Blood supply chain network design considering blood group compatibility under uncertainty

This paper addresses the design of a blood supply chain (SC) network considering blood group compatibility. To this aim, a bi-objective mathematical programming model is developed which minimises the total cost as well as the maximum unsatisfied demand. Due to uncertain nature of some input parameters, two novel robust possibilistic programming models are proposed based on credibility measure. The data of a real case study are then used to illustrate the applicability and performance of the proposed models as well as validating the proposed robust possibilistic programming approach. The obtained results show the superiority of the developed models and significant cost savings compared to current existed blood SC network.

[1]  Jun Huang,et al.  The Multi-period Location-allocation Problem of Engineering Emergency Blood Supply Systems , 2012 .

[2]  Reza Tavakkoli-Moghaddam,et al.  Multi-objective design of an organ transplant network under uncertainty , 2014 .

[3]  Matthias Ehrgott,et al.  Multicriteria Optimization , 2005 .

[4]  W. Flegel.  Fresh blood for transfusion: how old is too old for red blood cell units? , 2012, Blood transfusion = Trasfusione del sangue.

[5]  Reza Tavakkoli-Moghaddam,et al.  An interactive approach for designing a robust disaster relief logistics network with perishable commodities , 2016, Comput. Ind. Eng..

[6]  Anna Nagurney,et al.  Supply Chain Network Design of a Sustainable Blood Banking System , 2011 .

[7]  Hasan Selim,et al.  A fuzzy multi-objective covering-based vehicle location model for emergency services , 2007, Comput. Oper. Res..

[8]  Cem Saydam,et al.  A multiperiod set covering location model for dynamic redeployment of ambulances , 2008, Comput. Oper. Res..

[9]  Hai Jiang,et al.  A robust counterpart approach to the bi-objective emergency medical service design problem , 2014 .

[10]  Liesje De Boeck,et al.  Optimizing the facility location design of organ transplant centers , 2013, Decis. Support Syst..

[11]  Reza Tavakkoli-Moghaddam,et al.  Towards a psychologically consistent cellular manufacturing system , 2017 .

[12]  Mohd Omar,et al.  Location allocation modeling for healthcare facility planning in Malaysia , 2012, Comput. Ind. Eng..

[13]  Reza Tavakkoli-Moghaddam,et al.  Robust humanitarian relief logistics network planning , 2014 .

[14]  Seyedeh Zahra Hosseinifard,et al.  The inventory centralization impacts on sustainability of the blood supply chain , 2018, Comput. Oper. Res..

[15]  Sally C. Brailsford,et al.  A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making , 2015 .

[16]  Navonil Mustafee,et al.  A game-based approach towards facilitating decision making for perishable products: An example of blood supply chain , 2014, Expert Syst. Appl..

[17]  BeliëNJeroen,et al.  Optimizing the facility location design of organ transplant centers , 2013, DSS 2013.

[18]  Seyed Jafar Sadjadi,et al.  A scenario-based robust optimization approach for batch processing scheduling , 2016 .

[19]  Mir Saman Pishvaee,et al.  Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty , 2012, Comput. Ind. Eng..

[20]  R. Tavakkoli-Moghaddam,et al.  Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty , 2016 .

[21]  Seyed Ali Torabi,et al.  Blood collection management: Methodology and application , 2015 .

[22]  Mir Saman Pishvaee,et al.  A possibilistic programming approach for closed-loop supply chain network design under uncertainty , 2010, Fuzzy Sets Syst..

[23]  許鉅秉,et al.  國際期刊 Transportation Research-Part E---Logistics and Transportation Review 特刊編輯補助 , 2006 .

[24]  John C. Papageorgiou,et al.  Some operations research applications to problems of health care systems (a survey). , 1978, International journal of bio-medical computing.

[25]  F. Jolai,et al.  Optimal design of distributed energy system in a neighborhood under uncertainty , 2016 .

[26]  Gerhard Reinelt,et al.  Locating Health Facilities in Nouna District, Burkina Faso , 2005, OR.

[27]  Armin Jabbarzadeh,et al.  Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application , 2014 .

[28]  A. Muriel,et al.  Un modelo de optimización bi-objetivo para la selección de tecnología y asignación de donantes en la cadena de suministro de sangre , 2014 .

[29]  M. Sheikhalishahi,et al.  Improved design of CMS by considering operators decision-making styles , 2015 .

[30]  F. Jolai,et al.  Optimal investment and unit sizing of distributed energy systems under uncertainty: A robust optimization approach , 2014 .

[31]  Reza Tavakkoli-Moghaddam,et al.  A robust possibilistic programming approach to multi-period location-allocation of organ transplant centers under uncertainty , 2014, Comput. Ind. Eng..

[32]  Brecht Cardoen,et al.  On the design of custom packs: grouping of medical disposable items for surgeries , 2015 .

[33]  S.A. Torabi,et al.  An interactive possibilistic programming approach for multiple objective supply chain master planning , 2008, Fuzzy Sets Syst..

[34]  Naoufel Cheikhrouhou,et al.  An approximation approach to a trade-off among efficiency, efficacy, and balance for relief pre-positioning in disaster management , 2016 .

[35]  S. Ali Torabi,et al.  A robust possibilistic programming approach for pharmaceutical supply chain network design , 2015, Comput. Chem. Eng..

[36]  Michael G. H. Bell,et al.  Supply chain design for efficient and effective blood supply in disasters , 2017 .

[37]  Jeroen Beliën,et al.  Supply chain management of blood products: A literature review , 2012, Eur. J. Oper. Res..

[38]  Abdur Rais,et al.  Operations Research in Healthcare: a survey , 2011, Int. Trans. Oper. Res..

[39]  T. Liao,et al.  Optimization of blood supply chain with shortened shelf lives and ABO compatibility , 2014 .

[40]  C. Hwang.  Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[41]  Mir Saman Pishvaee,et al.  Robust possibilistic programming for socially responsible supply chain network design: A new approach , 2012, Fuzzy Sets Syst..

[42]  Vaidyanathan Jayaraman,et al.  Sustainable supply chains : models, methods and public policy implications , 2011 .

[43]  Haiping Zhu,et al.  A Credibility-Based Fuzzy Programming Model for APP Problem , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[44]  Ali Kaveh,et al.  A Multistage Algorithm for Blood Banking Supply Chain Allocation Problem , 2017 .

[45]  Yian-Kui Liu,et al.  Expected value of fuzzy variable and fuzzy expected value models , 2002, IEEE Trans. Fuzzy Syst..

[46]  Wei Chen,et al.  Design under uncertainty , 2012 .

[47]  E. Çetin,et al.  A Blood Bank Location Model: A Multiobjective Approach , 2009 .

[48]  W. Pierskalla.  Supply Chain Management of Blood Banks , 2005 .

[49]  Siddhartha S. Syam,et al.  A location–allocation model for service providers with application to not-for-profit health care organizations , 2010 .

[50]  L. C. Leung,et al.  Inventory Management of Platelets in Hospitals: Optimal Inventory Policy for Perishable Products with Regular and Optional Expedited Replenishments , 2011 .

[51]  Jeroen Belien,et al.  Supply chain management of blood products: A literature review , 2012 .