Hasil Pencarian  ::  Simpan CSV :: Kembali

"This volume presents up-to-date material on the state of the art in evolutionary and deterministic methods for design, optimization and control with applications to industrial and societal problems from Europe, Asia, and America.EUROGEN 2015 was the 11th of a series of International Conferences devoted to bringing together specialists from universities, research institutions and industries developing or applying evolutionary and deterministic methods in design optimization, with emphasis on solving industrial and societal problems.The conference was organised around a number of parallel symposia, regular sessions, and keynote lectures focused on surrogate-based optimization in aerodynamic design, adjoint methods for steady & unsteady optimization, multi-disciplinary design optimization, holistic optimization in marine design, game strategies combined with evolutionary computation, optimization under uncertainty, topology optimization, optimal planning, shape optimization, and production scheduling."

"This book contains thirty-five selected papers presented at the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2017). This was one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS).Topics treated in the various chapters reflect the state of the art in theoretical and numerical methods and tools for optimization, and engineering design and societal applications. The volume focuses particularly on intelligent systems for multidisciplinary design optimization (mdo) problems based on multi-hybridized software, adjoint-based and one-shot methods, uncertainty quantification and optimization, multidisciplinary design optimization, applications of game theory to industrial optimization problems, applications in structural and civil engineering optimum design and surrogate models based optimization methods in aerodynamic design."

"Di Indonesia, kereta api telah menjadi moda transportasi yang populer dan efisien, menawarkan kenyamanan dan kecepatan bagi pengguna. Dalam operasionalnya, salah satu tantangan utama adalah optimisasi, di mana kereta listrik muncul sebagai solusi yang efektif dengan biaya operasional rendah dan gaya traksi tinggi. Profil trayektori kecepatan yang mengindikasikan kecepatan yang diizinkan pada setiap posisi dapat membimbing pengemudi atau sistem operasi otomatis kereta (ATO) untuk mengoperasikan kereta dengan lebih efisien. Penelitian ini mengkaji pendekatan optimisasi untuk trayektori kecepatan Kereta Rel Listrik (KRL), dengan mempertimbangkan konsumsi energi sebagai ukuran kepuasan perusahaan kereta api dan waktu perjalanan sebagai ukuran kepuasan penumpang. Optimisasi kecepatan kereta dapat menggunakan algoritma Hybrid Evolutionary Algorithm (HEA). Penelitian ini mengusulkan metode multiobjektif untuk mengoptimalkan lintasan kecepatan kereta, dengan mempertimbangkan batasan kecepatan, strategi mengemudi yang meliputi fase accelerating, cruising, coasting, braking, serta adaptasi terhadap kondisi kemiringan, dan kurvatur lintasan. Selain itu, penelitian ini menunjukkan bagaimana Pareto front dari setiap generasi algoritma dapat digunakan untuk mengevaluasi dan memilih strategi operasi yang paling efektif. Dalam penelitian ini didapat bahwa hasil dari solusi yang didapat bisa mengurangi total energi sebesar 21.97% dan total waktu tempuh sebesar 5.11%.

---

In Indonesia, trains have become a popular and efficient mode of transportation, offering comfort and speed to users. One of the main challenges in their operation is optimization, where electric trains emerge as an effective solution with low operational costs and high tractive force. A speed trajectory profile that indicates the authorized speed at each position can guide the driver or the automatic train operation (ATO) system to operate the train more efficiently. This study examines the optimization approach for the speed trajectory of Electric Rail Trains (KRL), considering energy consumption as a measure of railway company satisfaction and travel time as a measure of passenger satisfaction. Train speed optimization can utilize the Hybrid Evolutionary Algorithm (HEA). This research proposes a multi-objective method to optimize the train speed trajectory, taking into account speed limits, driving strategies including accelerating, cruising, coasting, and braking phases, as well as adaptation to track slope and curvature conditions. Additionally, this study demonstrates how the Pareto front of each algorithm generation can be used to evaluate and select the most effective operational strategy. In this research, it was found that the results of the solution obtained could reduce total energy by 21.97% and total travel time by 5.11%."