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An integrative location-allocation model for humanitarian logistics with distributive injustice and dissatisfaction under uncertainty

  • S.I. : Design and Management of Humanitarian Supply Chains
  • Published:
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Abstract

Humanitarian logistics is an integral part of disaster relief operations, which involves the phases of preparedness, disaster operations, and post-disaster operations. Integrating the planning and execution between phases minimizes the gaps in providing relief to the affected population. This paper presents a two-stage multi-objective mathematical model for integrated decision-making during the preparation and response phases. The proposed model is developed to jointly optimize the location of emergency shelters (and/or depots) and coordinate the movement of relief vehicles between the disaster site and emergency shelters. Focusing on the optimal distribution of relief supplies to the emergency shelters, the proposed model aims to minimize the operational, distributive injustice, and dissatisfaction costs. To address the computational complexity of the introduced model, two multi-objective meta-heuristics, namely multi-objective vibration damping optimization and non-dominated sorting genetic algorithm (NSGA-II), are used. A comprehensive sensitivity analysis is conducted to study the impacts of variations in key parameters on model output under different scenarios. Our results suggests that the employed solution algorithms outperform the traditional optimization methods in achieving the Pareto-Front solutions.

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References

  • Akbari, M. E., Farshad, A. A., & Asadi-Lari, M. (2004). The devastation of Bam: An overview of health issues 1 month after the earthquake. Public Health, 118(6), 403–408.

    Article  Google Scholar 

  • Akgün, İ, Gümüşbuğa, F., & Tansel, B. (2015). Risk based facility location by using fault tree analysis in disaster management. Omega, 52, 168–179.

    Article  Google Scholar 

  • Alem, D., Clark, A., & Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research, 255(1), 187–206.

    Article  Google Scholar 

  • Asian, S., & Nie, X. (2014). Coordination in supply chains with uncertain demand and disruption risks: Existence, analysis, and insights. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(9), 1139–1154.

    Article  Google Scholar 

  • Balcik, B., Beamon, B. M., Krejci, C. C., Muramatsu, K. M., & Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126(1), 22–34.

    Article  Google Scholar 

  • Bayram, V., Tansel, B. Ç., & Yaman, H. (2015). Compromising system and user interests in shelter location and evacuation planning. Transportation Research Part B: Methodological, 72, 146–163.

    Article  Google Scholar 

  • Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.

    Article  Google Scholar 

  • Bish, D. R., Agca, E., & Glick, R. (2014). Decision support for hospital evacuation and emergency response. Annals of Operations Research, 221(1), 89–106.

    Article  Google Scholar 

  • Bozorgi-Amiri, A., Jabalameli, M. S., Alinaghian, M., & Heydari, M. (2012). A modified particle swarm optimization for disaster relief logistics under uncertain environment. The International Journal of Advanced Manufacturing Technology, 60(1–4), 357–371.

    Article  Google Scholar 

  • Campbell, A. M., & Jones, P. C. (2011). Prepositioning supplies in preparation for disasters. European Journal of Operational Research, 209(2), 156–165.

    Article  Google Scholar 

  • Campbell, A. M., Vandenbussche, D., & Hermann, W. (2008). Routing for relief efforts. Transportation Science, 42(2), 127–145.

    Article  Google Scholar 

  • Cavdur, F., Kose-Kucuk, M., & Sebatli, A. (2016). Allocation of temporary disaster response facilities under demand uncertainty: An earthquake case study. International Journal of Disaster Risk Reduction, 19, 159–166.

    Article  Google Scholar 

  • Davis, L. B., Samanlioglu, F., Qu, X., & Root, S. (2013). Inventory planning and coordination in disaster relief efforts. International Journal of Production Economics, 141(2), 561–573.

    Article  Google Scholar 

  • De Vries, H., & Van Wassenhove, L. N. (2020). Do optimization models for humanitarian operations need a paradigm shift? Production and Operations Management, 29(1), 55–61.

    Article  Google Scholar 

  • Dubey, R., Altay, N., & Blome, C. (2019a). Swift trust and commitment: The missing links for humanitarian supply chain coordination? Annals of Operations Research, 283(1), 159–177.

    Article  Google Scholar 

  • Dubey, R., Bryde, D. J., Foropon, C., Graham, G., Giannakis, M., & Mishra, D. B. (2020a). Agility in humanitarian supply chain: an organizational information processing perspective and relational view. Annals of Operations Research, Article in Press, 1–21.

  • Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020b). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Wamba, S. F., Giannakis, M., & Foropon, C. (2019b). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120–136.

    Article  Google Scholar 

  • Duque, P. A. M., Dolinskaya, I. S., & Sörensen, K. (2016). Network repair crew scheduling and routing for emergency relief distribution problem. European Journal of Operational Research, 248(1), 272–285.

    Article  Google Scholar 

  • Erbeyoğlu, G., & Bilge, Ü. (2020). A robust disaster preparedness model for effective and fair disaster response. European Journal of Operational Research, 280(2), 479–494.

    Article  Google Scholar 

  • Faghih-Roohi, S., Ong, Y. S., Asian, S., & Zhang, A. N. (2016). Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks. Annals of Operations Research, 247(2), 715–734.

    Article  Google Scholar 

  • Falasca, M., & Zobel, C. W. (2011). A two-stage procurement model for humanitarian relief supply chains. Journal of Humanitarian Logistics and Supply Chain Management, 1(2), 151–169.

    Article  Google Scholar 

  • Fikar, C., Gronalt, M., & Hirsch, P. (2016). A decision support system for coordinated disaster relief distribution. Expert Systems with Applications, 57, 104–116.

    Article  Google Scholar 

  • Ghafory-Ashtiany, M., & Hosseini, M. (2008). Post-Bam earthquake: recovery and reconstruction. Natural Hazards, 44(2), 229–241.

    Article  Google Scholar 

  • Goldschmidt, K. H., & Kumar, S. (2019). Reducing the cost of humanitarian operations through disaster preparation and preparedness. Annals of Operations Research, 1–14.

  • Gunasekaran, A., Dubey, R., Fosso Wamba, S., Papadopoulos, T., Hazen, B. T., & Ngai, E. W. (2018). Bridging humanitarian operations management and organisational theory. International Journal of Production Research, 56(21), 6735–6740.

    Article  Google Scholar 

  • Hajipour, V., Mehdizadeh, E., & Tavakkoli-Moghaddam, R. (2014). A novel Pareto-based multi-objective vibration damping optimization algorithm to solve multi-objective optimization problems. Scientia Iranica–Transaction E, 21(6), 2368–2378.

    Google Scholar 

  • He, F., & Zhuang, J. (2016). Balancing pre-disaster preparedness and post-disaster relief. European Journal of Operational Research, 252(1), 246–256.

    Article  Google Scholar 

  • Holguín-Veras, J., Jaller, M., Van Wassenhove, L. N., Pérez, N., & Wachtendorf, T. (2012). On the unique features of post-disaster humanitarian logistics. Journal of Operations Management, 30(7–8), 494–506.

    Article  Google Scholar 

  • Jin, S., Jeong, S., Kim, J., & Kim, K. (2015). A logistics model for the transport of disaster victims with various injuries and survival probabilities. Annals of Operations Research, 230(1), 17–33.

    Article  Google Scholar 

  • Kelle, P., Schneider, H., & Yi, H. (2014). Decision alternatives between expected cost minimization and worst case scenario in emergency supply—Second revision. International Journal of Production Economics, 157, 250–260.

    Article  Google Scholar 

  • Khayal, D., Pradhananga, R., Pokharel, S., & Mutlu, F. (2015). A model for planning locations of temporary distribution facilities for emergency response. Socio-Economic Planning Sciences, 52, 22–30.

    Article  Google Scholar 

  • Kilci, F., Kara, B. Y., & Bozkaya, B. (2015). Locating temporary shelter areas after an earthquake: A case for Turkey. European Journal of Operational Research, 243(1), 323–332.

    Article  Google Scholar 

  • Kovács, G., & Spens, K. (2009). Identifying challenges in humanitarian logistics. International Journal of Physical Distribution & Logistics Management, 39(6), 506–528.

    Article  Google Scholar 

  • Li, A. C., Nozick, L., Xu, N., & Davidson, R. (2012). Shelter location and transportation planning under hurricane conditions. Transportation Research Part E: Logistics and Transportation Review, 48(4), 715–729.

    Article  Google Scholar 

  • Lin, Y. H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2012). Location of temporary depots to facilitate relief operations after an earthquake. Socio-Economic Planning Sciences, 46(2), 112–123.

