scholarly journals A Routing Filter for the Real-time Railway Traffic Management Problem Based on Ant Colony Optimization

2015 ◽  
Vol 10 ◽  
pp. 534-543 ◽  
Author(s):  
Marcella Sama‘ ◽  
Paola Pellegrini ◽  
Andrea D’Ariano ◽  
Joaquin Rodriguez ◽  
Dario Pacciarelli
Keyword(s):  
Ant Colony Optimization ◽  
Real Time ◽  
Traffic Management ◽  
Ant Colony ◽  
Management Problem ◽  
The Real ◽  
Railway Traffic

scholarly journals A Conditional Time-Intervals formulation of the real-time Railway Traffic Management Problem

2021 ◽  
Vol 54 (2) ◽  
pp. 187-194
Author(s):  
Grégory Marlière ◽  
Sonia Sobieraj Richard ◽  
Paola Pellegrini ◽  
Joaquin Rodriguez
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Management Problem ◽  
Time Intervals ◽  
The Real ◽  
Railway Traffic ◽  
Conditional Time

scholarly journals RECIFE-MILP: An Effective MILP-Based Heuristic for the Real-Time Railway Traffic Management Problem

2015 ◽  
Vol 16 (5) ◽  
pp. 2609-2619 ◽  
Author(s):  
Paola Pellegrini ◽  
Gregory Marliere ◽  
Raffaele Pesenti ◽  
Joaquin Rodriguez
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Management Problem ◽  
The Real ◽  
Railway Traffic

Ant colony optimization for the real-time train routing selection problem

2016 ◽  
Vol 85 ◽  
pp. 89-108 ◽  
Author(s):  
Marcella Samà ◽  
Paola Pellegrini ◽  
Andrea D’Ariano ◽  
Joaquin Rodriguez ◽  
Dario Pacciarelli
Keyword(s):  
Ant Colony Optimization ◽  
Real Time ◽  
Ant Colony ◽  
Selection Problem ◽  
The Real ◽  
Train Routing

scholarly journals MCTS Based Approach for Solving Real-time Railway Rescheduling Problem

2021 ◽  
Author(s):  
István Ferenc Lövétei ◽  
Bálint Kővári ◽  
Tamás Bécsi
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Critical Factor ◽  
Management Problem ◽  
Traffic Situation ◽  
Railway Company ◽  
Railway Traffic ◽  
Human Decision ◽  
Human Operators ◽  
Abstraction Levels

Solving a real-time Railway Traffic Management Problem (rtRTMP) is a challenging task for human operators. To solve the traffic situation, many factors need to be considered. Traditionally, the most critical factor is the availability of the possible routes and the relative position of the vehicles to each other. Besides, additional constraints can be found, such as the velocity, the length, and railway company regulations. The human decision-making process is essential in case of any disturbance (deviation from the pre-planned timetable). The human operator may solve this situation, but generally, the solution is not optimal. In this paper, the authors present a new method, where they consider an MCTS based algorithm to solve the traffic situation in a fast way in a given station. The performance of the algorithm is examined in two abstraction levels. The main purpose is to execute an experimental study to examine the efficiency of the MCTS based algorithms to solve railway traffic situations.

scholarly journals Distributed optimization for real-time railway traffic management

2018 ◽  
Vol 51 (9) ◽  
pp. 106-111 ◽  
Author(s):  
Xiaojie Luan ◽  
Bart De Schutter ◽  
Ton van den Boom ◽  
Francesco Corman ◽  
Gabriel Lodewijks
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Distributed Optimization ◽  
Railway Traffic

scholarly journals Energy Optimization in Smart Urban Buildings Using Bio-Inspired Ant Colony Optimization

2021 ◽  
Author(s):  
Fakhri Alam Khan ◽  
Kifayat Ullah ◽  
Atta ur Rahman ◽  
Sajid Anwar
Keyword(s):  
Ant Colony Optimization ◽  
Real Time ◽  
Energy Management ◽  
Management System ◽  
Ant Colony ◽  
Consumption Pattern ◽  
Energy Management System ◽  
Electricity Cost ◽  
Home Appliances ◽  
Home Energy Management

Abstract Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the nature of demand of consumer. In return, utility pay incentives to the consumer. The increasing load demand in residential area and irregular electricity load profile have encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. In order to meet the electricity demand of the consumers, the energy consumption pattern of a consumer is maintained through scheduling the appliances in day-ahead and real-time bases. In this paper we propose a hybrid algorithm Bacterial foraging Ant colony optimization is proposed (HB-ACO) which contain both BFA and ACO properties. Primary objectives of scheduling is to shift load from On-peak hour to Off-peak hours to reduce electricity cost and peak to average ratio. A comparison of these algorithms is also presented in terms of performance parameters electricity cost, reduction of PAR and user comfort in term of waiting time. The proposed techniques are evaluated using two pricing scheme time of use and critical peak pricing. The HB-ACO shows better performance as compared to ACO and BFA which is evident from the simulation results Moreover the concept of coordination among home appliances is presented for real time scheduling. We consider this is knapsack problem and solve it through Ant colony optimization algorithm.

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