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A Bi-objective stochastic blood type supply chain configuration and optimization considering time-dependent routing in post-disaster relief logistics

Published: 01 February 2024 Publication History

Highlights

This study incorporates time-dependent transportation routing decisions in the blood disaster relief logistics.
A scenario based two-stage stochastic programming approach is adopted to deal with uncertainties.
The improved version of Augmented Ɛ-constraint 2 (AUGMECON2) is applied to solve the proposed model.

Abstract

Blood plays a critical role in humanitarian logistics, and it is essential to create an appropriate blood type supply chain (BTSC) network to manage the supply and demand of blood components efficiently, especially during crises. This paper formulates a bi-objective model to minimize cost and blood shortage simultaneously. This study considers all essential aspects of the BTSC network, entailing the position of blood amenities, allocation of donors, as well as different facilities and blood centers, collection, production, and testing of perishable blood components of multiple blood types, uncertain demand, and more importantly, time-dependent routing decisions. Supply and demand uncertainties are considered in this study; a scenario-based two-stage stochastic programming is adopted to cope with the scenario-dependent variables, like blood allocation and routing selection after a disaster. The modified version of augmented ε-constraint 2 (AUGMECON2) is applied to solve the proposed model. Several numerical instances are applied to check the model and the suggested solution method's accuracy, and a real case study is utilized to reveal the model's applicability. Through solving the problem, the importance of several new ideas, such as blood types and novel type I regional blood centers (RBC) as well as time-dependent routing problem, has been highlighted. Finally, sensitivity analysis has been conducted to analyze the impacts of the significant parameters on the target functions; consequently, pertinent managerial insights are derived from sensitivity analysis.

