Integrated Ant Colony Optimization and Mixed Integer Linear Programming for Multi-objective Railway Timetabling.

Nicola Coviello,Giorgio Medeossi,Thomas Nygreen,Paola Pellegrini,Joaquin Rodriguez

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)（2023）

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摘要

This paper presents an algorithmic framework for automatic railway timetabling, developed within the project “Tools for mathematical optimization of strategic railway timetable models” funded by the Norwegian Railway Directorate (Jernbanedirektoratet). It describes the algorithmic core of the developed tool, called Automatic Timetabler with Multiple Objectives. The framework integrates a Multi-Objective Ant Colony Optimization (MOACO) algorithm and a Mixed Integer Linear Programming (MILP) formulation. MOACO performs a fast-but-coarse exploration of the solution space, populating and maintaining an approximated Pareto optimal set of timetables. The timetables generated by MOACO are refined by the MILP formulation, exploring a neighborhood of the input solution and returning feasible, high-quality timetables. The tool is assessed on case studies driven from real practice in Norway.

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关键词

Mixed Integer Linear Programming,Ant Colony Optimization,Optimization Algorithm,Space Exploration,Ant Colony Optimization Algorithm,Pareto Optimal Set,Energy Consumption,Objective Function,Running Time,Training Time,Local Search,Travel Time,Arrival Time,Training Group,Stopping Rule,Number Of Training,Training Schedule,Technical Constraints,Departure Time,Training Pairs,Pheromone Trails,Heuristic Information,Service Concept,Test Instances,Rolling Stock,Scheduled Group,Lexicographic,Soft Constraints,Objective Function Value,Traffic Patterns

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