    Article  Google Scholar 

  • Liu, Y., Lei, H., Wu, Z., & Zhang, D. (2019). A robust model predictive control approach for post-disaster relief distribution. Computers & Industrial Engineering, 135, 1253–1270.

    Article  Google Scholar 

  • Mehdizadeh, E., & Tavakkoli-Moghaddam, R. (2008). Vibration damping optimization. In Proc. of the Int. Conf. Operations Research 2008 – OR and Global Business, Germany, 3–5 September 2008.

  • Mehdizadeh, E., Tavakkoli-Moghaddam, R., & Yazdani, M. (2015). A vibration damping optimization algorithm for a parallel machines scheduling problem with sequence-independent family setup times. Applied Mathematical Modelling, 39, 6845–6859.

    Article  Google Scholar 

  • Memari, P., Tavakkoli-Moghaddam, R., Partovi, M., & Zabihian, A. (2018). Fuzzy dynamic location-allocation problem with temporary multi-medical centers in disaster management. IFAC-Papers OnLine., 51(11), 1554–1560.

    Article  Google Scholar 

  • Movahedi, H. (2005). Search, rescue, and care of the injured following the 2003 Bam, Iran, earthquake. Earthquake Spectra, 21(1_suppl), 475–485.

    Article  Google Scholar 

  • Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E: Logistics and Transportation Review, 49(1), 217–249.

    Article  Google Scholar 

  • Ni, W., Shu, J., & Song, M. (2018). Location and emergency inventory pre-positioning for disaster response operations: Min–max robust model and a case study of Yushu earthquake. Production and Operations Management, 27(1), 160–183.

    Article  Google Scholar 

  • Noyan, N. (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Computers & Operations Research, 39(3), 541–559.

    Article  Google Scholar 

  • Onan, K., Ülengin, F., & Sennaroğlu, B. (2015). An evolutionary multi-objective optimization approach to disaster waste management: A case study of Istanbul Turkey. Expert Systems with Applications, 42(22), 8850–8857.

    Article  Google Scholar 

  • Özdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of Operations Research, 129(1), 217–245.

    Article  Google Scholar 

  • Paul, D., Zhang, A. N., & Asian, S. (2018). On the value of demand management for mitigating risk: peak-order reduction through trend filtering. In Proceedings of the 2018 IEEE international conference on systems, man, and cybernetics (SMC), Miyazaki, Japan, 7–10 October 2018, (pp. 2260–2265). IEEE.

  • Paul, J. A., & MacDonald, L. (2016). Location and capacity allocations decisions to mitigate the impacts of unexpected disasters. European Journal of Operational Research, 251(1), 252–263.

    Article  Google Scholar 

  • Paul, S. K., Asian, S., Goh, M., & Torabi, S. A. (2019). Managing sudden transportation disruptions in supply chains under delivery delay and quantity loss. Annals of Operations Research, 273(1–2), 783–814.

    Article  Google Scholar 

  • Pérez-Rodríguez, N., & Holguín-Veras, J. (2016). Inventory-allocation distribution models for postdisaster humanitarian logistics with explicit consideration of deprivation costs. Transportation Science, 50(4), 1261–1285.

    Article  Google Scholar 

  • Pyakurel, U., Nath, H. N., & Dhamala, T. N. (2019). Partial contraflow with path reversals for evacuation planning. Annals of Operations Research, 283(1–2), 591–612.

    Article  Google Scholar 

  • Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research, 42, 25–39.

    Article  Google Scholar 

  • Raziei, Z., Tavakkoli-Moghaddam, R., Rezaei-Malek, M., Bozorgi-Amiri, A., & Jolai, F. (2018). Postdisaster relief distribution network design under disruption risk: A tour covering location-routing approach. In P. Samui, D. Kim, C. Ghosh (Eds.) Global case studies in mitigation and recovery, Chapter 23, (pp. 393–406). Elsevier.

  • Rezaei Somarin, A., Asian, S., Jolai, F., & Chen, S. (2018). Flexibility in service parts supply chain: A study on emergency resupply in aviation MRO. International Journal of Production Research, 56(10), 3547–3562.

    Article  Google Scholar 

  • Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Cheikhrouhou, N., & Taheri-Moghaddam, A. (2016). An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management. Transportation Research Part E: Logistics and Transportation Review, 93, 485–509.

