Scheduling Extra Train Paths Into Cyclic Timetable Based on the Genetic Algorithm

Abstract

In order to support the process of scheduling a hybrid cyclic timetable, this paper is devoted to inserting additional non-cyclic train paths into existing cyclic timetable. The adding train paths problem is an integration of timetable scheduling and rescheduling problem. The train dispatcher can not only modify the given timetable to manage the interruptions in existing operations, but also establish schedules for additional trains. A multi-objective model minimizing both the travel time of additional trains and the variation of existing trains is proposed in this paper. In addition, we consider both general constraints and some additional practical constraints, such as the overtaking priority constraint, reasonable adjustment of initial schedules and the scheduled connections. This problem is very difficult and must be solved in practice. A heuristic algorithm is introduced to find high-quality solutions for large-scale cases within reasonable computing time. Based on high-speed railway line in China, the case studies illustrate the methodology and compare the performance of trains. Numerical experiments indicate that the proposed solution approach to the adding train paths problem is promising. INDEX TERMS High-speed railway, cyclic timetable, adding additional train paths, genetic algorithm, mixed integer programming.

Urban Train Timetable Optimization Based on Multi-Objective Optimization

“…Genetic algorithm is a random search optimization algorithm based on evolution theory, biological selection theory, and population genetics theory [10] . It represents a computational model that imitates the natural evolution process to establish and solve problem extremum.…”

Automatic Timetable Generation

“…[3] A review of the scheduling system at our institute revealed the need for a feasible lecture/tutorial timetable for a department, which has been a continuous challenge in educational establishments. [5] The N Queen algorithm-based approach proved to be a useful solution; however, certain issues still need to be addressed. [7] By reviewing existing literature and proposing innovative solutions to existing problems, this work contributes to the advancement of knowledge in the field of educational scheduling.…”

Genetic Algorithm with Machine Learning to Estimate the Optimal Objective Function Values of Subproblems

“…It is evaluated to be good if the determination of x 2 results in generating a good solution. For generating the good solution, the value of x 2 is determined by applying metaheuristics or greedy algorithms [2][3][4][5][6][7]. However, the metaheuristics are timeconsuming, and the greedy algorithms are not general-purpose.…”

“…When a GA is applied to an optimization problem, especially to one with two decision variable vectors, the following strategy is often adopted [2][3][4][5][6][7]. An individual of GA is expressed by the values of one of the decision variable vectors.…”