Bus Timetabling as a Fuzzy Multiobjective Optimization Problem Using Preference-based Genetic Algorithm
AbstractTransportation plays a vital role in the development of a country and the car is the most commonly used means. However, in third world countries long waiting time for public buses is a common problem, especially when people need to switch buses. The problem becomes critical when one considers buses joining different villages and cities. Theoretically this problem can be solved by assigning more buses on the route, which is not possible due to economical problem. Another option is to schedule the buses so that customers who want to switch buses at junction cities need not have to wait long. This paper discusses how to model single frequency routes bus timetabling as a fuzzy multiobjective optimization problem and how to solve it using preference-based genetic algorithm by assigning appropriate fuzzy preference to the need of the customers. The idea will be elaborated with an example.
Yu, B., Yang, Z., & Yao, J. (2010). Genetic Algorithm for Bus Frequency Optimization. Journal of Transportation Engineering, 136 (6), 576-583.
Zhou, L., & Hui, H. (2009). Synthetically Improved Genetic Algorithm in Public Traffic Dispatch System. The 1st international Conference on Information Science and Engineering (ICISE2009), (pp. 5169-5172). Nanjing,China.
Kidwai, F.A., Marwah, B.R., Deb, K., & Karim, M.R. (2005). A Genetic Algorithm Based Bus Scheduling Model for Transit Network. Proceeding of the Eastern Asia Society for Transportation Studies, 5, pp. 477-489. Bangkok, Thailand.
Stefancic, G., Mikulcic, I., & Benakic, M. (1999). Application of Genetic Algorithm in Organising Passenger Transport. Promet Traffic & Transportation: Proceedings TTC-ITSI’99, 11, 75-77.
Cevallos, F., & Zhao, F. (2006). Minimizing Transfer Times in Public Transit Network with Genetic Algorithm. Transportation Research Record: Journal of the Transportation Research Board (1971), 74-79.
Wang, J.-Y., & Lin, C.-M. (2010). Mass Transit Route Network Design Using Genetic Algorithm. Journal of the Chinese Institute of Engineers, 33 (2), 301-315.
Wren, A., & Wren, D.O. (1995). A genetic algorithm for public transport driver scheduling. Computers & Operations Research, 22 (1), 101-110.
Gupta, R., Singh, B., & Pandey, D. (2010). Multi-Objective Fuzzy Vehicle Routing Problem: A Case Study. International Journal of Contemporary Mathematical Sciences, 5 (29), 1439-1454.
Surapholchai, C., Reinelt, G., & Bock, H.G. (2008). Solving City Bus Scheduling Problems in Bangkok by Eligen-Algorithm. Modeling, Simulation and Optimization of Complex Processes: Proceedings of the Third International Conference on High Performance Scientific Computing (pp. 557-564). Hanoi, Vietnam: Springer.
Park, J., & Kim, B.-I. (2009). The School Bus Routing Problem: A review. European Journal of Operational Research, 202, 311-319.
Ren, C., Yin, C., Liu, F., Li, Z., & Wang, H. (2009). The Application of an Improved Hybrid Genetic and Simulated Annealing for Optimization of the Bus Timetable. International Conference on Transportation Engineering (ICTE 2009) (pp. 3918-3923). Chengdu, China: American Society of Civil Engineers.
Yaochu Jin, M. O. (2001). Dynamic Weighted Aggregation for Evolutionary Multi-Objective Optimization: Why Does It Work and How? Genetic and evolutionary computation (GECCO’2001). San Francisco: California: Morgan Kaufmann Publishers.
Yaochu Jin, B. S. (2002). Incorporating of Fuzzy Preferences into Evolutionary Multiobjective Optimization. Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, 1, pp. 26-30. Singapore.
Tilahun, S. L., & Ong, H. C. (2011). Fuzzy Preference Incorporated Evolutionary Algorithm for Multiobjective Optimization. Proceeding of the International Conference on Advanced Science, Engineering and Information Technology (ICASEIT2011), (pp. 26-30). Bangi-Putrajaya, Malaysia.
Ong, H. C. and Tilahun, S. L. (2011). Integerating Fuzzy Preference in Genetic Algorithm to Solve Multiobjective Optimization Problems. Far East Journal of Mathematical Sciences (FJMS), 55, 2, 165 -179.
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