    Article  Google Scholar 

  • Rodríguez-Espíndola, O., Albores, P., & Brewster, C. (2018). Disaster preparedness in humanitarian logistics: A collaborative approach for resource management in floods. European Journal of Operational Research, 264(3), 978–993.

    Article  Google Scholar 

  • Sabouhi, F., Bozorgi-Amiri, A., Moshref-Javadi, M., & Heydari, M. (2019). An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study. Annals of Operations Research, 283(1–2), 643–677.

    Article  Google Scholar 

  • Saeidian, B., Mesgari, M. S., & Ghodousi, M. (2016). Evaluation and comparison of genetic algorithm and bees algorithm for location–allocation of earthquake relief centers. International Journal of Disaster Risk Reduction, 15, 94–107.

    Article  Google Scholar 

  • Sazvar, Z., Zokaee, M., Tavakkoli-Moghaddam, R., Salari, S. A., & Nayeri, S. (2021). Designing a sustainable closed-loop pharmaceutical supply chain in a competitive market considering demand uncertainty, manufacturer’s brand and waste management. Annals of Operations Research, Article in Press.

  • Sharma, B., Ramkumar, M., Subramanian, N., & Malhotra, B. (2019). Dynamic temporary blood facility location-allocation during and post-disaster periods. Annals of Operations Research, 283(1), 705–736.

    Article  Google Scholar 

  • Sheu, J. B. (2007). An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transportation Research Part E: Logistics and Transportation Review, 43(6), 687–709.

    Article  Google Scholar 

  • Somarin, A. R., Asian, S., & Chen, S. (2016). Dynamic priority repair policy for service parts supply chain. In Proceedings of the 2016 IEEE international conference on industrial engineering and engineering management (IEEM), Bali, Indonesia, 4–7 December 2016 (pp. 798–802). IEEE.

  • Somarin, A. R., Chen, S., Asian, S., & Wang, D. Z. (2017). A heuristic stock allocation rule for repairable service parts. International Journal of Production Economics, 184, 131–140.

    Article  Google Scholar 

  • Tofighi, S., Torabi, S. A., & Mansouri, S. A. (2016). Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, 250(1), 239–250.

    Article  Google Scholar 

  • Toyasaki, F., Arikan, E., Silbermayr, L., & Falagara Sigala, I. (2017). Disaster relief inventory management: Horizontal cooperation between humanitarian organizations. Production and Operations Management, 26(6), 1221–1237.

    Article  Google Scholar 

  • Tricoire, F., Graf, A., & Gutjahr, W. J. (2012). The bi-objective stochastic covering tour problem. Computers & Operations Research, 39(7), 1582–1592.

    Article  Google Scholar 

  • Van Wassenhove, L. N. (2006). Humanitarian aid logistics: Supply chain management in high gear. Journal of the Operational Research Society, 57(5), 475–489.

    Article  Google Scholar 

  • Whybark, D. C. (2007). Issues in managing disaster relief inventories. International Journal of Production Economics, 108(1–2), 228–235.

    Article  Google Scholar 

  • Yang, F., Yuan, Q., Du, S., & Liang, L. (2016). Reserving relief supplies for earthquake: A multi-attribute decision making of China Red Cross. Annals of Operations Research, 247(2), 759–785.

    Article  Google Scholar 

  • Zahiri, B., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2017). A novel multi-stage possibilistic stochastic programming approach (with an application in relief distribution planning). Information Sciences, 385, 225–249.

    Article  Google Scholar 

  • Zhu, L., Gong, Y., Xu, Y., & Gu, J. (2019). Emergency relief routing models for injured victims considering equity and priority. Annals of Operations Research, 283(1), 1573–1606.

    Article  Google Scholar 

  • Zokaee, S., Bozorgi-Amiri, A., & Sadjadi, S. J. (2016). A robust optimization model for humanitarian relief chain design under uncertainty. Applied Mathematical Modelling, 40(17–18), 7996–8016.

    Article  Google Scholar 

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Seraji, H., Tavakkoli-Moghaddam, R., Asian, S. et al. An integrative location-allocation model for humanitarian logistics with distributive injustice and dissatisfaction under uncertainty. Ann Oper Res 319, 211–257 (2022). https://doi.org/10.1007/s10479-021-04003-5

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