Development of a hybrid fuzzy genetic algorithm model for solving transportation scheduling problem

H.C.W Lau, Dilupa Nakandala, Li Zhao

Resumo


There has been an increasing public demand for passenger rail service in the recent times leading to a strong focus on the need for effective and efficient use of resources and managing the increasing passenger requirements, service reliability and variability by the railway management. Whilst shortening the passengers’ waiting and travelling time is important for commuter satisfaction, lowering operational costs is equally important for railway management. Hence, effective and cost optimised train scheduling based on the dynamic passenger demand is one of the main issues for passenger railway management. Although the passenger railway scheduling problem has received attention in operations research in recent years, there is limited literature investigating the adoption of practical approaches that capitalize on the merits of mathematical modeling and search algorithms for effective cost optimization. This paper develops a hybrid fuzzy logic based genetic algorithm model to solve the multi-objective passenger railway scheduling problem aiming to optimize total operational costs at a satisfactory level of customer service. This hybrid approach integrates genetic algorithm with the fuzzy logic approach which uses the fuzzy controller to determine the crossover rate and mutation rate in genetic algorithm approach in the optimization process. The numerical study demonstrates the improvement of the proposed hybrid approach, and the fuzzy genetic algorithm has demonstrated its effectiveness to generate better results than standard genetic algorithm and other traditional heuristic approaches, such as simulated annealing.

Palavras-chave


passenger railway scheduling; fuzzy logic approacpassenger railway scheduling; fuzzy logic approach; genetic algorithm; customer service optimization

Texto completo:

PDF (English)

Referências


Caprara, A.(2015). Timetabling and assignment problems in railway planning and integer multicommodity flow. Networks, 66: 1-10.

Chang, YH, Yeh, CH. & Shen, CC. (2000). A multiobjective model for passenger train services planning: application to Taiwain’s high-speed rail line. Transportation Research Part B, 34: 91-106.

Della, Croce, F., Tadei, R. & Volta, G. (1995). A Genetic Algorithm for the Job Shop Problem. Computers & Operations Research, 22: 15-24.

Fay, A. (2000). A fuzzy knowledge-based system for railway traffic control. Engineering application of artificial intelligence, 13: 719-729.

He, S, Song, R. & Chaudhry, SS. (2000). Fuzzy dispatching model and genetic algorithms for railyards operations. European journal of operational research, 124 (2): 307-331.

Huang, ZP. & Niu, H. (2012). Study on the train operation optimization of passenger dedicated lines based on satisfaction. Discrete dynamics in nature and society, 2012: 1-11.

Huisman, D. & Kroon, LG. (2005). Operations research in passenger railway transportation. Statistica Neerlandica, 49(4):467-497.

Last, M., Eyal, S. (2005). A fuzzy-based lifetime extension of genetic algorithms. Fuzzy sets and systems, 149, 131-147.

Lau, H. C. W., Chan, T. M., Tsui, W. T., Ho, G. T. S. & Choy, K. L. (2009). An Ai Approach for Optimizing Multi-Pallet Loading Operations. Expert Systems with Applications, 36, 4296-4312.

Lau, H.C.W. & Dwight, R.A. (2011). A fuzzy-based decision support model for engineering asset condition monitoring – A case study of examination of water pipelines. Expert Systems with Application, 38 (10), 13342-13350.

Maiti, MK (2011). A fuzzy genetic algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon. European journal of operational research, 213: 96-106.

Niu, HM. (2011).Determination of the skip-station scheduling for a congested transit line by bilevel genetic algorithm. International journal of computational intelligence systems, 6(4): 1158-1167.

Peng, ZH, Song, B. (2010). Research on fault diagnosis method for transformer based on fuzzy genetic algorithm and artificial neural network. Kybernetes, 39(8): 1235-1244.

Schindl, D., Zufferey, N. (2015). A learning tabu search for a truck allocation problem with linear and nonlinear cost components. Naval Research Logistics , 62: 32-45.

Taleizadeh, AA, Niaki, STA, Aryanezhad, MB. & Shafii, N. (2013). A hybrid method of fuzzy simulation and genetic algorithm to optimize constrained inventory control systems with stochastic replenishments and fuzzy demand. Information sciences, 220: 425-441.

Vromans, MJCM. & Kroon, LG. (2004). Stochastic optimization of railway timetables, in: Proceedings TRAIL 8th Annual Congress, Delft University Press, Delft, 423– 445.

Zomaya, A.Y. (2001). Natural and simulated annealing. Computing in Science & Engineering, 3, 97-99.




DOI: http://dx.doi.org/10.4301/S1807-17752015000300001