Skip to main content
SpringerLink
Log in

Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Resource sharing, as a coordination mechanism, can mitigate disruptions in supply and changes in demand. It is particularly crucial for platelets because they have a short lifespan and need to be transferred and allocated within a limited time to prevent waste or shortages. Thus, a coordinated model comprised of a mixed vertical-horizontal structure, for the logistics of platelets, is proposed for disaster relief operations in the response phase. The aim of this research is to reduce the wastage and shortage of platelets due to their critical role in wound healing. We present a bi-objective location-allocation robust possibilistic programming model for designing a two-layer coordinated organization strategy for multi-type blood-derived platelets under demand uncertainty. Computational results, derived using a heuristic ε-constraint algorithm, are reported and discussed to show the applicability of the proposed model. The experimental results indicate that surpluses and shortages in platelets remarkably declined following instigation of a coordinated disaster relief operation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abounacer, R., Rekik, M., & Renaud, J. (2014). An exact solution approach for multi-objective location-transportation problem for disaster response. Computers & Operation Research, 41, 83–93.

    Article  Google Scholar 

  • Afshar, A., & Haghani, A. (2012). Modeling integrated supply chain logistics in real-time large-scale disaster relief operations. Socio-Economic Planning Sciences, 46, 327–338.

    Article  Google Scholar 

  • Altay, N., & Green, W. (2006). OR/MS research in disaster operation management. European Journal of Operational Research, 175, 475–493.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Barbarosoglu, G., & Arda, Y. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. The Journal of the Operational Research Society, 55, 43–53.

    Article  Google Scholar 

  • Barbarosoglu, G., Ozdamar, L., & Cevik, A. (2002). An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. European Journal of Operational Research, 140, 118–133.

    Article  Google Scholar 

  • Ben-Tal, A., Chung, B. D., Mandala, S. R., & Yao, T. (2011). Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains. Transportation Research Part B, 45, 1177–1189.

    Article  Google Scholar 

  • Bozorgi-Amiri, A., Jabalameli, M. S., & Al-e-Hashem, S. M. J. M. (2013). A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty. OR Spectrum, 35(4), 905–933.

    Article  Google Scholar 

  • Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46, 4–13.

    Article  Google Scholar 

  • Chung, Y. T., Erhun, F., & Kraft, T. (2014). Improving Standford blood Center's platelets supply chain. New Jersey: Pearson Education, Inc..

    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, 561–573.

    Article  Google Scholar 

  • Edrissi, A., Poorzahedy, H., Nassiri, H., & Nourinejad, M. (2013). A multi-agent optimization formulation of earthquake disaster prevention and management. European Journal of Operational Research, 229, 261–275.

    Article  Google Scholar 

  • Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastage and shortage of bloods at hospitals. Computers & Operations Research, 54, 129–141.

    Article  Google Scholar 

  • Hess, J. R., & Thomas, M. J. G. (2003). Blood use in war and disaster: Lessons from the past century. Transfusion, 43, 1622–1633.

    Article  Google Scholar 

  • Huang, K., Jiang, Y., Yuan, Y., & Lindu, Z. (2015). Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E, 75, 1–17.

    Article  Google Scholar 

  • Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disaster: A robust model with real world application. Transportation Research Part E, 70, 225–244.

    Article  Google Scholar 

  • Janssen, M., Lee, J., Bharosa, N., & Cresswell, A. (2010). Advances in multi-agency disaster management: Key elements in disaster research. Information Systems Frontiers, 12(1), 1–7.

    Article  Google Scholar 

  • Li, N., Sun, M., Bi, Z., Su, Z., & Wang, C. (2014). A new methodology to support group decision-making for IoT-based emergency response systems. Information Systems Frontiers, 16(5), 953–977.

    Article  Google Scholar 

  • Liberatore, F., Ortuno, M., Tirado, G., Vitoriano, B., & Scaparra, M. (2014). A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in humanitarian logistics. Computers & Operations Research, 42, 3–13.

    Article  Google Scholar 

  • Moreno, A., Alem, D., & Ferreira, D. (2016). Heuristic approaches for the multiperiod location-transportation problem with reuse of vehicles in emergency logistics. Computers & Operations Research, 69, 79–96.

    Article  Google Scholar 

  • Nagurney, A., Masoumi, A. H., & Yu, M. (2012). Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science, 9(2), 205–231.

    Article  Google Scholar 

  • Najafi, M., Eshghi, K., & Leeuw, S. d. (2014). A dynamic dispatching and routing model to plan/re-plan logistics activities in response to an earthquake. OR Spectrum, 36, 323–356.

    Article  Google Scholar 

  • Norouzi, M., Ahmadi, A., Esmaeel Nezhad, A., & Ghaedi, A. (2014). Mixed integer programming of multi-objective security-constrained hydro/thermal unit commitment. Renewable and Sustainable Energy Reviews, 29, 911–923.

    Article  Google Scholar 

  • Onur Mete, H., & Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics, 126, 76–84.

    Article  Google Scholar 

  • Ozdamar, L., & Ertem, M. A. (2014). Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research, 244(1), 55–65.

    Article  Google Scholar 

  • Ozdamar, L., Ekinci, E., & Kucukyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of Operational Research, 129, 217–245.

    Article  Google Scholar 

  • Paul, J., & MacDonald, L. (2016). Optimal location, capacity and timing of stockpiles for improved hurricane preparedness. International Journal of Production Economics, 174, 1–50.

    Article  Google Scholar 

  • Pierskala, W. P. (2003). Supply chain management of blood banks. International Series in Operations Research & Management Science, 70, 103–145. https://doi.org/10.1007/1-4020-8066-2_5.

