Measuring the sustainability and resilience of blood supply chains

[1]  Harshal Lowalekar,et al.  Model for blood collections management , 2010, Transfusion.

[2]  George Halkos,et al.  Treating undesirable outputs in DEA: A critical review , 2019, Economic Analysis and Policy.

[3]  Seyyed-Mahdi Hosseini-Motlagh,et al.  Toward a coordination of inventory and distribution schedules for blood in disasters , 2020 .

[4]  S. Ali Torabi,et al.  Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach , 2018, Comput. Ind. Eng..

[5]  Jiafu Su,et al.  Blood supply chain operation considering lifetime and transshipment under uncertain environment , 2021, Appl. Soft Comput..

[6]  Mir Saman Pishvaee,et al.  Blood supply chain network design considering blood group compatibility under uncertainty , 2017, Int. J. Prod. Res..

[7]  Babak Abbasi,et al.  On the volatility of blood inventories , 2018, Int. Trans. Oper. Res..

[8]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[9]  Stelios Rozakis,et al.  Efficiency under different methods for incorporating undesirable outputs in an LCA+DEA framework: A case study of winter wheat production in Poland. , 2020, Journal of environmental management.

[10]  W. H. M. Zijm,et al.  Exploring recommendations for circular supply chain management through interactive visualisation , 2021, Decis. Support Syst..

[11]  Timo Kuosmanen,et al.  Duality of weakly disposable technology , 2011 .

[12]  Mehdi Toloo,et al.  Developing a new chance constrained NDEA model to measure performance of sustainable supply chains , 2020, Annals of Operations Research.

[13]  Chiang Kao,et al.  Efficiency decomposition in network data envelopment analysis: A relational model , 2009, Eur. J. Oper. Res..

[14]  Liron Yedidsion,et al.  Minimizing operational costs by restructuring the blood sample collection chain , 2015 .

[15]  Ruiyue Lin,et al.  Modified super-efficiency DEA models for solving infeasibility under non-negative data set , 2018, INFOR Inf. Syst. Oper. Res..

[16]  Laurens Cherchye,et al.  Multi-output efficiency with good and bad outputs , 2015, Eur. J. Oper. Res..

[17]  Reza Farzipoor Saen,et al.  A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context , 2015, Comput. Oper. Res..

[18]  Bahar Yetis Kara,et al.  Selective vehicle routing for a mobile blood donation system , 2015, Eur. J. Oper. Res..

[19]  Jiasen Sun,et al.  DEA cross-efficiency evaluation considering undesirable output and ranking priority: a case study of eco-efficiency analysis of coal-fired power plants , 2017 .

[20]  Brian W. Hollocks.  Enterprise Resources Planning and Beyond: Integrating Your Entire Organization , 2001, J. Oper. Res. Soc..

[21]  W. Liu,et al.  A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs , 2007, J. Oper. Res. Soc..

[22]  B. Abbasi,et al.  A two-stage stochastic programming model for inventory management in the blood supply chain , 2017 .

[23]  Seyed Hamid Reza Pasandideh,et al.  Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation , 2018, Comput. Ind. Eng..

[24]  D. Triulzi,et al.  Electronic enhancements to blood ordering reduce component waste , 2016, Transfusion.

[25]  Razamin Ramli,et al.  Developing a two-stage approach of super efficiency slack-based measure in the presence of non-discretionary factors and mixed integer-valued data envelopment analysis , 2018, Expert Syst. Appl..

[26]  K. Pfaff.  Assessing the risk of pre-existing grievances in non-democracies: The conditional effect of natural disasters on repression , 2020 .

[27]  Jiazhen Huo,et al.  Undesirable factors in integer-valued DEA: Evaluating the operational efficiencies of city bus systems considering safety records , 2012, Decis. Support Syst..

[28]  M. Postma,et al.  The costs of producing a unit of blood in Zimbabwe , 2016, Transfusion.

[29]  Timo Kuosmanen.  Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs , 2005 .

[30]  M. Sönmezoglu,et al.  Effects of a major earthquake on blood donor types and infectious diseases marker rates , 2005, Transfusion medicine.

[31]  Ali Emrouznejad,et al.  A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA , 2010, Eur. J. Oper. Res..

[32]  Holger Scheel,et al.  Undesirable outputs in efficiency valuations , 2001, Eur. J. Oper. Res..

[33]  Reza Farzipoor Saen,et al.  Developing network data envelopment analysis model for supply chain performance measurement in the presence of zero data , 2015, Expert Syst. J. Knowl. Eng..

[34]  Sebastián Lozano,et al.  Data envelopment analysis of integer-valued inputs and outputs , 2006, Comput. Oper. Res..

[35]  Christel Kamp,et al.  Management of blood supplies during an influenza pandemic , 2010, Transfusion.

[36]  Seyyed-Mahdi Hosseini-Motlagh,et al.  Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty , 2020 .

[37]  Francisco Rodrigues Lima Junior,et al.  An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics , 2020, Comput. Ind. Eng..

