Document Type : Research Paper

Authors

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

2 Department of Industrial Engineering, Bu-Ali Sina University, Hamadan, Iran.

Abstract

Nowadays, designing a reliable network for blood supply chains by which most blood demands can be supplied is an important problem in the health care systems. In this paper, a multi-objective model is provided to create a sustainable blood supply chain, which contains multiple donors, collection centers, distribution centers, and hospitals at different echelons. Regarding the potential of a blood shortage occurring, the suggested model considers the supply chain's capacity to meet hospitals' blood demands as dependable and a means of achieving the societal purpose. In addition, limiting the overall cost and environmental effect of designing a supply network and blood transportation are considered economical and environmental objectives. To solve the proposed multi-objective model, an improved ε-constraint approach is first employed to construct a single-objective model. Additionally, an imperialist competitive algorithm is developed to solve the single-objective model. Several test cases are analysed to determine the technique's effectiveness. CPLEX is then used to compare the results.

Keywords

Main Subjects

  • Amin, S. H., Zhang, G., & Akhtar, P. (2017). Effects of uncertainty on a tire closed-loop supply chain network. Expert systems with applications, 73, 82-91.
  • Fu, J., & Fu, Y. (2015). An adaptive multi-agent system for cost collaborative management in supply chains. Engineering applications of artificial intelligence, 44, 91-100.
  • Devika, K., Jafarian, A., & Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques. European journal of operational research, 235, 594-615.
  • Sepehriar, A., Eslamipoor, R. & Nobari, A. (2013). A new mixed fuzzy-LP method for selecting the best supplier using fuzzy group decision making. Neural computing and applications23(1), 345-352.
  • Govindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International journal of production economics, 152, 9-28.
  • Eslamipoor, R. & Sepehriar, A. (2014). Firm relocation as a potential solution for environment improvement using a SWOT-AHP hybrid method. Process safety and environmental protection92(3), 269-276.
  • Diaz-Diestra, D., Gholipour, H. M., Bazian, M., Thapa, B., & Beltran-Huarac, J. (2022). Photodynamic therapeutic effect of nanostructured metal sulfide photosensitizers on cancer treatment. Nanoscale research letters, 17(1), 1-24.
  • Abdulwahab, U., & Wahab, M. (2014). Approximate dynamic programming modeling for a typical blood platelet bank. Computers and industrial engineering, 78, 259-270.
  • Bashiri, M., Badri, H., & Talebi, J. (2012). A new approach to tactical and strategic planning in production–distribution networks. Applied mathematical modelling, 36(4), 1703-1717.
  • Natarajarathinam, M., Capar, I., & Narayanan, A. (2009). Managing supply chains in times of crisis: a review of literature and insights. International journal of physical distribution & logistics management, 39(7), 535-573. https://doi.org/10.1108/09600030910996251
  • Haijema, R., van Dijk, N., Van der Wal, J., & Sibinga, C. S. (2009). Blood platelet production with breaks: optimization by SDP and simulation. International journal of production economics121(2), 464-473.
  • 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.
  • Duan, Q., & Liao, T. W. (2014). Optimization of blood supply chain with shortened shelf lives and ABO compatibility. International journal of production economics, 153, 113-129.
  • Jokar, A., & Hosseini-Motlagh, S. M. (2015). Impact of capacity of mobile units on blood supply chain performance: results from a robust analysis. International journal of hospital research, 4(3), 101-105.
  • Mobasher, A., Ekici, A., & Özener, O. Ö. (2015). Coordinating collection and appointment scheduling operations at the blood donation sites. Computers and industrial engineering,87, 260-266.
  • Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers and operations research54, 129-141.
  • Yousefi Nejad Attari, M., Pasandide, S. H. R., Agaie, A., & Akhavan Niaki, S. T. (2017). Presenting a stochastic multi choice goal programming model for reducing wastages and shortages of blood products at hospitals. Journal of industrial and systems engineering10, 81-96.
  • Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2017). Supply chain design for efficient and effective blood supply in disasters. International journal of production economics183, 700-709.
  • Puranam, K., Novak, D. C., Lucas, M. T., & Fung, M. (2017). Managing blood inventory with multiple independent sources of supply. European journal of operational research259(2), 500-511.
  • Rajendran, S., & Ravindran, A. R. (2017). Platelet ordering policies at hospitals using stochastic integer programming model and heuristic approaches to reduce wastage. Computers and industrial engineering110, 151-164.
  • Hosseinifard, Z., & Abbasi, B. (2018). The inventory centralization impacts on sustainability of the blood supply chain. Computers and operations research89, 206-212.
