

[29] Tukamuhabwa BR, Stevenson M, Busby J, et al. (2015) Supply chain resilience: Definition, review and theoretical foundations for further study. Int J Prod Res 53: 5592-5623. http://dx.doi.org/10.1080/00207543.2015.1037934

[30] Kaur H, Singh SP (2019) Sustainable procurement and logistics for disaster resilient supply chain. Ann Oper Res 283: 309-354. http://dx.doi.org/10.1007/s10479-016-2374-2

[31] Scholten K, Scott PS, Fynes B (2014) Mitigation processes-antecedents for building supply chain resilience. Supply Chain Manag 19: 211-228. http://dx.doi.org/10.1108/SCM-06-2013-0191

[32] Khalili-Fard A, Hashemi M, Bakhshi A, et al. (2024) Integrated relief pre-positioning and procurement planning considering non-governmental organizations support and perishable relief items in a humanitarian supply chain network. Omega 127: 103111. http://dx.doi.org/10.1016/j.omega.2024.103111

[33] Gunasekaran A, Lai KH, Cheng TCE (2008) Responsive supply chain: A competitive strategy in a networked economy. Omega 36: 549-564. http://dx.doi.org/10.1016/j.omega.2006.12.002

[34] Ganji VY, Hozan E, Babolhavaeji P, et al. (2025) A robust design of a circular supply chain network based on the resilience and responsiveness dimensions: A data-driven model. Socio-Econ Plan Sci 101: 102294. http://dx.doi.org/10.1016/j.seps.2025.102294

[35] Cheraghi S, Hosseini-Motlagh SM (2020) Responsive and reliable injured-oriented blood supply chain for disaster relief: A real case study. Ann Oper Res 291: 129-167. http://dx.doi.org/10.1007/s10479-018-3050-5

[36] Yılmaz OF, Yeni FB, Yılmaz BG, et al. (2023) An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey. Transport Res E-Log 173: 103089. http://dx.doi.org/10.1016/j.tre.2023.103089

[37] Lotfi R, Kargar B, Gharehbaghi A, et al. (2022) Viable medical waste chain network design by considering risk and robustness. Environ Sci Pollut R 29: 79702-79717. http://dx.doi.org/10.1007/s11356-021-16727-9

[38] Yazdani M, Haghani M (2023) Logistics of patient evacuation in response to disease outbreaks: Critical considerations for transportation planning. Transp Res Interdisc 22: 100975. http://dx.doi.org/10.1016/j.trip.2023.100975

[39] Chowdhury MMH, Khan E, Mahmud AKMS, et al. (2024) Health service operational challenges and resilience strategies in “extreme” disruptive events. Asia Pac J Mark Lo 36: 2953-2977. http://dx.doi.org/10.1108/APJML-09-2023-0843

[40] Lotfi R, Hazrati R, Aghakhani S, et al. (2024) A data-driven robust optimization in viable supply chain network design by considering open innovation and blockchain technology. J Clean Prod 436: 140369. http://dx.doi.org/10.1016/j.jclepro.2023.140369

[41] DiStefano L, Than KK, Traill T, et al. (2026) A liminal health system: Exploring the resilience strategies of healthcare providers and communities along the Thailand-Myanmar border. SSM Health Syst 6: 100209. http://dx.doi.org/10.1016/j.ssmhs.2026.100209

[42] Chai H, Sun T (2026) Measuring the resilience of urban healthcare service availability using metro-bus double-layer network against extreme disturbances. Reliab Eng Syst Safe 270: 112167. http://dx.doi.org/10.1016/j.ress.2025.112167

[43] Tang CS (2006) Perspectives in supply chain risk management. Int J Prod Econ 103: 451-488. http://dx.doi.org/10.1016/j.ijpe.2005.12.006

[44] Shen L, Yang Q, Xu X, et al. (2026) A generalized three-stage optimization model for emergency medical services under uncertainties: Integrating rescue station locations, ambulance deployment, and vehicle dispatch. Transport Res E-Log 205: 104499. http://dx.doi.org/10.1016/j.tre.2025.104499

[45] Sun L, DePuy GW, Evans GW (2014) Multi-objective optimization models for patient allocation during a pandemic influenza outbreak. Comput Oper Res 51: 350-359. http://dx.doi.org/10.1016/j.cor.2013.12.001

[46] Fathollahi-Fard AM, Li Z (2026) Adaptive large neighborhood search algorithm for redesigning a closed-loop supply chain network considering capacity configurations. Comput Oper Res 190: 107415. http://dx.doi.org/10.1016/j.cor.2026.107415

[47] Ruszczyński A, Shapiro A (2003) Stochastic programming models, In: Stochastic Programming, Handbooks in Operations Research and Management Science, Elsevier, 10: 1-64. http://dx.doi.org/10.1016/S0927-0507(03)10001-1

[48] Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev 53: 464-501. http://dx.doi.org/10.1137/080734510

[49] Heilpern S (1992) The expected value of a fuzzy number. Fuzzy Set Syst 47: 81-86. http://dx.doi.org/10.1016/0165-0114(92)90062-9

[50] Mavrotas G (2009) Effective implementation of the ε-constraint method in multi- objective mathematical programming problems. Appl Math Comput 213: 455-465. http://dx.doi.org/10.1016/j.amc.2009.03.037

[51] Aghaei J, Amjady N, Shayanfar HA (2011) Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Appl Soft Comput 11: 3846-3858. http://dx.doi.org/10.1016/j.asoc.2011.02.022

[52] Wang Z, Rangaiah GP (2017) Application and analysis of methods for selecting an optimal solution from the Pareto-optimal front obtained by multiobjective optimization. Ind Eng Chem Res 56: 560-574. http://dx.doi.org/10.1021/acs.iecr.6b03453