A Simulated Annealing Approach for the Train Design Optimization Problem
The Train Design Optimization Problem regards making optimal decisions on the number and movement of locomotives and crews through a railway network, so as to satisfy requested pick-up and delivery of car blocks at stations. In a mathematical programming formulation, the objective function to minimi...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2017/4703106 |
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doaj-93a1b9b1a39a4aaeba8fc6a37be9d6d92020-11-24T23:17:13ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472017-01-01201710.1155/2017/47031064703106A Simulated Annealing Approach for the Train Design Optimization ProblemFederico Alonso-Pecina0David Romero1Universidad Autónoma del Estado de Morelos, 62209 Cuernavaca, MOR, MexicoInstituto de Matemáticas, Universidad Nacional Autónoma de México, 62210 Cuernavaca, MOR, MexicoThe Train Design Optimization Problem regards making optimal decisions on the number and movement of locomotives and crews through a railway network, so as to satisfy requested pick-up and delivery of car blocks at stations. In a mathematical programming formulation, the objective function to minimize is composed of the costs associated with the movement of locomotives and cars, the loading/unloading operations, the number of locomotives, and the crews’ return to their departure stations. The constraints include upper bounds for number of car blocks per locomotive, number of car block swaps, and number of locomotives passing through railroad segments. We propose here a heuristic method to solve this highly combinatorial problem in two steps. The first one finds an initial, feasible solution by means of an ad hoc algorithm. The second step uses the simulated annealing concept to improve the initial solution, followed by a procedure aiming to further reduce the number of needed locomotives. We show that our results are competitive with those found in the literature.http://dx.doi.org/10.1155/2017/4703106 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Federico Alonso-Pecina David Romero |
spellingShingle |
Federico Alonso-Pecina David Romero A Simulated Annealing Approach for the Train Design Optimization Problem Mathematical Problems in Engineering |
author_facet |
Federico Alonso-Pecina David Romero |
author_sort |
Federico Alonso-Pecina |
title |
A Simulated Annealing Approach for the Train Design Optimization Problem |
title_short |
A Simulated Annealing Approach for the Train Design Optimization Problem |
title_full |
A Simulated Annealing Approach for the Train Design Optimization Problem |
title_fullStr |
A Simulated Annealing Approach for the Train Design Optimization Problem |
title_full_unstemmed |
A Simulated Annealing Approach for the Train Design Optimization Problem |
title_sort |
simulated annealing approach for the train design optimization problem |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2017-01-01 |
description |
The Train Design Optimization Problem regards making optimal decisions on the number and movement of locomotives and crews through a railway network, so as to satisfy requested pick-up and delivery of car blocks at stations. In a mathematical programming formulation, the objective function to minimize is composed of the costs associated with the movement of locomotives and cars, the loading/unloading operations, the number of locomotives, and the crews’ return to their departure stations. The constraints include upper bounds for number of car blocks per locomotive, number of car block swaps, and number of locomotives passing through railroad segments. We propose here a heuristic method to solve this highly combinatorial problem in two steps. The first one finds an initial, feasible solution by means of an ad hoc algorithm. The second step uses the simulated annealing concept to improve the initial solution, followed by a procedure aiming to further reduce the number of needed locomotives. We show that our results are competitive with those found in the literature. |
url |
http://dx.doi.org/10.1155/2017/4703106 |
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