An implicit enumeration algorithm for the passenger service planning problem: Application to the Taiwan Railways Administration line

In a passenger railroad system, the service planning problem determines the train stopping strategy, taking into consideration multiple train classes and customer origin–destination (OD) demand, to maximize the short-term operational profit of a rail company or the satisfaction levels of the passengers. The service plan is traditionally decided by rule of thumb, an approach that leaves much room for improvement. To systematically analyze this problem, we propose an integer program approach to determine the optimal service plan for a rail company. The formulated problem has a complex solution space, and commonly used commercial optimization packages are currently incapable of solving this problem efficiently, especially when problems of realistic sizes are considered. Therefore, we develop an implicit enumeration algorithm that incorporates intelligent branching and effective bounding strategies so that the solution space of this integer program can be explored efficiently. The numerical results show that the proposed implicit enumeration algorithm can solve real-world problems and can obtain service plans that are at least as good as those developed by the rail company.

[1]  Yusin Lee,et al.  A heuristic for the train pathing and timetabling problem , 2009 .

[2]  Marshall L. Fisher,et al.  The Lagrangian Relaxation Method for Solving Integer Programming Problems , 2004, Manag. Sci..

[3]  Lorenzo Mussone,et al.  An analytical approach to calculate the capacity of a railway system , 2013, Eur. J. Oper. Res..

[4]  Arjang A. Assad,et al.  Modelling of rail networks: Toward a routing/makeup model , 1980 .

[5]  Brian Borchers,et al.  Using an interior point method in a branch and bound algorithm for integer programming July , 2007 .

[6]  Chung-Hsing Yeh,et al.  A Multiobjective Model for Passenger Train Services Planning: Application to Taiwan's High-Speed Rail Line , 2000 .

[7]  Laurence A. Wolsey,et al.  Integer and Combinatorial Optimization , 1988 .

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Jyh-Cherng Jong,et al.  Decision Support System to Optimize Railway Stopping Patterns , 2012 .

[10]  Anita Schöbel,et al.  Line planning in public transportation: models and methods , 2012, OR Spectr..

[11]  Gilbert Laporte,et al.  Double-horizon based heuristics for the dynamic pickup and delivery problem with time windows , 2004 .

[12]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

[13]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[14]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[15]  Paolo Toth,et al.  Nominal and robust train timetabling problems , 2012, Eur. J. Oper. Res..

[16]  Young-Jae Lee Mathematical Modeling for Optimizing Skip-Stop Rail Transit Operation Strategy Using Genetic Algorithm , 2012 .

[17]  Dung-Ying Lin,et al.  Using Genetic Algorithms to Optimize Stopping Patterns for Passenger Rail Transportation , 2014, Comput. Aided Civ. Infrastructure Eng..

[18]  Dennis Huisman,et al.  Delay Management with Rerouting of Passengers , 2012, Transp. Sci..