  • Lowalekar, H., & Ravichandran, N. (2017). A combined age‐and‐stock‐based policy for ordering blood units in hospital blood banks.International transactions in operational research24(6), 1561-1586.
  • Baş, S., Carello, G., Lanzarone, E., & Yalçındağ, S. (2018). An appointment scheduling framework to balance the production of blood units from donation. European journal of operational research265(3), 1124-1143.
  • Bashiri, M., & Ghasemi, E. (2018). A selective covering-inventory-routing problem to the location of bloodmobile to supply stochastic demand of blood. International journal of industrial engineering & production research29 (2), 147-158.
  • Özener, O. Ö., & Ekici, A. (2018). Managing platelet supply through improved routing of blood collection vehicles. Computers and operations research98, 113-126.
  • Jafarkhan, F., & Yaghoubi, S. (2018). An efficient solution method for the flexible and robust inventory-routing of red blood cells. Computers and industrial engineering117, 191-206.
  • Hamdan, B., & Diabat, A. (2019). A two-stage multi-echelon stochastic blood supply chain problem. Computers and operations research101, 130-143.
  • Samani, M. R. G., Hosseini-Motlagh, S. M., Sheshkol, M. I., & Shetab-Boushehri, S. N. (2019). A bi-objective integrated model for the uncertain blood network design with raising products quality. European journal of industrial engineering13(5), 553-588.
  • Larimi, N. G., Yaghoubi, S., & Hosseini-Motlagh, S. M. (2019). Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories. Socio-economic planning sciences68, 100697. https://doi.org/10.1016/j.seps.2019.03.003
  • Rajendran, S., & Ravindran, A. R. (2019). Inventory management of platelets along blood supply chain to minimize wastage and shortage. Computers and industrial engineering130, 714-730.
  • Hosseini-Motlagh, S. M., Samani, M. R. G., & Cheraghi, S. (2020). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-economic planning sciences70, 100725. https://doi.org/10.1016/j.seps.2019.07.001
  • Khalilpourazari, S., Soltanzadeh, S., Weber, G. W., & Roy, S. K. (2020). Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study. Annals of operations research289(1), 123-152.
  • Haeri, A., Hosseini‐Motlagh, S. M., Ghatreh Samani, M. R., & Rezaei, M. (2020). A mixed resilient‐efficient approach toward blood supply chain network design.International transactions in operational research27(4), 1962-2001.
  • Hosseini-Motlagh, S. M., Gilani Larimi, N., & Oveysi Nejad, M. (2020). A qualitative, patient-centered perspective toward plasma products supply chain network design with risk controlling. Operational research, 22, 779-824. https://link.springer.com/article/10.1007/s12351-020-00568-4
  • Haghjoo, N., Tavakkoli-Moghaddam, R., Shahmoradi-Moghadam, H., & Rahimi, Y. (2020). Reliable blood supply chain network design with facility disruption: a real-world application. Engineering applications of artificial intelligence, 90, 103493. https://doi.org/10.1016/j.engappai.2020.103493
  • Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation research part E: logistics and transportation review, 134, 101764. https://doi.org/10.1016/j.tre.2019.08.005
  • Moslemi, S., & Pasandideh, S. H. R. (2021). A location-allocation model for quality-based blood supply chain under IER uncertainty. RAIRO-operations research, 55, S967-S998.
  • Asadpour, M., Boyer, O., & Tavakkoli-Moghaddam, R. (2021). A blood supply chain network with backup facilities considering blood groups and expiration date: a real-world application. International journal of engineering34(2), 470-479.
  • Arani, M., Chan, Y., Liu, X., & Momenitabar, M. (2021). A lateral resupply blood supply chain network design under uncertainties. Applied mathematical modelling93, 165-187.
  • Soltani, M., Mohammadi, R. A., Hosseini, S. M. H., & Zanjani, M. M. (2021). A new model for blood supply chain network design in disasters based on hub location approach considering intercity transportation. International journal of industrial engineering, 32(2), 1-28.
  • Dehghani, M., Abbasi, B., & Oliveira, F. (2021). Proactive transshipment in the blood supply chain: a stochastic programming approach. Omega, 98, 102112. https://doi.org/10.1016/j.omega.2019.102112
  • Pishvaee, M., Razmi, J., & Torabi, S. (2014). An accelerated benders decomposition algorithm for sustainable supply chain network design under uncertainty: a case study of medical needle and syringe supply chain. Transportation reswarch part E: logistics and transportation review, 67, 14–38.
  • Mavrotas G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
  • Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialist competition. IEEE congress on evolutionary computation (pp. 4661-4667). IEEE. https://doi.org/10.1109/CEC.2007.4425083
  • Nobari, A., Kheirkhah, S., & Esmaeili, M. (2016). Considering pricing problem in a dynamic and integrated design of sustainable closed-loop supply chain network. International journal of industrial engineering and production research, 27(4), 353-371.
  • Mousavi, M., Hajipour, V., Niaki, S. T. A., & Aalikar, N. (2013). Optimizing multi-item multi-period inventory control system with discounted cash ow and inflation approaches: two calibrated meta-heuristic algorithms. Applied mathematical modelling, 37, 2241-2256.