A Study on Optimize Skip Stop Service Using Genetic Algorithm in Selected Indian Railway Division

Authors

  • Sengupta S Department of Computer Science and Applications Panskura Banamali College, Vidyasagar University, INDIA
  • Neogi A Department of Computer Science and Engineering Dr B.C. Roy Engineering College, West Bengal University of Technology, INDIA

Keywords:

Genetic Algorithm, Indian Railway, SkipStop Service, Optimization

Abstract

Railway authorities aim to provide transportation service to the customer in a safe as well as effective and efficient manner. The main constraints forcing them to regulate their service are the limitation of resources. This paper found the optimal coordination of stopping stations that can increase and improve overall benefits of skip stop service. A skip stop pattern must find an optimal balance between faster passenger travel time and lower service frequencies at each station. The main objective is to optimize passenger travel time with maintaining railway and infrastructural behavior.

References

Assad, A.A., 1980. Models for rail transportation. Transportation Research Part A: General 14 (3), 205–220.

Cordeau, J.F., Toth, P., Vigo, D., 1998. A survey of optimization models for routing and scheduling. Transportation Science 32 (4), 380– 404.

Fay, A., 2000. A fuzzy knowledge-based s ystem for railway traffic control. Engineering Applications of Artificial Intelligence 13 (6), 719–729.

Peeters L, Kroon L (2000) A cycle based optimization model for the cyclic railway timetabling problem. In: Voss S, Daduna J (eds) Computer-Aided Scheduling of Public Transport, Springer, Berlin.

Li, F., Z. Gao, K. Li, and L. Yang. 2008. “ Efficient Scheduling of Railway Traffic Based on Global Information of Train.” Transportation Research Part B 42:1008–1030.

J.-C. Jong, C.-S. Suen and S. J. Chang, "A Decision Support System to Optimize Railway Stopping Patterns: Application To The Taiwan High Speed Rail," in Transportation Research Board 91st Annual Meeting, Washington D.C., 2011.

Corman, F., D’Ariano, A., Pacciarelli, D., Pranzo, M., 2010. Centralized versus distributed systems to reschedule trains in two dispatching areas. Public Transport: Planning and Operations 2 (3), 219–247.

Acuna-Agost, R., Michelon, P., Feillet, D., Gueye, S., 2011. SAPI: Statistical Analysis of Propagation of Incidents. A new approach for rescheduling trains after disruptions. European Journal of Operational Research 215 (1), 227– 243.

Tnquist Krasemann, J., 2012. Design of an effective algorithm for fast response to the re- scheduling of railway traffic during disturbances. Transportation Research Part C 20 (1), 62–78.

Downloads

Published

2025-11-10

How to Cite

[1]
S. Sengupta and A. Neogi, “A Study on Optimize Skip Stop Service Using Genetic Algorithm in Selected Indian Railway Division”, Int. J. Comp. Sci. Eng., vol. 3, no. 7, pp. 124–128, Nov. 2025.

Issue

Vol. 3 No. 7 (2015): IJCSE July Edition

Section

Study Paper

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.