Transit network design and scheduling  using genetic algorithm – a review

Johar, Amita; Jain, Saurabh; Garg, Pooja

doi:10.11121/ijocta.01.2016.00258

Cited by 10 publications

(3 citation statements)

References 25 publications

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“…Its strengths lie in its ability to efciently navigate expansive solution spaces, manage nonlinear objective functions, accommodate a range of goals, and fexibly integrate constraints. Te versatility of the GA in transportation planning is well-documented across many academic reviews, and the GA is the most typically used optimization algorithm for optimal route recommendations [42][43][44][45]. For example, one study [46] utilized the GA to fne-tune the arrival and departure times of trains to better align with passenger schedules, while another application of the GA [47] was used to optimize the establishment of cordon sanitaire for controlling epidemics, taking intricate transportation systems into account.…”

Section: Te Applied Algorithmmentioning

confidence: 99%

An Activity-Based Travel Personalization Tool Driven by the Genetic Algorithm

Dingil,

Esztergár-Kiss

2023

Journal of Advanced Transportation

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The necessity for an external control mechanism that optimizes daily urban trips becomes evident when considering numerous factors at play within a complex environment. This research introduces an activity-based travel personalization tool that incorporates 10 travel decision-making factors driven by the genetic algorithm. To evaluate the framework, a complex artificial scenario is created comprising six activities in a daily plan. Afterwards, the scenario is simulated for predefined user profiles, and the results of the simulation are compared based on the users’ characteristics. The simulations of the scenario successfully demonstrate the appropriate utilization of activity constraints and the efficient implementation of users’ spatiotemporal priorities. In comparison to the base case, significant time savings ranging from 31.2% to 70.2% are observed in the daily activity chains of the simulations. These results indicate that the magnitude of time savings in daily activity simulations depends on how users assign values to the travel decision-making parameters, reflecting the attitudinal differences among the predefined users in this study. This tool holds promise for advancing longitudinal travel behavior research, particularly in gaining a more profound understanding of travel patterns.

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Section: Te Applied Algorithmmentioning

confidence: 99%

An Activity-Based Travel Personalization Tool Driven by the Genetic Algorithm

Dingil,

Esztergár-Kiss

2023

Journal of Advanced Transportation

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“…Selain digunakan untuk mencari solusi dari permasalahan optimalisasi, penelitian sebelumnya juga menunjukkan penggunaan GA untuk menangani masalah penjadwalan. Seperti penelitian tentang penjadwalan penggunaan sumber daya pada perusahaan well-service [11], jadwal transit untuk kendaraan umum [12], jadwal jaga perawat di rumah sakit [13] dan jadwal penggunaan mesin di pabrik Li dan Chen [14] dan [15]. Penelitian-penelitian tersebut menunjukkan bahwa GA mampu menangani permasalahan penjadwalan karena pada dasarnya penjadwalan juga merupakan masalah optimasi yang dapat diselesaikan dengan cara mengenerate beberapa alternatif solusi secara iteratif menggunakan suatu fungsi fitness tertentu.…”

Section: Pendahuluanunclassified

Pengembangan Algoritma Genetika dengan Pendekatan Repetitive Random untuk Penjadwalan Ujian Pendadaran Proyek Tugas Akhir

