A decision-making tool for the determination of the distribution center location in a humanitarian logistics network

•We propose a novel tool for selecting a distribution center for aid distribution.•We combine knowledge from different fields for a hybrid decision-making approach.•We provide a promising solution for timely decisions in humanitarian operations.•Results allow using a small proportion of all nodes in...

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Veröffentlicht in:Expert systems with applications Jg. 238; S. 122010
Hauptverfasser: Taouktsis, Xenofon, Zikopoulos, Christos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.03.2024
Schlagworte:
Binary classification problem
Centrality index
Deep neural network
Humanitarian logistics
Network science
Traveling salesman problem
Centrality index
HL
DNN
HN
DL
DM
DT
Traveling salesman problem
LLRP
EC
ML
DGC
DSS
MS
AI
F1
BCP
TSP
Deep neural network
DROs
NA
ReLU
ANN
BC
BD
FI
NR
CNRA
FL
F
FLP
CIs
Binary classification problem
HOs
SC
ANOVA
DCs
PPV
TPR
CC
SS
ACC
CM
DMT
MCC
Network science
SGC
Humanitarian logistics
LC
HRC
HA
HC
CTN
SCM
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:•We propose a novel tool for selecting a distribution center for aid distribution.•We combine knowledge from different fields for a hybrid decision-making approach.•We provide a promising solution for timely decisions in humanitarian operations.•Results allow using a small proportion of all nodes in a network to make decisions. The distribution of humanitarian aid is a vital issue for humanity's future. In recent years, the management of humanitarian crises has become more crucial than it was a decade ago. Due to the volatility and urgency that characterize such situations, one of the most important challenges globally is the optimization of decisions regarding the timely distribution of aid during humanitarian operations. Our main goal is to develop an innovative decision-making tool, essential for non-profit organizations and governments that aims at the prompt selection of the location of the distribution center of humanitarian aid, in cases of natural or human-made disasters. The proposed tool is based on network science principles and can be used for selecting a suitable node for the installation of a distribution center during the beginning of a humanitarian crisis, considering that networks have a volatile nature and require quick decisions. For the configuration of the proposed tool we use a combination of a classical heuristic algorithm and predictive models based on a binary classification problem with the support of a supervised deep neural network. It is developed using the R programming language with the contribution of the “Shiny” package (web application framework for R) along with other packages for network analysis, data manipulation and visualization.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.122010