Ant Colony Optimization for Simulated Dynamic Multi-Objective Railway Junction Rescheduling

Abstract

Minimising the ongoing impact of train delays has benefits to both the users of the railway system and the railway stakeholders. However, the efficient rescheduling of trains after a perturbation is a complex real-world problem. The complexity is compounded by the fact that the problem may be both dynamic and multi-objective. The aim of this research is to investigate the ability of ant colony optimisation algorithms to solve a simulated dynamic multi-objective railway rescheduling problem and, in the process, to attempt to identify the features of the algorithms that enable them to cope with a multi-objective problem that is also dynamic. Results showed that, when the changes in the problem are large and frequent, retaining the archive of non-dominated solution between changes and updating the pheromones to reflect the new environment play an important role in enabling the algorithms to perform well on this dynamic multi-objective railway rescheduling problem.

A Disruption Recovery Model for Time-Dependent Vehicle Routing Problem With Time Windows in Delivering Perishable Goods

“…Suitable performances of the adapted ant colony optimization (ACO) algorithm have been achieved on disruption management models [38], [39] and SDVRP [40], which are closest to our study. Hence, the ACO algorithm is fit for solving our problem with certain modifications.…”

A review of modelling and optimisation methods applied to railways energy consumption

“…They consist of simple individuals who interact locally with one another as well as with their environment. Noteworthy examples belonging to this family are Ant Colony (Eaton et al, 2017) and Particle Swam (Domínguez et al, 2014).  Teaching Learning Based Optimisation: Another population-based method, in this case inspired by the influence of a teacher on learners.…”

“…This classification is partially based on (Nguyen et al, 2014). (Shangguan et al, 2015) Ant colony (AC) (Lu et al, 2013) (Zhao et al, 2015) (Eaton et al, 2017) Particle Swarm (PS) (Domínguez et al, 2014) (Yang et al, 2015) (Fernández et al, 2015) Teaching Learning Based Optimisation (TLBO) (Huang et al, 2015)…”

A rolling window with genetic algorithm approach to sorting aircraft for automated taxi routing

“…Metaheuristics have also found application in a variety of other routing and transportation problems including GPS navigation [33] and railway scheduling [15].…”