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Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2026 / Cilt: 26 Sayı: 1

Time-Plan Optimization with Genetic Algorithm for Regain of Energy from Train Tracks

TREN RAYLARINDAN ENERJİNİN GERİ KAZANIMI İÇİN GENETİK ALGORİTMA İLE ZAMAN-PLANI OPTİMİZASYONU

Büşra Tural(istanbul ticaret üniversitesi)0000-0003-3645-8761
Metin Turan(İstanbul Ticaret Üniversitesi, Turkey)0000-0002-1941-6693
İbrahim Ethem Demirci(Metro İstanbul A.Ş.)0000-0002-3527-3514
Pages
187–202
Publication Date
2021-04-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Result in of the simulation, around 26% better results compared to the reference study was obtained.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this article, the research results for optimizing the maximum energy gain are shared by adapting the time plan of Metro Istanbul vehicles.
📊
Key Findings & Data:Regenerative energy recovery is based on the principle that energy produced by the trains which make electromagnetic brake is transferred to the other trains that are ready to move.
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Conclusion & Impact:Result in of the simulation, around 26% better results compared to the reference study was obtained.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yeniden Enerji Kazanımı · Metro Zaman Planı · Genetik Algoritma

Abstract

In this article, the research results for optimizing the maximum energy gain are shared by adapting the time plan of Metro Istanbul vehicles. Regenerative energy recovery is based on the principle that energy produced by the trains which make electromagnetic brake is transferred to the other trains that are ready to move. One of the ways to re-energize is to arrange the waiting times of the trains at the stations and to realize the time-plan optimization. Genetic algorithm was used to find station dwell times. Genetic algorithms are search and optimization methods that work similarly to the evolutionary process. This method is based on seeking the best solution according to the principle of survival of the best in multidimensional and complex space. At the end of each repetition, several of the best elite individuals were transferred to the next generation. For each repetition, the number of society individuals has been kept constant, while other individuals have been formed by crossing elite individuals or producing them randomly. Aggressive mutation was applied in cases where the change in station waiting times was not equal to zero. Result in of the simulation, around 26% better results compared to the reference study was obtained.

Özet

Bu makalede, Metro İstanbul araçlarından zaman planı uyarlanarak maksimum enerji kazanımının optimize edilmesine yönelik araştırma sonuçları paylaşılmıştır. Yeniden enerji kazanımı (rejeneratif enerji), elektromanyetik frenleme yapan trenlerin ürettiği enerjiyi hatta hareket etmeye hazır durumunda bulunan diğer trenlere aktarması prensibine dayanmaktadır. Yeniden enerji kazanımı elde etmenin en etkili yollarından birisi, trenlerin istasyonlarda bekleme sürelerinde düzenleme yaparak zaman-planı en iyileştirmesinin gerçekleştirilmesidir. Bu oldukça karışık ve elle yapılması mümkün olmayan bir NP problemi olduğundan bu çalışmada bekleme sürelerini bulmak için genetik algoritma kullanılmıştır. Genetik algoritmalar, evrimsel sürece benzer şekilde çalışan arama ve en iyileştirme yöntemidir. Bu yöntem çok boyutlu ve karmaşık uzayda en iyinin hayatta kalması ilkesine göre en iyi çözümü aramaya dayanır. Her tekrar sonunda en iyi birkaç elit birey bir sonraki nesle aktarılmıştır. Her tekrarda toplam birey sayısı sabit tutulmuş, diğer bireyler ise elit bireylerin çaprazlanması sonucu veya rastgele üretilmesiyle oluşturulmuştur. Agresif mutasyon işlemi, istasyon bekleme sürelerindeki değişimin sıfıra eşit olmadığı durumlarda uygulanmıştır. Yapılan simülasyon sonucunda, genetik algoritma ile elde edilen yeni bekleme süreleriyle trenlerin hızlanma ve frenleme anlarındaki örtüşme, referans çalışmaya göre %26 civarında daha iyi sonuçlar elde edilmiştir. Referans çalışmada %60 oranında olan trenlerin örtüşme anları bu çalışma ile %76 ‘ya kadar çıkartılmıştır.

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Cite This Article

Tural, B., Turan, M., Demirci, İ. E. (2021). Time-Plan Optimization with Genetic Algorithm for Regain of Energy from Train Tracks. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 26 Sayı: 1, 187–202. https://doi.org/10.17482/uumfd.687214

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