Dynamic railway junction rescheduling using population based ant colony optimisation

Eaton, Jayne; Yang, Shengxiang

doi:10.1109/ukci.2014.6930174

Cited by 6 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To investigate the effectiveness of the algorithms under consideration, a train simulator was developed to allow the trains in each ant's solution to be evaluated. More details about the simulator can be found in Eaton and Yang (2014). Table 2 shows the trains used, their routes through the junction, the penalty for delay and their scheduled arrival times.…”

Section: The Stenson Junction Train Simulatormentioning

confidence: 99%

“…In the original investigation (Eaton and Yang 2014), the problem was decomposed into a fully connected, partly one-directional, weighted graph (see Fig. 4) based on that designed by Van Der Zwaan and Marques (1999) for the jobshop scheduling problem.…”

Section: Framework Of the Proposed Algorithmsmentioning

confidence: 99%

“…4 The problem before a change modelled by a fully connected, partly one-directional, weighted graph (Eaton and Yang 2014) Fig. 5 The problem after a change modelled by a fully connected, partly one-directional, weighted graph (Eaton and Yang 2014) have been selected, the ant's tour is complete and the ant's solution is evaluated by running it through the simulator. In the case of the P-ACO algorithm, the best ant of the iteration is stored in memory and the pheromone is positively updated for that ant's tour, while in the case of the MMAS algorithm, the best ant is used directly to update the pheromone matrix.…”

Section: Framework Of the Proposed Algorithmsmentioning

confidence: 99%

“…Further constraints of the problem are that trains are not allowed to enter a block section occupied by another train and that trains are not allowed to cross the path of other trains entering or leaving the junction. Both these constraints are dealt with in the simulator (Eaton and Yang 2014). In the first case, a train can only enter the next track section if it is clear of all other trains, and in the second case a simulated interlocking system incorporated in the simulator prevents trains from crossing the path of other trains.…”

Section: Handling Constraintsmentioning

confidence: 99%

“…The aim of this work is to extend the dynamic junction simulator for the dynamic railway junction rescheduling problem (DRJRP), introduced in Eaton and Yang (2014), to include multiple delays taking place over the period of the simulation. As ant colony optimization (ACO) gave promising results in the original single delay simulator, it is proposed to investigate its application in the new multiple delay simulator.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

2015

Self Cite

View full text Add to dashboard Cite

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it Communicated by D. Neagu.

show abstract

Section: The Stenson Junction Train Simulatormentioning

confidence: 99%

Section: Framework Of the Proposed Algorithmsmentioning

confidence: 99%

Section: Framework Of the Proposed Algorithmsmentioning

confidence: 99%

Section: Handling Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

2015

Self Cite

View full text Add to dashboard Cite

show abstract

Meta-Heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing

Younis

Yang

Passow

2017

Applications of Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

Grid computing is an infrastructure which connects geographically distributed computers owned by various organizations allowing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling problem is one of the most difficult tasks in grid computing systems. To solve this problem efficiently, new methods are required. In this paper, a seeded genetic algorithm is proposed which uses a meta-heuristic algorithm to generate its initial population. To evaluate the performance of the proposed method in terms of minimizing the makespan, the Expected Time to Compute (ETC) simulation model is used to carry out a number of experiments. The results show that the proposed algorithm performs better than other selected techniques.

show abstract

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Botte

D’Acierno

2018

Urban Rail Transit

View full text Add to dashboard Cite

The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown.

show abstract

Dynamic railway junction rescheduling using population based ant colony optimisation

Cited by 6 publications

References 17 publications

Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Meta-Heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Contact Info

Product

Resources

About