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https://olympias.lib.uoi.gr/jspui/handle/123456789/32713| Title: | Μεταευρετικοί αλγόριθμοι βελτιστοποίησης για διαχείριση εφοδιαστικής αλυσίδας σε περιπτώσεις φυσικών καταστροφών Metaheuristic optimization for logistics in natural disasters |
| Institution and School/Department of submitter: | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών |
| Subject classification: | Logistics |
| Keywords: | Αλγόριθμοι Βελτιστοποίησης,Εφοδιαστική Αλυσίδα,Φυσικές Καταστροφές,Logistics |
| URI: | https://olympias.lib.uoi.gr/jspui/handle/123456789/32713 http://dx.doi.org/10.26268/heal.uoi.12515 |
| Table of contents: | L ist o f F ig u r e s 1.1 Disaster Categories..................................................................................................... 2 1.2 Global trends in disaster events and death tolls (1980-2013)......................... 2 1.3 Share of occurrence of natural disasters by disaster type, (1994-2013). . . 4 1.4 Number of affected people per disaster type (Deaths are excluded). . . . 5 1.5 The trend of natural disaster occurrence in respect of biological, climato logical, geological, hydrometeorological, and meteorological disasters. . . 6 1.6 Number of deaths per income group between (1994-2013).............................. 7 2.1 Disaster management cycle....................................................................................... 12 2.2 The Relief Mission Cycle........................................................................................... 14 2.3 The two conflicting criteria in designing metaheuristics: exploration (di versification) vs exploitation (intensification)..................................................... 24 4.1 Averaged solution error per algorithm and problem (upper part) and zoom in center area (lower p a rt)....................................................................................... 40 4.2 Standard deviation of the solution error per algorithm and problem (upper part) and zoom in center area (lower p art)......................................................... 41 4.3 Success rates of the most promising algorithms per problem......................... 42 4.4 Solution error distribution of the most promising algorithms for all test problems....................................................................................................................... 43 4.5 Results of the pairwise statistical comparisons among the most competi tive algorithms for all test problems..................................................................... 44 L ist o f T a b les 3.1 Notation used in the proposed model................................................................... 28 4.1 Capacity and volume information for vehicles of Type I (small) and II (big). 38 4.2 Commodities information......................................................................................... 38 4.3 Number of vehicles per DC...................................................................................... 39 4.4 Mean, standard deviation, minimum, and maximum solution error values for all algorithms, averaged over all problems. Best values are boldfaced. The “+ ” symbol denotes AP approach constituting of the corresponding algorithms.................................................................................................................... 39 4.5 W ins/losses/draws of row vs column algorithms for all problem instances. 43 |
| Appears in Collections: | Διατριβές Μεταπτυχιακής Έρευνας (Masters) - ΜΥ |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| M.E Θωμαή Κόρκου 2015.pdf | 1.39 MB | Adobe PDF | View/Open |
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