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A fuzzy-based prediction approach for blood delivery using machine learning and genetic algorithm

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 1 (2022)

DOI
10.11591/ijece.v12i1.pp1056-1068
ISSN
2722-2578, 2088-8708
Published
2022-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Multiple diseases require a blood transfusion on daily basis. The process of a blood transfusion is successful when the type and amount of blood is available and when the blood is transported at the right time from the blood bank to the operating room. Blood distribution has a large portion of the cost in hospital logistics. The blood bank can serve various hospitals; however, amount of blood is limited due to donor shortage. The transportation must handle several requirements such as timely delivery, vibration avoidance, temperature maintenance, to keep the blood usable. In this paper, we discuss in first section the issues with blood delivery and constraint. The second section present routing and scheduling system based on artificial intelligence to deliver blood from the blood-banks to hospitals based on single blood bank and multiple blood banks with respect of the vehicle capacity used to deliver the blood and creating the shortest path. The third section consist on solution for predicting the blood needs for each hospital based on transfusion history using machine learning and fuzzy logic. The last section we compare the results of well-known solution with our solution in several cases such as shortage and sudden changes.

Collaboration network of the authors

26 authors · 55 co-authorship links · 6 across different organisations

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  • University of Hassan II Casablanca
  • École Normale Supérieure de l'Enseignement Technique de Mohammedia
  • Arts et Métiers
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Abir El YamamiUniversity of Hassan II Casablanca4
Morad HajjiUniversity of Hassan II Casablanca4
Ansar DaghouriUniversity of Hassan II Casablanca3
Mustapha RiadUniversity of Hassan II Casablanca3
Abdelghani LakhdarUniversity of Hassan II Casablanca2
Zineb LaabidUniversity of Hassan II Casablanca2
El Hossein IllousamenAffiliation unknown2
Lamia MoudoubahUniversity of Hassan II Casablanca2
Amina OuatiqUniversity of Hassan II Casablanca2
Kamal ElGuemmatAffiliation unknown2
Soukaina GouraguineUniversity of Hassan II Casablanca2
Aziz MoumenUniversity of Hassan II Casablanca1
Abdelilah ChahidÉcole Normale Supérieure de l'Enseignement Technique de Mohammedia1
Souad AhrizÉcole Normale Supérieure de l'Enseignement Technique de Mohammedia1
Kamal El GuemmatÉcole Normale Supérieure de l'Enseignement Technique de Mohammedia1
Es-Saâdia AoulaUniversity of Hassan II Casablanca1
Omar BouattaneUniversity of Hassan II Casablanca1
Mohamed YoussfiUniversity of Hassan II Casablanca1
El Mehdi Ben LaoulaUniversity of Hassan II Casablanca1
Omar ElfahimUniversity of Hassan II Casablanca1
Ayoub El-AlamiUniversity of Hassan II Casablanca1
Younes NadirUniversity of Hassan II Casablanca1
Ali SiadatArts et Métiers1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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