Unaddressed problems and research perspectives in scheduling blood collection from donors-Reference-Cited by-同舟云学术

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Unaddressed problems and research perspectives in scheduling blood collection from donors

Published:2017-08-29 Issue:1 Volume:29 Page:84-90
ISSN:0953-7287
Container-title:Production Planning & Control
language:en
Short-container-title:Production Planning & Control

Author:

Baş Güre Seda¹,Carello Giuliana²,Lanzarone Ettore³,Yalçındağ Semih¹

Affiliation:

1. Industrial and Systems Engineering Department, Yeditepe University, Istanbul, Turkey

2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

3. Istituto di Matematica Applicata e Tecnologie Informatiche (IMATI), Consiglio Nazionale delle Ricerche (CNR), Milan, Italy

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management,Computer Science Applications

Link

https://www.tandfonline.com/doi/pdf/10.1080/09537287.2017.1367860

Reference50 articles.

1. A simulation-optimization approach for capacity planning and appointment scheduling of blood donors based on mathematical programming representation of event dynamics

2. Modelling and simulation of blood collection systems: improvement of human resources allocation for better cost-effectiveness and reduction of candidate donor abandonment

3. Allocation of hospital capacity to multiple types of patients

4. Management of Blood Donation System: Literature Review and Research Perspectives

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2. A three-stage matheuristic for home blood donation appointment reservation and collection routing;Flexible Services and Manufacturing Journal;2023-11-11

3. Predicting donations and profiling donors in a blood collection center: a Bayesian approach;Flexible Services and Manufacturing Journal;2023-11-10

4. A Systematic Literature Review of the Blood Supply Chain through Bibliometric Analysis and Taxonomy;Systems;2023-02-25

5. Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning;Information;2023-01-05

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