A Method for Generating the Timetable of Double-Track Railway Line

2013 ◽  
Vol 347-350 ◽  
pp. 2501-2505
Author(s):  
Yan Zhang ◽  
Yan Ping Cui ◽  
Wen Tao Yang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Scheduling Problem ◽  
Freight Train ◽  
Train Scheduling ◽  
Railway Line ◽  
Run Time ◽  
Double Track

Passenger and freight train scheduling problem on double-track railway line is considered by using Ant Colony Optimization (ACO) algorithm. The aim is to reasonably arrange the dispatch sequence of the trains to minimize the total run time. The constrains in train scheduling problem are considered and the model is established. Due to the complexity of train scheduling problem, this problem is solved by ACO and implemented by programming. A case study is presented to illustrate the solution. The results illustrate that the proposed method is effective to solve the scheduling problem on double-track railway line.

scholarly journals ACS-TS: train scheduling using ant colony system

2006 ◽  
Vol 2006 ◽  
pp. 1-28 ◽  
Author(s):  
Keivan Ghoseiri ◽  
Fahimeh Morshedsolouk
Keyword(s):  
Mathematical Model ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony System ◽  
Scheduling Problem ◽  
Train Scheduling ◽  
Numerical Examples ◽  
Accuracy Comparison ◽  
Time Savings

This paper develops an algorithm for the train scheduling problem using the ant colony system metaheuristic called ACS-TS. At first, a mathematical model for a kind of train scheduling problem is developed and then the algorithm based on ACS is presented to solve the problem. The problem is considered as a traveling salesman problem (TSP) wherein cities represent the trains. ACS determines the sequence of trains dispatched on the graph of the TSP. Using the sequences obtained and removing the collisions incurred, train scheduling is determined. Numerical examples in small and medium sizes are solved using ACS-TS and compared to exact optimum solutions to check for quality and accuracy. Comparison of the solutions shows that ACS-TS results in good quality and time savings. A case study is presented to illustrate the solution.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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