Bus Fleet Management Optimization Using the Augmented Weighted Tchebycheff Method

This paper presents a multi-objective optimization model for the buses fleet management problem. This model is solved using the Augmented Weighted Tchebycheff method. The aim is to minimize three objective functions, \(Z_1\) (CO\(_2\) emissions), \(Z_2\) (other types of emissions) and \(Z_3\) (total costs), for a bus fleet that uses four types of buses: diesel, electric bus, electric bus of fast charging, and Compressed Natural Gas (CNG). A public transport (PT) company of Joinville, Brazil, where it operates three different PT lines, owns the fleet. The respective data was modelled and optimized using the MS Excel solver. Results provided interesting insights concerning the most adequate strategy for bus selection according with public transport line characteristics and taking into account trade-off between costs and emissions. The results indicate that optimal solutions include the diesel in the Itinga line and the CNG in the South line. The electric bus is more adequate in the South-North line due to the large number of stops and low average speed. However, when the costs are disregarded, in some scenarios, the best option is the electric bus for all lines.

[1]  Robert Prohaska,et al.  Foothill Transit Battery Electric Bus Demonstration Results , 2016 .

[2]  Sadiq M. Sait,et al.  Multi-objective optimal path selection in electric vehicles , 2012, Artificial Life and Robotics.

[3]  K. P. Nurjanni,et al.  Green supply chain design: a mathematical modeling approach based on a multi-objective optimization model , 2017 .

[4]  Wei Feng,et al.  Vehicle technologies and bus fleet replacement optimization: problem properties and sensitivity analysis utilizing real-world data , 2014, Public Transp..

[5]  Snehamay Khasnabis,et al.  Multiobjective Optimization Model for Transit Fleet Resource Allocation , 2013 .

[6]  Zeng Qiang,et al.  Optimization Method for Train Plan of Urban Rail Transit with Elastic Demands , 2012 .

[7]  Joao M. C. Sousa,et al.  Reducing the carbon footprint of urban bus fleets using multi-objective optimization , 2015 .

[8]  Ching-Lai Hwang,et al.  Multiple Objective Decision Making , 1994 .

[9]  Sheldon S. Williamson,et al.  Optimal drivetrain component sizing for a Plug-in Hybrid Electric transit bus using Multi-Objective Genetic Algorithm , 2010, 2010 IEEE Electrical Power & Energy Conference.

[10]  Miguel A. Figliozzi,et al.  An economic and technological analysis of the key factors affecting the competitiveness of electric commercial vehicles: A case study from the USA market , 2013 .

[11]  Uk-Don Choi,et al.  Commercial operation of ultra low floor electric bus for Seoul city route , 2012, 2012 IEEE Vehicle Power and Propulsion Conference.

[12]  Omer Tatari,et al.  Optimization of transit bus fleet's life cycle assessment impacts with alternative fuel options , 2015 .

[13]  Kathrin Klamroth,et al.  An augmented weighted Tchebycheff method with adaptively chosen parameters for discrete bicriteria optimization problems , 2012, Comput. Oper. Res..

[14]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[15]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.