  • Pishvaee, M., Razmi, J., & Torabi, S. (2012). Robust Possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20.

    Article  Google Scholar 

  • Poblet, M., Garcia-Cuesta, E., & Casanovas, P. (2017). Crowdsourcing roles, methods and tools for data-intensive disaster management. Information Systems Frontiers, 1–17. https://doi.org/10.1007/s10796-017-9734-6.

  • Pradhananga, R., Mutlu, F., Pokharel, S., Holguín-Veras, J., & Seth, D. (2016). An integrated resource allocation and distribution model for pre-disaster planning. Computers & Industrial Engineering, 91, 229–238.

    Article  Google Scholar 

  • Raju, E., & Becker, P. (2013). Multi-organizational coordination for disaster recovery: The story of post-tsunami Tamil Nadu, India. International Journal of Disaster Risk Reduction, 4, 82–91.

    Article  Google Scholar 

  • Raju, E., & Van Niekerk, D. (2013). Intra-governmental coordination for sustainable disaster recovery: A case-study of the Eden District municipality, South Africa. International Journal of Disaster Risk Reduction, 4, 92–99.

    Article  Google Scholar 

  • Rossel, P. O., Herskovic, V., & Ormeno, E. (2016). Creating a family of collaborative applications for emergency management in the firefighting sub-domain. Information Systems Frontiers, 18(1), 69–84.

    Article  Google Scholar 

  • Salmeron, J., & Apte, A. (2010). Stochastic optimization for natural disaster asset prepositioning. Production and Operations Management, 19(5), 561–574.

    Article  Google Scholar 

  • Schulz, S. F., & Blecken, A. (2010). Horizontal cooperation in disaster relief logistics: Benefits and impediments. International Journal of Physical Distribution and Logistics Management, 40, 636–656.

    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, 43, 687–709.

    Article  Google Scholar 

  • Sheu, J. B., & Pan, C. (2014). Relief supply collaboration for emergency logistics responses to large-scale disasters. Transportmetrica A: Transport Science, 11, 210–242.

    Article  Google Scholar 

  • Shokar, I., Torabi, S.A., (2017). An enhanced reverse auction framework for relief procurement management. International Journal of Disaster Risk Reduction, 24, 66–80.

  • Talaei, M., Moghaddam, B. F., Pishvaee, M., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of Cleaner Production, 113, 662–673.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Toner, R. W., Pizzi, L., Leas, B., Ballas, S. K., Quigley, A., & Goldfarb, N. I. (2011). Cost to hospitals of acquiring and processing blood in the US: A survey of hospital-based blood banks and transfusion services. Applied Health Economics and Health Policy, 9(1), 29–37.

    Article  Google Scholar 

  • Wang, L., Song, J., & Shi, L. (2015). Dynamic emergency logistics planning: Models and heuristic algorithm. Optimization Letters, 9(8), 1533–1552.

    Article  Google Scholar 

  • Yi, W., & Ozdamar, L. (2007). A dynamic logistics coordination model for evacuation and support in disaster response activities. European Journal of Operational Research, 179, 1177–1193.

    Article  Google Scholar 

  • Zahiri, B., Tavakkoli-Moghaddam, R., & Pishvaee, M. (2014). A robust possibilistic programming approach to multi period location-allocation of organ transplant centers under uncertainty. Computers & Industrial Engineering, 74, 139–148.

    Article  Google Scholar 

Download references

Acknowledgements

We wish to express our sincere thanks to the anonymous referee and the Editorial office for their through and constructive review that have helped us improve both the presentation of the work and the methodologies developed here. We are also most grateful to Professor Hua Cai whose valuable support led to a significant improvement in the article.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran

    Afshin Kamyabniya & M. M. Lotfi

  2. School of Industrial Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Afshin Kamyabniya & Yuehwern Yih

  3. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), Sydney, Australia

    Mohsen Naderpour

Authors
  1. Afshin Kamyabniya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. M. Lotfi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohsen Naderpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuehwern Yih
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. M. Lotfi.

Appendices

Appendix 1. Case Data Studied

Table 8 Supply of vehicles (unit: helicopter or bus)
Full size table
Table 9 Daily supply (new donations) of platelets (unit: bags of platelets)
Full size table
Table 10 Platelet demand at TESs and hospitals (unit: bags of platelets)
Full size table
Table 11 Distance between BSUs and hospitals (unit: miles)
Full size table
Table 12 Distance between TESs and BSUs (in miles)
Full size table
Table 13 Initial inventory of platelets at hospitals (unit: bags of platelets)
Full size table

Appendix 2. Linearization Technique

In this technique, interactions between discrete and binary variables are changed by replacing the product of interacting variables to new discrete variables. The linearization variable is called y and reflects the products of x (discrete variable) and d (binary) in the process of linearization. The lower bound and upper bound of x are assumed to be known and take the values of L and U, respectively. The linear mixed-integer programming model after linearization process is:

$$ \mathrm{Ld}\le \mathrm{y}\le \mathrm{Ud} $$
(61)
$$ \mathrm{L}\left(1-\mathrm{d}\right)\le \left(\mathrm{x}-\mathrm{y}\right)\le \mathrm{U}\left(1-\mathrm{d}\right) $$
(62)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamyabniya, A., Lotfi, M.M., Naderpour, M. et al. Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach. Inf Syst Front 20, 759–782 (2018). https://doi.org/10.1007/s10796-017-9788-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-017-9788-5

Keywords

Search

Navigation