[38]  Timo Kuosmanen,et al.  Discrete and integer valued inputs and outputs in data envelopment analysis , 2015 .

[39]  Chiang Kao,et al.  Measuring the effects of undesirable outputs on the efficiency of production units , 2020, Eur. J. Oper. Res..

[40]  M. Raturi,et al.  The blood supply management amid the COVID-19 outbreak , 2020, Transfusion Clinique et Biologique.

[41]  Reza Farzipoor Saen,et al.  Assessing sustainability of supply chains: An inverse network dynamic DEA model , 2019, Comput. Ind. Eng..

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

[43]  Chiang Kao,et al.  Efficiency measurement for network systems: IT impact on firm performance , 2010, Decis. Support Syst..

[44]  Jose Arturo Garza-Reyes,et al.  Sustainable stochastic production and procurement problem for resilient supply chain , 2020, Comput. Ind. Eng..

[45]  Seyyed-Mahdi Hosseini-Motlagh,et al.  Robust and stable flexible blood supply chain network design under motivational initiatives , 2020 .

[46]  Grisselle Centeno,et al.  Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals , 2015, Comput. Oper. Res..

[47]  Yuhong Li,et al.  Establishing a frame of reference for measuring disaster resilience , 2021, Decis. Support Syst..

[48]  Manoj Kumar,et al.  Green logistics decision support system for blood distribution in time window , 2018 .

[49]  김광욱,et al.  Network Data Envelopment Analysis를 적용한 생산 및 폐수처리 효율에 관한 연구 , 2015 .

[50]  David Sundaram,et al.  Sustainable supply chain management: Decision models for transformation and maturity , 2018, Decis. Support Syst..

[51]  J. Dropkin,et al.  Effect of resiliency and age on musculoskeletal injuries and lost workdays in emergency medical service personnel , 2019, International Journal of Industrial Ergonomics.

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

[53]  Emmanuel Thanassoulis,et al.  Negative data in DEA: a directional distance approach applied to bank branches , 2004, J. Oper. Res. Soc..

[54]  Sudip Bhattacharjee,et al.  Economic sustainability of closed loop supply chains: A holistic model for decision and policy analysis , 2015, Decis. Support Syst..

[55]  Joe Zhu,et al.  Super-efficiency infeasibility and zero data in DEA , 2012, Eur. J. Oper. Res..

[56]  C. Mock,et al.  Assessing the appropriateness of blood transfusion among injured patients at a Ghanaian tertiary hospital: Time for clarity on the use of a scarce resource. , 2021, Injury.

[57]  Reza Kazemi Matin,et al.  A two-phase approach for setting targets in DEA with negative data , 2011 .

[58]  L. Cecchini,et al.  Environmental efficiency analysis and estimation of CO2 abatement costs in dairy cattle farms in Umbria (Italy): A SBM-DEA model with undesirable output , 2018, Journal of Cleaner Production.

[59]  Cláudia S. Sarrico,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 2001, J. Oper. Res. Soc..

[60]  Joe Zhu,et al.  DEA models for two‐stage processes: Game approach and efficiency decomposition , 2008 .

[61]  Miika Linna,et al.  Variation of platelet production and discard rates in 17 blood centers representing 10 European countries from 2000 to 2002 , 2006, Transfusion.

[62]  Mohammad Tavassoli,et al.  A new super-eciency model in the presence of both zero data and undesirable outputs , 2014 .

[63]  Lianmin Zhang,et al.  Scheduling the distribution of blood products: A vendor-managed inventory routing approach , 2020 .

[64]  Hervé Leleu,et al.  Shadow pricing of undesirable outputs in nonparametric analysis , 2013, Eur. J. Oper. Res..

[65]  Lawrence M. Seiford,et al.  Modeling undesirable factors in efficiency evaluation , 2002, Eur. J. Oper. Res..

[66]  F. Hosseinzadeh Lotfi,et al.  Undesirable inputs and outputs in DEA models , 2005, Appl. Math. Comput..

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

[68]  Martin W. P. Savelsbergh,et al.  Delivery strategies for blood products supplies , 2009, OR Spectr..

[69]  Timo Kuosmanen,et al.  Theory of integer-valued data envelopment analysis under alternative returns to scale axioms , 2009 .

[70]  Reza Tavakkoli-Moghaddam,et al.  Reliable blood supply chain network design with facility disruption: A real-world application , 2020, Eng. Appl. Artif. Intell..

[71]  Zhixiang Zhou,et al.  Modeling undesirable output with a DEA approach based on an exponential transformation: An application to measure the energy efficiency of Chinese industry , 2019, Journal of Cleaner Production.

[72]  Mohammad Izadikhah,et al.  Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains , 2018, Int. J. Prod. Res..

[73]  Lipan Feng,et al.  Effects of supply chain competition on firms’ product sustainability strategy , 2020 .

[74]  Alireza Amirteimoori,et al.  Two-stage additive integer-valued data envelopment analysis models , 2019, Journal of Modelling in Management.

