HİBRİT GASA İLE İSTANBUL'DA DİNAMİK OTOBÜS

Yıl 2025, Cilt: 13 Sayı: 2, 487 - 516, 17.12.2025

Sara El Tannir , Yakup Çelikbilek

https://doi.org/10.14514/beykozad.1634075

https://izlik.org/JA27WE85WH

Öz

Ulaşım, herhangi bir şehirde en dıştaki banliyöleri merkeze bağlayan önemli bir unsurdur. Ulaşım sistemini yükseltmek için yapılan çalışmaların çoğu, şirketin faydasına göre araçların programını optimize etmek yoluyla yapılır. Odak noktası yolculara kaydığında, diğer projeler gelecekteki talebi tahmin etmek için yolcu sayısı verilerini toplar. Ancak bu proje, hibrit genetik algoritma kullanarak bekleme istasyonlarında kalan yolcu sayısını en aza indirme amacıyla bir otobüsün programını optimize ederek yolcunun deneyimini dikkate alır. Çizelgeleme artık statik olmayacak, dinamik olacaktır ve uzaysal-zamansal faktörleri, filo büyüklüğünü, araç kapasitesini ve daha fazlasını göz önünde bulundurarak mümkün olan her yolcuya hizmet etmek için periyodik olarak değişecektir. Çoğu çizelgeleme problemi, çizelgeleme problemlerini çözmek için genetik algoritmayı ve onun varyasyonlarını kullanmaktadır ve bu çalışma genetik algoritma ile benzetilmiş tavlamayı entegre ederek, genetik algoritmanın zayıf yerel aramasından kaçınır ve süreci hızlandırır. Önerilmekte olan entegre genetik algoritma ve benzetilmiş tavlama, bir günde kalan toplam yolcu sayısını en aza indiren bir çözüm bulmada hem genetik algoritmadan hem de benzetilmiş tavlamadan daha iyi sonuçlar ürettiğini kanıtlamıştır. Bu teknik, hızlı otobüs taşımacılığı, metro, tren ve daha fazlası gibi diğer ulaşım biçimlerine uyarlanabilir.

Anahtar Kelimeler

Dinamik Çizelgeleme , Optimizasyon , Genetik Algoritma , Benzetilmiş Tavlama , Hibrit GASA

DYNAMIC BUS SCHEDULING IN ISTANBUL WITH HYBRID GASA

https://izlik.org/JA27WE85WH

Öz

Transportation is a key element in any city, connecting the outermost suburbs to the center. Most studies done to elevate the transportation system are through optimizing the schedule of vehicles with respect to company’s benefit. When the focus shifts to the riders, other projects gather ridership data to predict future demand. However, this project considers the rider’s experience by optimizing a bus’s schedule with the objective of minimizing passengers remaining at waiting stations using a hybrid genetic algorithm. The schedule will no longer be static, but dynamic, morphing periodically, to service every passenger possible while considering spaciotemporal factors, fleet size, vehicle capacity and more. Most scheduling problems use the genetic algorithm and its variations to solve scheduling problems, and this study integrates it with simulated annealing, since their combination can avoid poor local search of genetic algorithm and speeding its process. The proposed integrated genetic algorithm and simulated annealing has proven to be the best both genetic algorithm and simulated annealing in finding a solution that minimized the total number of remaining passengers in a day. This technique can be translated into other forms of transportation such as bus rapid transport, subways, trains and more.

Anahtar Kelimeler

Dynamic Scheduling , Optimization , Genetic Algorithm , Simulated Annealing , Hybrid GASA

Kaynakça

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