A goal programming technique for railroad passenger scheduling


Masoud Yaghini, Alireza Alimohammadian and Samaneh Sharifi


Railroad industry has received tremendous challenges in the world in terms of handling cost and efficiency. For many years, the railroad business lost money in many countries such as Japan until many governments decided to privatize the industry in an attempt to reduce the cost components and to increase the efficiency of various units, significantly. In this paper, we propose a new goal programming technique to handle two objectives of operating cost and the number of direct passengers travel by train. We consider different types of trains for public transportation of passengers in order to make the proposed model of this paper more realistic. The implementation of the proposed model is demonstrated using some numerical examples to show the effectiveness of the method.


DOI: j.msl.2011.12.013

Keywords: Goal programming ,Mixed Integer Programming ,Railroad planning ,Passenger scheduling

How to cite this paper:

Yaghini, M., Alimohammadian, A & Sharifi, S. (2012). A goal programming technique for railroad passenger scheduling.Management Science Letters, 2(2), 535-542.


References

Barnhart, C. & Laporte, G. (2007). Handbook in OR & MS. Vol. 14, Elsevier B.V.

Bussieck, M. R., Kreuzer, P., & Zimmermann, U. T. (1996). Optimal lines for railway systems. European Journal of Operational Research, 96, 54–63.

Brønmo, G., Nygreen, B., & Lysgaard, J. (2010). Column generation approaches to ship scheduling with flexible cargo sizes. European Journal of Operational Research, 200(1), 139-150.

Burdett, R. L., & Kozan, E. (2009). Techniques for inserting additional trains into existing timetables. Transportation Research, Methodological, 43(8-9), 821-836.

Claessens, M. T., van Dijk, N. M., & Zwaneveld, P. J. (1998). Cost optimal allocation of passenger lines. European Journal of Operational Research, 110, 474–489.

Chung, J., Moo, S., & Choi, I. (2009). A hybrid genetic algorithm for train sequencing in the Korean railway. Omega, 37(3), 55-65.

D’Ariano, A., Pacciarelli, D., & Pranzo, M. (2007). A branch and bound algorithm for scheduling trains in a railway network. European Journal of Operational Research, 183(2), 643-657.

Ghoseiri, K., & Ghannadpour, F. (2010). A hybrid genetic algorithm for multi-depot homogenous locomotive assignment with time windows. Applied Soft Computing, 10(1), 53-65.

Goossens, J. W., Hoesel, S. V., & Kroon, L. (2006). On solving multi-type railway line planning problem. European Journal of Operational Research, 168(2), 403-424.

Goossens, J. H. M., van Hoesel, C. P. M., & Kroon, L. G. (2004). A branch-and-cut approach for solving railway line-planning problems. Transportation Science, 38, 379–393.

Kroeger, A. (2005). Retiring the Crow Rate: A Narrative of Political Management. University of Alberta Press, 280.

Lindner, T. ( 2000). Train schedule optimization in public rail transport. PhD thesis, TU Braunschweig.

Liu, S.Q., & Kozan, E. (2009). Scheduling trains as a blocking parallel-machine job shop scheduling problem. Computers & Operations Research, 36(10), 2840-2852.

Lee, Y., & Chen, C. (2009). A heuristic for the train pathing and timetabling problem. Transportation Research, Methodological, 43(8-9), 837-851.

Mesquita, M., & Paias, A. (2008). Set partitioning/covering-based approaches for the integrated vehicle and crew scheduling problem. Computers & Operations Research, 35(5) 1562-1575.

Peeters, M., & Kroon, L. (2008). Circulation of railway rolling stock: a branch-and-price approach. Computers & Operations Research, 35(2), 538-556.

Scholl, S. (2005). Customer-oriented line planning. PhD thesis, University of Kaiserslautern.

Suryani, E., Chou, S., & Chen, C. H. (2010). Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework. Expert Systems with Applications, 37(3), 2324-2339.

Tsai, T. H., Lee, C. K., & Wei, C. H. (2009). Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Systems with Applications, 36(2), 3728-3736.