A pareto artificial fish swarm algorithm for solving a multi-objective electric transit network design problem

This study presents a multi-objective optimization model for the urban electric transit network design problem by simultaneously determining the set of transit routes, the service frequency and the location of charging depots with the objectives of minimizing the costs for passengers and operators. The constraints about the bus route, charging depot, vehicle operation and charging schedule are considered to ensure the rational design and operational feasibility of the electric transit network. The solution approach is based on a Pareto artificial fish swarm algorithm (PAFSA) in combination with the crossover and mutation operators. The transit network in an urban region of Beijing is studied in a case study, revealing that the proposed optimization model solved by the PAFSA is able to provide the Pareto optimal solutions to design a relatively large-scaled transit network for the best fits between the passengers and operators.

[1]  Renato Oliveira Arbex,et al.  Efficient transit network design and frequencies setting multi-objective optimization by alternating objective genetic algorithm , 2015 .

[2]  Semida Silveira,et al.  Locating charging infrastructure for electric buses in Stockholm , 2017 .

[3]  Yi Wang,et al.  A Pareto improved artificial fish swarm algorithm for solving a multi-objective fuzzy disassembly line balancing problem , 2017, Expert Syst. Appl..

[4]  Wei-Hong Yang.  An Improved Artificial Fish Swarm Algorithm and Its Application in Multiple Sequence Alignment , 2014 .

[5]  Navid Kalantari,et al.  Bus fleet optimization using genetic algorithm a case study of Mashhad , 2013 .

[6]  Wangtu Xu,et al.  The Memetic algorithm for the optimization of urban transit network , 2015, Expert Syst. Appl..

[7]  Grigorios N. Beligiannis,et al.  Electric Transit Route Network Design Problem: Model and Application , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[8]  Seref Soylu,et al.  Development of PN emission factors for the real world urban driving conditions of a hybrid city bus , 2015 .

[9]  Randy B Machemehl,et al.  Using a Simulated Annealing Algorithm to Solve the Transit Route Network Design Problem , 2006 .

[10]  Md. Khaledur Rahman,et al.  Transit network design by genetic algorithm with elitism , 2014 .

[11]  Manoj Kumar Tiwari,et al.  A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system , 2019, Comput. Ind. Eng..

[12]  Mahmoud Owais,et al.  Complete hierarchical multi-objective genetic algorithm for transit network design problem , 2018, Expert Syst. Appl..

[13]  Ernesto Cipriani,et al.  Transit network design with allocation of green vehicles: A genetic algorithm approach , 2009 .

[14]  Bin Yu,et al.  Transit route network design-maximizing direct and transfer demand density , 2012 .

[15]  Fang Zhao,et al.  Simulated Annealing-Genetic Algorithm for Transit Network Optimization , 2006 .

[16]  Ana Maria A. C. Rocha,et al.  Improved binary artificial fish swarm algorithm for the 0-1 multidimensional knapsack problems , 2014, Swarm Evol. Comput..

[17]  Lixin Miao,et al.  Multistage large-scale charging station planning for electric buses considering transportation network and power grid , 2019, Transportation Research Part C: Emerging Technologies.

[18]  Lay Eng Teoh,et al.  Scenario-based electric bus operation: A case study of Putrajaya, Malaysia , 2017 .

[19]  Yafeng Yin,et al.  A cost-competitiveness analysis of charging infrastructure for electric bus operations , 2018, Transportation Research Part C: Emerging Technologies.

[20]  Christina Iliopoulou,et al.  Integrated transit route network design and infrastructure planning for on-line electric vehicles , 2019 .

[21]  Yongxi Huang,et al.  Optimal recharging scheduling for urban electric buses: A case study in Davis , 2017 .

[22]  James C. Chu.  Mixed-integer programming model and branch-and-price-and-cut algorithm for urban bus network design and timetabling , 2018 .

[23]  D. Goehlich,et al.  Electrification of a city bus network—An optimization model for cost-effective placing of charging infrastructure and battery sizing of fast-charging electric bus systems , 2017 .

[24]  Xuesong Feng,et al.  A new transit network design study in consideration of transfer time composition , 2019, Transportation Research Part D: Transport and Environment.

[25]  Matthew G. Karlaftis,et al.  Sustainable urban transit network design , 2015 .

[26]  Xiaozhou Zhang,et al.  The design of electric vehicle charging network , 2016 .

[27]  Mark Ferguson,et al.  Electric buses: A review of alternative powertrains , 2016 .

[28]  Wenwei Ke,et al.  Real-world performance of battery electric buses and their life-cycle benefits with respect to energy consumption and carbon dioxide emissions , 2016 .

[29]  W. Y. Szeto,et al.  Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach , 2014 .

[30]  Taïcir Loukil,et al.  The Pareto fitness genetic algorithm: Test function study , 2007, Eur. J. Oper. Res..

[31]  Yu Jiang,et al.  Hybrid Artificial Bee Colony Algorithm for Transit Network Design , 2012 .

[32]  Adam Lipowski,et al.  Roulette-wheel selection via stochastic acceptance , 2011, ArXiv.

[33]  He Ni,et al.  Stock index tracking by Pareto efficient genetic algorithm , 2013, Appl. Soft Comput..

[34]  Fang Zhao,et al.  Optimization of User and Operator Cost for Large-Scale Transit Network , 2007 .

[35]  Milos Nikolic,et al.  Transit network design by Bee Colony Optimization , 2013, Expert Syst. Appl..

[36]  Grigorios N. Beligiannis,et al.  Solving the Urban Transit Routing Problem using a particle swarm optimization based algorithm , 2014, Appl. Soft Comput..

[37]  Allan Larsen,et al.  Electric bus fleet size and mix problem with optimization of charging infrastructure , 2018 .

[38]  Ernesto Cipriani,et al.  Transit network design: A procedure and an application to a large urban area , 2012 .