A novel multi-objective optimization approach to guarantee quality of service and energy efficiency in a heterogeneous bus fleet system

References

• Alba, E., and B. Dorronsoro. 2008. Cellular Genetic Algorithms. Operations Research/Computer Science Interfaces Series (Vol. 42). New York: Springer.
   Google Scholar
• APTA. 2020. 2020 Public Transportation Fact Book. American Public Transportation Association. https://www.apta.com/wp-content/uploads/APTA-2020-Fact-Book.pdf.
   Google Scholar
• Ceder, A. 2007. Public Transit Planning and Operation: Theory, Modeling and Practice. 1st ed. Oxford: Elsevier.
   Google Scholar
• Ceder, A. 2016. Public Transit Planning and Operation: Modeling, Practice and Behavior. 2nd ed. Boca Raton, FL: CRC Press.
   Google Scholar
• Deb, K. 2001. Multi-objective Optimization Using Evolutionary Algorithms. New York: John Wiley and Sons.
   Google Scholar
• Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.” IEEE Transactions on Evolutionary Computation 6 (2): 182–197.
   Web of Science ®Google Scholar
• Dorronsoro, B. 2014. Evolutionary Algorithms for Mobile Ad Hoc Networks. Hoboken, NJ: John Wiley and Sons.
   Google Scholar
• EPA. 2020. Inventory of US Greenhouse Gas Emissions and Sinks. US Environmental Protection Agency.
   Google Scholar
• Frey, H. Christopher, Nagui M. Rouphail, Haibo Zhai, Tiago L. Farias, and Gonçalo A. Gonçalves. 2007. “Comparing Real-World Fuel Consumption for Diesel- and Hydrogen-Fueled Transit Buses and Implication for Emissions.” Transportation Research Part D: Transport and Environment 12 (4): 281–291.
   Web of Science ®Google Scholar
• García, S., A. Fernández, J. Luengo, and F. Herrera. 2009. “A Study of Statistical Techniques and Performance Measures for Genetics-Based Machine Learning: Accuracy and Interpretability.” Soft Computing 13 (10): 959–977.
   Web of Science ®Google Scholar
• Griswold, J. B., Tal Sztainer, Jinwoo Lee, Samer Madanat, and Arpad Horvath. 2017. “Optimizing Urban Bus Transit Network Design Can Lead to Greenhouse Gas Emissions Reduction.” Frontiers in Built Environment 3: 1–7.
   Google Scholar
• Hao, H., Yong Geng, Hewu Wang, and Minggao Ouyang. 2014. “Regional Disparity of Urban Passenger Transport Associated GHG (Greenhouse gas) Emissions in China: A Review.” Energy 68: 783–793.
   Web of Science ®Google Scholar
• Hassold, S., and A. A. Ceder. 2014. “Public Transport Vehicle Scheduling Featuring Multiple Vehicle Types.” Transportation Research Part B: Methodological 67: 129–143.
   Web of Science ®Google Scholar
• Jiménez-Palacios, J. L. 1999. “Understanding and Quantifying Motor Vehicle Emissions with Vehicle Specific Power and Tuneable Infrared Laser Differential Absorption Spectrometer Remote Sensing.” PhD thesis, MIT.
   Google Scholar
• Kar, M. B., S. Kar, S. Guo, X. Li, and S. Majumder. 2019. “A New Bi-objective Fuzzy Portfolio Selection Model and its Solution Through Evolutionary Algorithms.” Soft Computing 23: 4367–4381.
   Web of Science ®Google Scholar
• Khoo, H. L., and G. P. Ong. 2015. “Bi-objective Optimization Approach for Exclusive Bus Lane Scheduling Design.” Journal of Computing in Civil Engineering 29 (5): 04014056:1–04014056:16.
   Web of Science ®Google Scholar
• Kwan, C. M., and C. S. Chang. 2008. “Timetable Synchronization of Mass Rapid Transit System Using Multiobjective Evolutionary Approach.” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38 (5): 636–648.
   Web of Science ®Google Scholar
• Luna, F. 2018. “Addressing the 5G Cell Switch-off Problem with a Multi-objective Cellular Genetic Algorithm.” 2018 IEEE 5G world forum, 422–426.
   Google Scholar
• Mercier, Q., F. Poirion, and J. A. Désidéri. 2019. “Non-convex Multiobjective Optimization Under Uncertainty: A Descent Algorithm. Application to Sandwich Plate Design and Reliability.” Engineering Optimization 51 (5): 733–752.
   Web of Science ®Google Scholar
• Morales, C., and I. Gaspar. 2016. El 80% del tiempo que un autobús está parado es por culpa de los semáforos. https://www.atuc.es/noticias.
   Google Scholar
• Nebro, A. J., J. J. Durillo, F. Luna, B. Dorronsoro, and E. Alba. 2009. “MOCell: A Cellular Genetic Algorithm for Multiobjective Optimization.” International Journal of Intelligent Systems 24 (7): 726–746.
   Web of Science ®Google Scholar
• Newell, G. F. 1971. “Dispatching Policies for a Transportation Route.” Transportation Science 5 (1): 91–105.
   Google Scholar
• Oskarbski, J. 2015. “Estimating the Average Speed of Public Transport Vehicles Based on Traffic Control System Data.” 2015 international conference on models and technologies for intelligent transportation systems, 287–293.