Wibowo

Avianto

Imantoko

2021

TEKNOSI

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Penjadwalan adalah hal yang umum dilakukan terutama di instansi pendidikan seperti perguruan tinggi. Salah satu kegiatan yang memerlukan proses penjadwalan di perguruan tinggi adalah penjadwalan ujian pendadaran proyek tugas akhir. Faktor-faktor seperti jumlah mahasiswa, ketersediaan dosen, dan ketersediaan ruangan juga membuat proses penjadwalan menjadi lebih kompleks dan memakan waktu jika dilakukan secara manual. Metode Genetic Algorithm (GA) dapat dimanfaatkan untuk mengatasi masalah di dalam proses penjadwalan secara manual. Penelitian ini bertujuan untuk menghasilkan sebuah aplikasi yang diberi nama Automatically Generated Scheduling (ALGEN Scheduling) yang mampu melakukan proses penjadwalan ujian pendadaran dengan lebih mudah. “ALGEN Scheduling” dibangun menggunakan bahasa C# dan menerapkan GA yang telah dimodifikasi dengan pendekatan repetitive random sebagai inti untuk melakukan proses penjadwalan. Pembuatan ALGEN Scheduling dimulai dari perancangan kromosom, implementasi GA termodifikasi dalam bahasa C#, pembuatan GUI dengan tools visual studio, dan integrasi inti penjadwalan dengan GUI. Aplikasi yang dibangun bisa membantu mahasiswa dan dosen penguji dalam menginfokan jadwal ujian pendadaran. Untuk mengetahui kinerja Aplikasi “ALGEN Scheduling”, peneliti melakukan evaluasi dengan menyebar kuesioner kepada responden yang terdiri dari mahasiswa dan dosen. Peneliti berhasil mendapatkan tanggapan sebanyak 101 responden. Berdasarkan pengolahan data responden didapatkan 68% menilai bahwa Aplikasi “ALGEN Scheduling” memiliki tampilan yang bagus dan sangat mudah digunakan untuk menghasilkan jadwal ujian pendadaran dengan tanpa adanya bentrokan jadwal. Sedangkan 19% responden mengatakan bahwa Aplikasi “ALGEN Scheduling” tidak terlalu mudah digunakan tetapi juga tidak terlalu sulit untuk dipelajari. Sisanya, responden menganggap perlu adanya pelatihan khusus sebelum aplikasi benar-benar diterapkan

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“…In order to solve the DRT optimization model, genetic algorithm a common method [28]. For solving the bus line design or optimization model, Johar et al [29] summarizes and concludes the research based on genetic algorithm to solve this kind of problem, and concludes that genetic algorithm is an effective optimization technology. Chakrobority et al [30] shows the effectiveness of genetic algorithm in solving urban public transport network design and optimization problems, and designs a set of programs for solving such problems based on genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Model for the Demand-Responsive Transit With Non-Fixed Stops and Multi-Vehicle Type

Song,

Chen,

Wang

et al. 2023

IEEE Access

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As a new type of public transportation, demand responsive transit has gradually attracted attention for its flexibility and efficiency. In order to solve the problems such as single-vehicle type and fixed stop, and improve its operation efficiency, a collaborative scheduling method combining multi-occupancy vehicle type with non-fixed stop is proposed. Different from the previous studies in scheduling problems of demand responsive transit which only focus on stop mode such as fixed or non-fixed stop or vehicle types containing single-occupancy or multi-occupancy, this paper also studies the vehicle scheduling of demand responsive transit from the perspective of combination of non-fixed stop and multi-vehicle type. In addition, carbon emission cost is innovatively added into the scheduling model, and an improved genetic algorithm with multiple crossovers within individuals is designed to accelerate the convergence speed of the algorithm and improve the solution efficiency. Finally, taking Shijiazhuang downtown regional road network as an example, the validity of the proposed scheduling method is verified. The results show that compared with the single-occupancy vehicle scheduling methods, the operating costs of multi-occupancy vehicle scheduling method can be reduced by up to 25.0%, and the average passenger in-vehicle time is decreased by up to 8.8%, which could significantly reduce the system operating costs on the premise of ensuring shorter total passenger travel time. Compared with the mode of fixed stops, the average full load ratio of mode with non-fixed stops increased by 21.7%. Besides, the convergence speed and solving speed of the proposed improved genetic algorithm are increased by 31.7% and 4.8%, compared with the traditional genetic algorithm.

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Transit network design and scheduling using genetic algorithm – a review

Cited by 10 publications

References 25 publications

An Activity-Based Travel Personalization Tool Driven by the Genetic Algorithm

An Activity-Based Travel Personalization Tool Driven by the Genetic Algorithm

Pengembangan Algoritma Genetika dengan Pendekatan Repetitive Random untuk Penjadwalan Ujian Pendadaran Proyek Tugas Akhir

An Optimization Model for the Demand-Responsive Transit With Non-Fixed Stops and Multi-Vehicle Type

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