[75]  Biresh K. Sahoo,et al.  Integer data in DEA: Illustrating the drawbacks and recognizing congestion , 2019, Comput. Ind. Eng..

[76]  Ali Emrouznejad,et al.  An integer-valued data envelopment analysis model with bounded outputs , 2011, Int. Trans. Oper. Res..

[77]  Ali Emrouznejad,et al.  A modified Semi-Oriented Radial Measure for target setting with negative data , 2014 .

[78]  Farookh Khadeer Hussain,et al.  Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis , 2019, IEEE Transactions on Cloud Computing.

[79]  Hans Gombotz,et al.  Estimating the cost of blood: past, present, and future directions. , 2007, Best practice & research. Clinical anaesthesiology.

[80]  Suchithra Rajendran,et al.  Platelet ordering policies at hospitals using stochastic integer programming model and heuristic approaches to reduce wastage , 2017, Comput. Ind. Eng..

[81]  Michael O'Sullivan,et al.  On metrics for supply chain resilience , 2020, Eur. J. Oper. Res..

[82]  C.A.K. Lovell,et al.  Multilateral Productivity Comparisons When Some Outputs are Undesirable: A Nonparametric Approach , 1989 .

[83]  M. I. M. Wahab,et al.  Approximate dynamic programming modeling for a typical blood platelet bank , 2014, Comput. Ind. Eng..

[84]  Ali Emrouznejad,et al.  On the boundedness of the SORM DEA models with negative data , 2010, Eur. J. Oper. Res..

[85]  Parviz Ghandforoush,et al.  A DSS to manage platelet production supply chain for regional blood centers , 2010, Decis. Support Syst..

[86]  S. A. R. Khan,et al.  A state-of-the-art review and meta-analysis on sustainable supply chain management: Future research directions , 2021 .

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

[88]  Jan van der Wal,et al.  Blood platelet production: Optimization by dynamic programming and simulation , 2007, Comput. Oper. Res..

[89]  Christine A. Desan.  From Blood to Profit: Making Money in the Practice and Imagery of Early America , 2008 .

[90]  Gongbing Bi,et al.  Resource allocation for branch network system with considering heterogeneity based on DEA method , 2018, Central Eur. J. Oper. Res..

[91]  Reza Farzipoor Saen,et al.  Developing a New Theory of Integer-Valued Data Envelopment Analysis for Supplier Selection in the Presence of Stochastic Data , 2014, Int. J. Inf. Syst. Supply Chain Manag..

[92]  R. Färe,et al.  PRODUCTIVITY AND INTERMEDIATE PRODUCTS: A FRONTIER APPROACH , 1995 .

[93]  Seyyed-Mahdi Hosseini-Motlagh,et al.  A mixed resilient-efficient approach toward blood supply chain network design , 2020, Int. Trans. Oper. Res..

[94]  Debadyuti Das,et al.  The impact of Sustainable Supply Chain Management practices on firm performance: Lessons from Indian organizations , 2018, Journal of Cleaner Production.

[95]  Xiaolan Xie,et al.  Mathematical Programming Models for Annual and Weekly Bloodmobile Collection Planning , 2015, IEEE Transactions on Automation Science and Engineering.

[96]  Reza Farzipoor Saen,et al.  Predicting group membership of sustainable suppliers via data envelopment analysis and discriminant analysis , 2019, Sustainable Production and Consumption.

[97]  Ali Diabat,et al.  A perishable product supply chain network design problem with reliability and disruption considerations , 2019, International Journal of Production Economics.

[98]  Reza Ramezanian,et al.  Blood supply chain network design under uncertainties in supply and demand considering social aspects , 2017 .

[99]  Mehdi Toloo,et al.  A new non-radial directional distance model for data envelopment analysis problems with negative and flexible measures , 2020 .

[100]  Seyyed-Mahdi Hosseini-Motlagh,et al.  A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation , 2019, Comput. Ind. Eng..

[101]  Majid Azadi,et al.  A novel network data envelopment analysis model for evaluating green supply chain management , 2014 .

[102]  Ali Diabat,et al.  Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation , 2020 .

[103]  R. Cardigan,et al.  Effects of the COVID-19 pandemic on supply and use of blood for transfusion , 2020, The Lancet Haematology.

[104]  Alireza Amirteimoori,et al.  Inputs and outputs classification in integer-valued data envelopment analysis , 2019, Measurement.

[105]  Reza Kazemi Matin,et al.  A directional semi-oriented radial DEA measure: an application on financial stability and the efficiency of banks , 2018, Ann. Oper. Res..

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

[107]  Madjid Tavana,et al.  A new dynamic range directional measure for two-stage data envelopment analysis models with negative data , 2018, Comput. Ind. Eng..

[108]  Chris N. Potts,et al.  Location of low-cost blood collection and distribution centres in Thailand , 2016 .

[109]  Shannon M. Lloyd,et al.  Measuring Relative Efficiency and Effectiveness , 2014 .