References

[1]
O. Abdolazimi, J. Ma, D. Shishebori, M.A. Ardakani, S.E. Masaeli, A Multi-Layer blood supply chain configuration and optimization under uncertainty in COVID-19 pandemic, Computers & Industrial Engineering 182 (2023).
[2]
O. Abdolazimi, M.S. Pishvaee, M. Shafiee, D. Shishebori, J. Ma, S. Entezari, Blood supply chain configuration and optimization under the COVID-19 using benders decomposition based heuristic algorithm, International Journal of Production Research (2023),.
[3]
H. Abolghasemi, M.H. Radfar, M. Tabatabaee, N.S. Hosseini-Divkolayee, F.M. Burkle, Revisiting blood transfusion preparedness: Experience from the Bam earthquake response, Prehospital and disaster medicine 23 (5) (2008) 391–394.
[4]
H. Afshari, Q. Peng, Challenges and solutions for location of healthcare facilities, Industrial Engineering and Management 3 (2) (2014) 1–12.
[5]
M. Ahmadi, A. Seifi, B. Tootooni, A Humanitarian Logistics Model for Disaster Relief Operation Considering Network Failure and Standard Relief Time: A Case Study on San Francisco District, Transportation Research Part E: Logistics and Transportation Review 75 (1) (2015) 145–163.
[6]
Alderson D.L., G.G. Brown, and W.M. Carlyle. (2014). Assessing and Improving Operational Resilience of Critical Infrastructures and Other Systems, Tutorials in Operations Research. Institute for annual operations research and the management sciences (INFORMS) conference, November 8–12, San Francisco, California, USA.
[7]
Ali M., Ng M., Dias R., Muda I., Al-Obaidi R., Abdullaeva B., ... & Hammid A.T. (2022). Providing a Mathematical Routing-Inventory Model for the Drug Supply Chain Considering the Travel Time Dependence and Perishability on Multiple Graphs. Discrete Dynamics in Nature and Society. 2022.
[8]
M. Alizadeh, J. Ma, N. Mahdavi-Amiri, M. Marufuzzaman, R. Jaradat, A stochastic programming model for a capacitated location-allocation problem with heterogeneous demands, Computers & Industrial Engineering 137 (2019).
[9]
R. Banomyong, P. Varadejsatitwong, R. Oloruntoba, A Systematic Review of Humanitarian Operations, Humanitarian Logistics and Humanitarian Supply Chain Performance Literature 2005 to 2016, Annals of Operations Research 283 (1–2) (2019) 71–86.
[10]
G. Bruno, A. Diglio, C. Piccolo, L. Cannavacciuolo, Territorial reorganization of regional blood management systems: Evidences from an Italian case study, Omega 89 (2019) 54–70.
[11]
P. Chaiwuttisak, H. Smith, Y. Wu, C. Potts, T. Sakuldamrongpanich, S. Pathomsiri, Location of low-cost blood collection and distribution centres in Thailand, Operations Research for Health Care 9 (2016) 7–15.
[12]
A. Diabat, A. Jabbarzadeh, A. Khosrojerdi, A perishable product supply chain network design problem with reliability and disruption considerations, International Journal of Production Economics 212 (2019) 125–138.
[13]
J. Du, Y. Ji, D. Qu, X. Wu, D. Yang, Three Stage Mixed Integer Robust Optimization Model Applied to Humanitarian Emergency Logistics by Considering Secondary Disasters, IEEE Access 8 (2020) 223255–223270.
[14]
M. Ehrenstein, C.H. Wang, G. Guillén-Gosálbez, Strategic planning of supply chains considering extreme events: Novel heuristic and application to the petrochemical industry, Computers & Chemical Engineering 125 (2019) 306–323.
[15]
B. Fahimnia, A. Jabbarzadeh, A. Ghavamifar, M. Bell, Supply chain design for efficient and effective blood supply in disasters, International Journal of Production Economics 183 (2017) 700–709.
[16]
S.A. Fahmy, A.M. Zaki, Y.H. Gaber, Optimal locations and flow allocations for aggregation hubs in supply chain networks of perishable products, Socio-Economic Planning Sciences 86 (2023).
[17]
G. Galindo, R. Batta, Review of recent developments in OR/MS research in disaster operations management, European journal of operational research 230 (2) (2013) 201–211.
[18]
J.L. Gerberding, H. Falk, I. Arias, R.C. Hunt, In a Moment’s Notice: Surge Capacity for Terrorist Bombings, U.S. Department of Health and Human Sciences, Atlanta, Georgia, 2007.
[19]
S.B. Ghorashi, M. Hamedi, R. Sadeghian, Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO, Neural computing and applications 32 (16) (2020) 12173–12200.
[20]
S. Gunpinar, G. Centeno, An integer programming approach to the bloodmobile routing problem, Transportation research part E: logistics and transportation review 86 (2016) 94–115.
[21]
S. Gupta, M.K. Starr, R.Z. Farahani, N. Matinrad, Disaster management from a POM perspective: Mapping a new domain, Production and Operations Management 25 (10) (2016) 1611–1637.
[22]
B. Hamdan, A. Diabat, A two-stage multi-echelon stochastic blood supply chain problem, Computers & Operations Research 101 (2019) 130–143.
[23]
O. Hashemi-Amiri, F. Ghorbani, R. Ji, Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach, Computers & Industrial Engineering 175 (2023).
[24]
H. Heidari-Fathian, S.H.R. Pasandideh, Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation, Computers & Industrial Engineering 122 (2018) 95–105.
[25]
M.C. Hoyos, R.S. Morales, R. Akhavan-Tabatabaei, OR models with stochastic components in disaster operations management: A literature survey, Computers & Industrial Engineering 82 (2015) 183–197.
[26]
C.L. Hsieh, June). An evolutionary-based optimization for a multi-objective blood banking supply chain model, Springer, Cham, 2014, pp. 511–520.
[27]
A. Hussain, T. Masood, H. Munir, M.S. Habib, M.U. Farooq, Developing resilience in disaster relief operations management through lean transformation, Production Planning & Control (2022) 1–22.
[28]
S. Hu, Z.S. Dong, B. Lev, Supplier selection in disaster operations management: Review and research gap identification, Socio-Economic Planning Sciences 101302 (2022).
[29]
L.M. Jensen, S. Hertz, The Coordination Roles of Relief Organisations in Humanitarian Logistics, International Journal of Logistics Research and Applications 19 (5) (2016) 465–485.
[30]
İ. Karadağ, M.E. Keskin, V. Yiğit, Re-design of a blood supply chain organization with mobile units, Soft Computing 25 (8) (2021) 6311–6327.
[31]
C. Liu, G. Kou, X. Zhou, Y. Peng, H. Sheng, F.E. Alsaadi, Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach, Knowledge-Based Systems 188 (2020).
[32]
