A New Idea For Train Scheduling Using
Ant Colony Optimization
Author(s)
K. Ghoseiri
Abstract
This paper develops a new algorithm to the train scheduling problem using Ant
Colony System (ACS) meta-heuristic. At first, a mathematical model for a kind
of train scheduling problem is developed and then the algorithm based on the
meta-heuristic is presented to solve the problem. The problem is considered as a
traveling salesman problem (TSP) wherein cities represent the trains. ACS
determines the sequence of trains dispatched on the graph of the TSP. Based on
the sequence obtained and removing for collisions incurred, train scheduling is
determined. Numerical examples in small and medium sizes are solved by using
the algorithm. The solutions are compared to the exact optimum solutions to
check for quality and accuracy. Comparison of the solutions shows that the
algorithm results in good enough quality and time savings. A case study is
presented to illustrate the solution.
Keywords: meta-heuristic, ant colony optimization, Ant Colony System, train
scheduling problem, traveling salesman problem.
1 Introduction
Rail transport planning is a very complex task which is carried out based on the
mutual reaction among a large number of impressed components. As it was
mentioned in Ghoseiri et al [5] and Lindner [10], in respect of complexity of rail
transport planning, this process is divided into several steps. These procedures
include the demand analysis, line planning, train scheduling, rolling stock
planning and crew management [5]. These problems are NP-hard problems and
solving them with exact algorithms is very difficult and time consuming.
Keywords
meta-heuristic, ant colony optimization, Ant Colony System, train
scheduling problem, traveling salesman problem.
Related Book
Other papers in this volume
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