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.

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.

An Improved Ant Colony System Based on Negative Biased

2010 ◽  
Vol 439-440 ◽  
pp. 558-562
Author(s):  
Jin Qiu Yang ◽  
Jian Gang Yang ◽  
Gen Lang Chen
Keyword(s):  
Convergence Rate ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant System

Ant System (AS) was the first Ant Colony Optimization (ACO) algorithm, which converged too slowly and consumed huge computation. Among the variants of AS, Ant Colony System (ACS) was one of the most successful algorithms. But ACS converged so rapidly that it always was in early stagnation. An improved Ant Colony System based on Negative Biased (NBACS) was introduced in the paper to overcome the early stagnation of the ACS. Experiments for Traveling Salesman Problem (TSP) showed that better solutions were obtained at the same time when the convergence rate accelerated more rapidly.

scholarly journals An ant colony system for solving fuzzy flow shop scheduling problem

2012 ◽  
Vol 1 (2) ◽  
pp. 44 ◽  
Author(s):  
Nasser Shahsavari Pour ◽  
Mohammad hossein Abolhasani Ashkezari ◽  
Hamed Mohammadi Andargoli
Keyword(s):  
Fuzzy Numbers ◽  
Flow Shop ◽  
Ant Colony ◽  
Flow Shop Scheduling ◽  
Process Time ◽  
Ant Colony System ◽  
Scheduling Problem ◽  
Computational Results ◽  
Shop Scheduling ◽  
The Past

During the past years, the flow shop has been regarded by many researchers and some extensive investigations have been done on this respect. Flow Shop includes n works performed on m machines in a same sequence. It is very difficult in the real world to determine the exact process time of an operation on a machine. Therefore, we consider in this article the process time as trapezoidal fuzzy numbers. Our purpose is that we obtain a sequence of works using such fuzzy numbers in order to minimize maximum fuzzy time of completion entire jobs or fuzzy makespan. We offered an optimization algorithm of Ant Colony System (ACS) to solve this problem. Finally, we present computational results for explanation and comparison with other articles in future.

scholarly journals A modified Ant Colony Optimization algorithm to solve a dynamic traveling salesman problem: A case study with drones for wildlife surveillance

2018 ◽  
Vol 6 (3) ◽  
pp. 368-386 ◽  
Author(s):  
Sudipta Chowdhury ◽  
Mohammad Marufuzzaman ◽  
Huseyin Tunc ◽  
Linkan Bian ◽  
William Bullington
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Real Life ◽  
Dynamic Environments ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Ant Colony Optimization Algorithm ◽  
Pheromone Trails

Abstract This study presents a novel Ant Colony Optimization (ACO) framework to solve a dynamic traveling salesman problem. To maintain diversity via transferring knowledge to the pheromone trails from previous environments, Adaptive Large Neighborhood Search (ALNS) based immigrant schemes have been developed and compared with existing ACO-based immigrant schemes available in the literature. Numerical results indicate that the proposed immigrant schemes can handle dynamic environments efficiently compared to other immigrant-based ACOs. Finally, a real life case study for wildlife surveillance (specifically, deer) by drones has been developed and solved using the proposed algorithm. Results indicate that the drone service capabilities can be significantly impacted when the dynamicity of deer are taken into consideration. Highlights Proposed a novel ACO-ALNS based metaheuristic. Four variants of the proposed metaheuristic is developed to investigate the efficiency of each of them. A real life case study mirroring the behavior of DTSP is developed.

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