N. Loree, F. Aros-Vera, Points of Distribution Location and Inventory Management Model for Post-Disaster Humanitarian Logistics, Transportation Research Part E: Logistics and Transportation Review 116 (2018) 1–24.
[33]
G. Mavrotas, Effective implementation of the ε-constraint method in multi-objective mathematical programming problems, Applied mathematics and computation 213 (2) (2009) 455–465.
[34]
G. Mavrotas, K. Florios, An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems, Applied Mathematics and Computation 219 (18) (2013) 9652–9669.
[35]
M. Mohammadi, P. Jula, R. Tavakkoli-Moghaddam, Reliable single-allocation hub location problem with disruptions, Transportation Research Part E: Logistics and Transportation Review 123 (2019) 90–120.
[36]
A.M. Mohammed, S.O. Duffuaa, A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks, Expert Systems with Applications 140 (2020).
[37]
M. Najafi, A. Ahmadi, H. Zolfagharinia, Blood inventory management in hospitals: Considering supply and demand uncertainty and blood transshipment possibility, Operations Research for Health Care 15 (2017) 43–56.
[38]
National Blood Authority. (2013). Managing blood and blood product inventory: guidelines for australian health providers. Tech. Rep.
[39]
O. Oke, S. Siddiqui, Efficient automated schematic map drawing using multiobjective mixed integer programming, Computers & Operations Research 61 (2015) 1–17.
[40]
A. Pirabán, W.J. Guerrero, N. Labadie, Survey on blood supply chain management: Models and methods, Computers & Operations Research 112 (2019).
[41]
M. Rabbani, M. Aghabegloo, H. Farrokhi-Asl, Solving a bi-objective mathematical programming model for bloodmobiles location routing problem, International Journal of Industrial Engineering Computations 8 (1) (2017) 19–32.
[42]
Y. Rahimi, S.A. Torabi, R. Tavakkoli-Moghaddam, A new robust-possibilistic reliable hub protection model with elastic demands and backup hubs under risk, Engineering Applications of Artificial Intelligence 86 (2019) 68–82.
[43]
K. Ransikarbum, S.J. Mason, Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration, International Journal of Production Economics 182 (2016) 324–341.
[44]
K. Ransikarbum, S.J. Mason, A bi-objective optimisation of post-disaster relief distribution and short-term network restoration using hybrid NSGA-II algorithm, International Journal of Production Research 60 (19) (2022) 5769–5793.
[45]
N. Razavi, H. Gholizadeh, S. Nayeri, T.A. Ashrafi, A robust optimization model of the field hospitals in the sustainable blood supply chain in crisis logistics, Journal of the Operational Research Society 72 (12) (2021) 2804–2828.
[46]
M. Sabbaghtorkan, R. Batta, Q. He, Prepositioning of assets and supplies in disaster operations management: Review and research gap identification, European Journal of Operational Research 284 (1) (2020) 1–19.
[47]
R. Saedinia, B. Vahdani, F. Etebari, B.A. Nadjafi, Robust gasoline closed loop supply chain design with redistricting, service sharing and intra-district service transfer, Transportation Research Part E: Logistics and Transportation Review 123 (2019) 121–141.
[48]
F.G. Şahinyazan, B.Y. Kara, M.R. Taner, Selective vehicle routing for a mobile blood donation system, European Journal of Operational Research 245 (1) (2015) 22–34.
[49]
M.R.G. Samani, S.A. Torabi, S.M. Hosseini-Motlagh, Integrated blood supply chain planning for disaster relief, International journal of disaster risk reduction 27 (2018) 168–188.
[50]
Z. Sazvar, M. Rahmani, K. Govindan, A sustainable supply chain for organic, conventional agro-food products: The role of demand substitution, climate change and public health, Journal of cleaner production 194 (2018) 564–583.
[51]
S.V. Sharif, P.H. Moshfegh, H. Kashani, Simulation modeling of operation and coordination of agencies involved in post-disaster response and recovery, Reliability Engineering & System Safety 235 (2023).
[52]
D. Teh, T. Khan, Types, Definition and Classification of Natural Disasters and Threat Level. In Handbook of Disaster Risk Reduction for Resilience, Springer, Cham, 2021, pp. 27–56.
[53]
E.B. Tirkolaee, H. Golpîra, A. Javanmardan, R. Maihami, A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study, Socio-Economic Planning Sciences 85 (2023).
[54]
A. Upadhyay, S. Mukhuty, S. Kumari, J.A. Garza-Reyes, V. Shukla, A review of lean and agile management in humanitarian supply chains: Analysing the pre-disaster and post-disaster phases and future directions, Production Planning & Control 33 (6–7) (2022) 641–654.
[55]
L.N. Van Wassenhove, Humanitarian aid logistics: Supply chain management in high gear, Journal of the Operational research Society 57 (5) (2006) 475–489.
[56]
Y. Xu, J. Szmerekovsky, A multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty, Health Care Management Science (2022) 1–19.
[57]
Y. Xu, J. Szmerekovsky, The impact of transshipment on an integrated platelet supply chain: A multi-stage stochastic programming approach, Computers & Industrial Engineering 176 (2023).
[58]
A. Yazdekhasti, J. Ma, A two-echelon two-indenture warranty distribution network development and optimization under batch-ordering inventory policy, International Journal of Production Economics 249 (2022).

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  • (2024)Joint optimization of day-ahead of a microgrid including demand response and electric vehiclesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-10327-828:21(12807-12825)Online publication date: 1-Nov-2024

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Published In

cover image Computers and Industrial Engineering
Computers and Industrial Engineering  Volume 188, Issue C
Feb 2024
1029 pages

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2024

Author Tags

  1. Disaster relief
  2. Blood type supply chain
  3. Bi-objective mathematical model
  4. Location-allocation
  5. Time-dependent routing

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  • (2024)Joint optimization of day-ahead of a microgrid including demand response and electric vehiclesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-024-10327-828:21(12807-12825)Online publication date: 1-Nov-2024

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