Bus Network Scheduling with Genetic Algorithms and Simulation

This thesis investigates the costs associated with a bus scheduling problem in an urban transit network for both deterministic and stochastic arrival processes and proposes computerized models for each. A simple genetic algorithm (SGA) with some problem-specific genetic operators is developed for the deterministic arrival process and a simulation-based genetic algorithm (SBGA) is developed for the stochastic arrival process. The new models are applied to an artificial bus network to test their efficiency. Several sensitivity analyses and a goodness test are conducted for each arrival process. The results show that the SGA model can find the optimized solution very quickly when it uses problem-specific operators such as the coordinated headway generator, coordinated headway crossover and coordinated headway mutation. They also show that the SBGA model can find a good solution even though it uses general genetic operators.

Author: Park, Seong Jae

Source: University of Maryland

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Contents

Chapter 1: Introduction
1.1 Problem Statement
1.2 Research Objectives
1.3 Scope
1.4 Organization
Chapter 2: Literature Review
2.1 Quantitative studies of bus scheduling problems
2.2 Genetic algorithm applications for transit network problems
2.3 Summary
Chapter 3: Model Formulation
3.1 Deterministic Arrival Process
3.1.1 Analytic Approach
3.1.2 Headway Optimization Model
3.2 Stochastic Arrival Process
3.2.1 Analytic Approach
3.2.2 Slack Time Optimization Model
Chapter 4: Genetic Algorithm
4.1 Initial Population
4.2 Fitness Function
4.3 Reproduction
4.4 Crossover
4.4.1 Simple Crossover
4.4.2 Two-point Crossover
4.4.3 Coordinated Headway Crossover
4.5 Mutation
4.5.1 Uniform Mutation
4.5.2 Coordinated Headway Mutation
4.6 Elitism
Chapter 5: Case Study and Analysis
5.1 Deterministic Case
5.1.1 Numerical Results and Sensitivity Analysis
5.1.2 Goodness Test
5.2 Stochastic Case
5.2.1 Numerical Results and Sensitivity Analysis
5.2.2 Goodness Test
Chapter 6: Conclusions and Recommendations
6.1 Conclusions
6.2 Recommendations for Further Research
References