[Εξώφυλλο]

Ανάλυση Δικτύων Πληγεισών Περιοχών για την Υποστήριξη των Λειτουργιών Διανομής σε Ανθρωπιστικές Αλυσίδες Εφοδιασμού = Network Analysis at Disaster-Affected Areas for Supporting Distribution Operations in Humanitarian Supply Chains.

Ξενοφών Ταουκτσής

Περίληψη


Αρχικά, παρουσιάζονται οι θεωρητικές έννοιες, σχετικά με την εφοδιαστική αλυσίδα και την ανθρωπιστική εφοδιαστική. Επιπλέον, γίνεται μελέτη εννοιών από την επιστήμη των δικτύων και τη θεωρία πληροφορίας όπως τα μέτρα κεντρικότητας και η εντροπία. Ακόμα, αναφέρονται θέματα στατιστικής ανάλυσης δεδομένων, για τη μελέτη των συσχετίσεων και των εξαρτήσεων. Επιπλέον, παρουσιάζεται η σημασία των τεχνητών νευρωνικών δικτύων στην ποσοτική ανάλυση της έρευνας. Στο δεύτερο κεφάλαιο, πραγματοποιείται η παρουσίαση της βιβλιογραφικής επισκόπησης σχετικά με την εμφάνιση και την αντιμετώπιση αβέβαιων καταστροφών, φυσικών ή ανθρωπογενών σύμφωνα με την επιστήμη των δικτύων. Γίνεται παρουσίαση ερευνών, σχετικών με «επιθέσεις» σε διάφορες κατηγορίες δικτύων καθώς και η χρήση των κατάλληλων μέτρων κεντρικότητας για την αντιμετώπισή μιας κρίσης. Στο τρίτο κεφάλαιο, παρουσιάζεται η μεθοδολογία για την αντιμετώπιση εμφάνισης των αβέβαιων καταστροφών σε ένα δίκτυο. Γίνεται παραγωγή εμπειρικών δεδομένων από ένα σημαντικό πλήθος τυχαίων δικτύων. Υπολογίζονται τα διάφορα μέτρα κεντρικότητας καθώς και το κόστος διανομής ως ένα τυπικό πρόβλημα του «Περιοδεύοντος Πωλητή» του κάθε κόμβου και του κάθε δικτύου. Εντοπίζονται, οι συσχετίσεις και οι εξαρτήσεις των επιλεγμένων μεταβλητών, με σκοπό την εφαρμογή στοχευμένων «επιθέσεων» σε συνδέσεις σημαντικών κόμβων σε πραγματικά δίκτυα. Ακόμα, διεξάγονται προσομοιώσεις με τη χρήση τεχνιτών νευρωνικών δικτύων βαθιάς μάθησης για τον εντοπισμό του κατάλληλου κόμβου εγκατάστασης ενός κέντρου διανομής που καλείται να εξυπηρετήσει μια ανθρωπιστική εφοδιαστική. Ολοκληρώνοντας, στο τέταρτο κεφάλαιο, γίνεται εφαρμογή των αποτελεσμάτων της ποσοτικής ανάλυσης σε μια μελέτη περίπτωσης ενός δικτύου πληγεισών περιοχών της Ιαπωνίας για την παροχή ανθρωπιστικής βοήθειας. Συνδυάζονται τα συμπεράσματα από τα αποτελέσματα των συσχετίσεων και των εξαρτήσεων καθώς και η χρήση της προβλεπτικής ικανότητας του νευρωνικού δικτύου για την καταλληλότερη επιλογή του κέντρου διανομής μετά από «επιθέσεις» σε συνδέσεις σημαντικών κόμβων του δικτύου.

We choose to begin our work by thoroughly explaining some theoretical information related to supply chain and humanitarian supply chain. We also examine, information concerning “Network Science” and “Information Theory” such as centrality metrics and entropy. Furthermore, based on statistical analysis of data, we deal correlations and dependencies. Also, we underline the importance of artificial neural networks in quantitative analysis of our research. In the second chapter, we include a detailed literature review concerning possible disasters, caused either by humans or by nature, according to network science. There is also the presentation of previous analysis related to “attacks” against different categories of networks, as well as, the use of the suitable centrality metrics as far as a crisis is concerned. In the third chapter, we deal with the methods of facing uncertain disasters as a network. In fact, we produce empirical data from several random networks, and we also estimate centrality metrics and distribution cost, as a typical “Travel Salesman Problem”. We also, detect correlations and dependencies of the chosen variables in order to implement “attacks” against the links of important nodes in real networks. Furthermore, we carry out simulations by using “Deep Learning Artificial Neural Networks” so as to detect the suitable node for the distribution center installation. To conclude, we finally implement the results of the quantitative analysis in the case of a network which concerns affected areas in Japan, so as to offer humanitarian support. We actually, combine the results of correlations and dependencies with the predictive ability of the “Neural Network” so as to predict and choose the suitable distribution center after next “attacks” against the links of important nodes in the network.

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