   Google Scholar
• Peña, D. 2017. “Multiobjective Optimization of Greenhouse Gas Emissions Enhancing the Quality of Service for Urban Public Transport Timetabling.” 4th international conference on engineering and telecommunication, 114–118.
   Google Scholar
• Peña, D., Andrei Tchernykh, Sergio Nesmachnow, Renzo Massobrio, Alexander Feoktistov, Igor Bychkov, Gleb Radchenko, Alexander Yu. Drozdov, and Sergey N. Garichev. 2019. “Operating Cost and Quality of Service Optimization for Multi-Vehicle-Type Timetabling for Urban Bus Systems.” Journal of Parallel and Distributed Computing 133: 272–285.
   Web of Science ®Google Scholar
• Potter, S. 2003. “Transport Energy and Emissions: Urban Public Transport.” In Handbook of Transport and the Environment, edited by D. A. Hensher, and K. J. Button, 247–262. Emerald Group Publishing Limited, Bingley.
   Google Scholar
• Rainville, W. S. 1982. Bus Scheduling Manual: Traffic Checking and Schedule Preparation. UMTA in cooperation with APTA, DOT-1-8223 (August 1947, reprinted July 1982).
   Google Scholar
• Ribau, J. P., S. M. Vieira, and C. M. Silva. 2018. “Selecting Sustainable Electric Bus Powertrains Using Multipreference Evolutionary Algorithms.” International Journal of Sustainable Transportation 12 (8): 592–612.
   Web of Science ®Google Scholar
• Riquelme, N., C. Von Lucken, and B. Baran. 2015. “Performance Metrics in Multi-Objective Optimization.” 2015 Latin American computing conference (CLEI), 1–11.
   Google Scholar
• Shapiro, S. S., and M. B. Wilk. 1965. “An Analysis of Variance Test for Normality (Complete Samples).” Biometrika 52 (3/4): 591–611.
   Web of Science ®Google Scholar
• Sheskin, D. J. 2003. Handbook of Parametric and Nonparametric Statistical Procedures. 3rd ed. New York: Chapman and Hall.
   Google Scholar
• Talasliuoglu, T. 2021. “A Comparative Study of Multi-objective Evolutionary Metaheuristics for Lattice Girder Design Optimization.” Structural Engineering and Mechanics 77 (3): 417–439.
   Web of Science ®Google Scholar
• Tang, J., Yifan Yang, Wei Hao, Fang Liu, and Yinhai Wang. 2021. “A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-objective Genetic Algorithm.” IEEE Transactions on Intelligent Transportation Systems 22 (4): 2417–2429.
   Web of Science ®Google Scholar
• UITP. 2019. Global Bus Survey Report of International Association of Public Transport. https://www.uitp.org/sites/default/files/cck-focus-papers-files/Statistics Brief_Global bus survey %28003%29.pdf.
   Google Scholar
• Wang, Z., Yang Cai, Yuping Zeng, and Jie Yu. 2019. “Multi-objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain.” Applied Sciences 9 (19): 4068:1–4068:21.
   Google Scholar
• Wang, J., and H. A. Rakha. 2017. “Convex Fuel Consumption Model for Diesel and Hybrid Buses.” Transportation Research Record: Journal of the Transportation Research Board 2647 (1): 50–60.
   Web of Science ®Google Scholar
• Wen, T., Haotian Liu, Luxin Lin, Bin Wang, Jigong Hou, Chuanbo Huang, Ting Pan, and Yu Du. 2020. “Multiswarm Artificial Bee Colony Algorithm Based on Spark Cloud Computing Platform for Medical Image Registration.” Computer Methods and Programs in Biomedicine 192: 105432:1–105432:10.
   Web of Science ®Google Scholar
• Wren, A. 2004. Scheduling Vehicles and Their Drivers (Issue April). Leeds, UK: School of Computing, University of Leeds.
   Google Scholar
• Wu, Y., H. Yang, J. Tang, and Y. Yu. 2016. “Multi-objective Re-synchronizing of Bus Timetable: Model, Complexity and Solution.” Transportation Research Part C: Emerging Technologies 67: 149–168.
   Web of Science ®Google Scholar
• Yang, X., and L. Liu. 2020. “A Multi-objective Bus Rapid Transit Energy Saving Dispatching Optimization Considering Multiple Types of Vehicles.” IEEE Access 8: 79459–79471.
   Web of Science ®Google Scholar
• Yu, Q., and T. Li. 2014. “Evaluation of Bus Emissions Generated Near Bus Stops.” Atmospheric Environment 85: 195–203.
   Web of Science ®Google Scholar
• Yu, Q., T. Li, and H. Li. 2016. “Improving Urban Bus Emission and Fuel Consumption Modelling by Incorporating Passenger Load Factor for Real World Driving.” Applied Energy 161: 101–111.
   Web of Science ®Google Scholar
• Zitzler, E., K. Deb, and L. Thiele. 2000. “Comparison of Multiobjective Evolutionary Algorithms: Empirical Results.” Evolutionary Computation 8 (2): 173–195.
   PubMed Web of Science ®Google Scholar
• Zitzler, E., and L. Thiele. 1999. “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach.” IEEE Transactions on Evolutionary Computation 3 (4): 257–271.
   Web of Science ®Google Scholar
• Zitzler, E., L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. da Fonseca. 2003. “Performance Assessment of Multiobjective Optimizers: An Analysis and Review.” IEEE Transactions on Evolutionary Computation 7 (2): 117–132.
   Web of Science ®Google Scholar