Evaluation of potential logistics village alternatives in Sakarya with multi-criteria decision making methods
Bibliographic record
Abstract
İÇİNDEKİLER TEŞEKKÜR ................................................................................................................ i İÇİNDEKİLER .......................................................................................................... ii KISALTMALAR ...................................................................................................... iv TABLOLAR LİSTESİ.............................................................................................. vi ŞEKİLLER LİSTESİ................................................................................................. v ÖZET......................................................................................................................... vii ABSTRACT.............................................................................................................viii BÖLÜM 1. GİRİŞ .......................................................................................................................... 1 1.1. Çalışmanın Amacı............................................................................................. 3 1.2. Literatür Araştırması ......................................................................................... 3 BÖLÜM 2. LOJİSTİK KÖYLER .............................................................................................. 10 2.1. Tedarik Zinciri................................................................................................. 11 2.2. Lojistik ............................................................................................................ 12 2.3. Lojistik Faaliyetler .......................................................................................... 15 2.4. Lojistik Merkez Kavramı ................................................................................ 19 2.4.1. Lojistik köy .............................................................................................. 23 2.4.2. Bir lojistik köyün genel nitelikleri ........................................................... 25 2.4.3. Lojistik köy örnekleri............................................................................... 26 2.4.3.1. Rotterdam (Hollanda)........................................................................ 27 2.4.3.2. Hamburg (Almanya) ......................................................................... 28 2.4.3.3. Quadrante Europa (Interporto Verona) (İtalya) ................................ 29 2.4.3.4. Europlatforms.................................................................................... 30 2.4.3.5. Singapur ............................................................................................ 30 2.4.3.6. Hong-Kong........................................................................................ 31 2.4.3.7. Alliance Global Logistics Hub/Texas/ABD...................................... 32 2.4.3.8. Atlantic Gateway-Halifax Logistics Park/ Kanada ........................... 32 2.4.4. Türkiye’de lojistik köyler......................................................................... 33 2.4.4.1. Samsun (Gelemen) lojistik köyü....................................................... 35 2.4.4.2. Kocaeli (Köseköy) lojistik köyü ....................................................... 35 iii 2.4.4.3. İstanbul (Halkalı) lojistik köyü ......................................................... 36 2.4.4.4. Balıkesir (Gökköy) lojistik köyü....................................................... 37 2.4.4.5. Eskişehir (Hasanbey) lojistik köyü ................................................... 37 2.4.4.6. Uşak lojistik köyü ............................................................................. 38 2.4.4.7. Denizli (Kaklık) lojistik köyü ........................................................... 38 2.4.4.8. Konya (Kayacık) lojistik köyü .......................................................... 39 2.4.4.9. Kahramanmaraş (Türkoğlu) lojistik köyü......................................... 40 2.4.4.10. Erzurum (Palandöken) lojistik köyü ............................................... 41 2.4.4.11. Kars lojistik köyü ............................................................................ 41 BÖLÜM 3. MATERYAL VE YÖNTEM................................................................................... 43 3.1. Lojistik Açısından Sakarya İlinin Değerlendirilmesi...................................... 43 3.2. Sakarya’da Organize Sanayi Bölgeleri............................................................ 44 3.3. Sakarya’da Limanlar ....................................................................................... 45 3.4. Çok Kriterli Karar Verme Yöntem ve Teknikleri ........................................... 45 3.4.1. Best-worst metodu ................................................................................... 47 BÖLÜM 4. UYGULAMA VE BULGULAR ............................................................................. 50 4.1. Sakarya Lojistik Köyünün Yer Seçim Çalışması............................................ 50 BÖLÜM 5. SONUÇ VE ÖNERİLER......................................................................................... 63 KAYNAKLAR ......................................................................................................... 65 EKLER...................................................................................................................... 72
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".