Evidence map›Paper›PMID 35946032›Full record

ArticleApplied mathematical modelling2022

A possibilistic-robust-fuzzy programming model for designing a game theory based blood supply chain network.

Peiman Ghasemi, Fariba Goodarzian, Ajith Abraham, Saeed Khanchehzarrin

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applied mathematical modelling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
13.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 4 institutions in 4 countries.

Peiman GhasemiDepartment of Logistics, Tourism & Service Management, German University of Technology in Oman (GUtech), Muscat, Oman.
Fariba GoodarzianEngineering Group, School of Engineering, University of Seville, Camino de los Descubrimientos s/n, Seville 41092, Spain.
Ajith AbrahamMachine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259. Auburn, WA 98071, USA.
Saeed KhanchehzarrinDepartment of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran.
German University of Technology · OMMachine Intelligence Research Labs · USMazandaran University of Science and Technology · IRUniversidad de Sevilla · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a bi-level blood supply chain network under uncertainty during the COVID-19 pandemic outbreak using a Stackelberg game theory technique. A new two-phase bi-level mixed-integer linear programming model is developed in which the total costs are minimized and the utility of donors is maximized. To cope with the uncertain nature of some of the input parameters, a novel mixed possibilistic-robust-fuzzy programming approach is developed. The data from a real case study is utilized to show the applicability and efficiency of the proposed model. Finally, some sensitivity analyses are performed on the important parameters and some managerial insights are suggested.

Indexed as

Bi-level mixed-integer linear programming modelBlood supply chain networkCOVID-19 pandemicGame theoryMixed possibilistic-robust-fuzzy programming

Identifiers

PMID35946032
PMCPMC9352651
OpenAlexW4289942325

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.