Electric bus fleet scheduling under travel time and energy consumption uncertainty

Name: Electric bus fleet scheduling under travel time and energy consumption uncertainty
Author: Avishan, Farzad, Yanıkoğlu, İhsan, Alwesabi, Y.

İsim	Electric bus fleet scheduling under travel time and energy consumption uncertainty
Yazar	Avishan, Farzad, Yanıkoğlu, İhsan, Alwesabi, Y.
Basım Tarihi:	2023-11
Basım Yeri	- Elsevier
Konu	Battery electric bus, Electric vehicle scheduling, Robust optimization
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0968-090X
Kayıt Numarası	d2cba3ec-1af5-4785-990d-8d1a7f850acd
Lokasyon	Industrial Engineering
Tarih	2023-11
Örnek Metin	The public transportation system is experiencing a substantial shift due to the rapid expansion of electromobility infrastructure and operations. This transformation is anticipated to contribute to decarbonizing and promoting environmental sustainability significantly. Among the most pressing planning issues in this area is the optimization of operational and strategic costs associated with electric fleets, which has recently garnered the attention of researchers. This paper investigates the scheduling and procurement problem of electric fleets under travel time and energy consumption uncertainty. A novel mixed-integer linear programming model is proposed, which determines the number of buses required to cover all trips, yields the schedule of the trips, and creates bus charging plans. The robust optimization paradigm is employed to address uncertainty, and a new budget uncertainty set is introduced to control the robustness of the solution. The efficiency of the model is evaluated through an extensive Monte Carlo simulation. Additionally, a case study is conducted on the off-campus college transport network at Binghamton University to demonstrate the real-world applicability of the model. The numerical results have shown that ignoring uncertainty can lead to schedules where up to 48% of the trips are affected, which are either delayed or missed. The proposed approach can also be applied to other transportation networks with similar characteristics and uncertainties.
DOI	10.1016/j.trc.2023.104357
Cilt	156

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Electric bus fleet scheduling under travel time and energy consumption uncertainty

Yazar Avishan, Farzad, Yanıkoğlu, İhsan, Alwesabi, Y.

Basım Tarihi 2023-11

Basım Yeri - Elsevier

Konu Battery electric bus, Electric vehicle scheduling, Robust optimization

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0968-090X

Kayıt Numarası d2cba3ec-1af5-4785-990d-8d1a7f850acd

Lokasyon Industrial Engineering

Tarih 2023-11

Örnek Metin The public transportation system is experiencing a substantial shift due to the rapid expansion of electromobility infrastructure and operations. This transformation is anticipated to contribute to decarbonizing and promoting environmental sustainability significantly. Among the most pressing planning issues in this area is the optimization of operational and strategic costs associated with electric fleets, which has recently garnered the attention of researchers. This paper investigates the scheduling and procurement problem of electric fleets under travel time and energy consumption uncertainty. A novel mixed-integer linear programming model is proposed, which determines the number of buses required to cover all trips, yields the schedule of the trips, and creates bus charging plans. The robust optimization paradigm is employed to address uncertainty, and a new budget uncertainty set is introduced to control the robustness of the solution. The efficiency of the model is evaluated through an extensive Monte Carlo simulation. Additionally, a case study is conducted on the off-campus college transport network at Binghamton University to demonstrate the real-world applicability of the model. The numerical results have shown that ignoring uncertainty can lead to schedules where up to 48% of the trips are affected, which are either delayed or missed. The proposed approach can also be applied to other transportation networks with similar characteristics and uncertainties.

DOI 10.1016/j.trc.2023.104357

Cilt 156