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Your search keyword '"Perea Rojas-Marcos, Federico"' showing total 4 results
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4 results on '"Perea Rojas-Marcos, Federico"'

1. Transportation infrastructure network design in the presence of modal competition: computational complexity classification and a genetic algorithm

Author

Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat, Agencia Estatal de Investigación, European Regional Development Fund, Ministerio de Ciencia e Innovación, Universitat Politècnica de València, Ministerio de Economía y Competitividad, Perea Rojas Marcos, Federico, Menezes, Mozart B. C., Mesa, Juan A., Rubio-Del-Rey, Fernando, Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat, Agencia Estatal de Investigación, European Regional Development Fund, Ministerio de Ciencia e Innovación, Universitat Politècnica de València, Ministerio de Economía y Competitividad, Perea Rojas Marcos, Federico, Menezes, Mozart B. C., Mesa, Juan A., and Rubio-Del-Rey, Fernando

Abstract

[EN] In this paper we analyze the computational complexity of transportation infrastructure network design problems, in the presence of a competing transportation mode. Some of these problems have previously been introduced in the literature. All problems studied have a common objective: the maximization of the number of travelers using the new network to be built. The differences between them are due to two factors. The first one is the constraints that the new network should satisfy: (1) budget constraint, (2) no-cycle constraint, (3) both constraints. The second factor is the topology of the network formed by the feasible links and stations: (1) a general network, (2) a forest. By combining these two factors, in total we analyze six problems, five of them are shown to be NP-hard, the sixth being trivial. Due to the NP-hardness of these problems, a genetic algorithm is proposed. Computational experiments show the applicability of this algorithm.

Published
2020

2. Transportation infrastructure network design in the presence of modal competition: computational complexity classification and a genetic algorithm

Author

Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat, Agencia Estatal de Investigación, European Regional Development Fund, Universitat Politècnica de València, Ministerio de Economía y Competitividad, Perea Rojas Marcos, Federico, Menezes, Mozart B. C., Mesa, Juan A., Rubio-Del-Rey, Fernando, Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat, Agencia Estatal de Investigación, European Regional Development Fund, Universitat Politècnica de València, Ministerio de Economía y Competitividad, Perea Rojas Marcos, Federico, Menezes, Mozart B. C., Mesa, Juan A., and Rubio-Del-Rey, Fernando

Abstract

[EN] In this paper we analyze the computational complexity of transportation infrastructure network design problems, in the presence of a competing transportation mode. Some of these problems have previously been introduced in the literature. All problems studied have a common objective: the maximization of the number of travelers using the new network to be built. The differences between them are due to two factors. The first one is the constraints that the new network should satisfy: (1) budget constraint, (2) no-cycle constraint, (3) both constraints. The second factor is the topology of the network formed by the feasible links and stations: (1) a general network, (2) a forest. By combining these two factors, in total we analyze six problems, five of them are shown to be NP-hard, the sixth being trivial. Due to the NP-hardness of these problems, a genetic algorithm is proposed. Computational experiments show the applicability of this algorithm.

Published
2020

3. The Railway Line Frequency and Size Setting Problem

Author

Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat, Junta de Andalucía, European Regional Development Fund, Universitat Politècnica de València, Ministerio de Economía y Competitividad, Ministerio de Economía, Industria y Competitividad, Natural Sciences and Engineering Research Council of Canada, De-Los-Santos, Alicia, Laporte, Gilbert, Mesa, Juan A., Perea Rojas Marcos, Federico, Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat, Junta de Andalucía, European Regional Development Fund, Universitat Politècnica de València, Ministerio de Economía y Competitividad, Ministerio de Economía, Industria y Competitividad, Natural Sciences and Engineering Research Council of Canada, De-Los-Santos, Alicia, Laporte, Gilbert, Mesa, Juan A., and Perea Rojas Marcos, Federico

Abstract

[EN] The problem studied in this paper takes as input data a set of lines forming a railway network, and an origin¿destination (OD) matrix. The OD pairs may use either the railway network or an alternative transportation mode. The objective is to determine the frequency/headway of each line as well as its number of carriages, so that the net profit of the railway network is maximized. We propose a mixed integer non-linear programming formulation for this problem. Because of the computational intractability of this model, we develop four algorithms: a mixed integer linear programming (MIP) model, a MIP-based iterative algorithm, a shortest-path based algorithm, and a local search. These four algorithms are tested and compared over a set of randomly generated instances. An application over a case study shows that only the local search heuristic is capable of dealing with large instances.

Published
2017

4. The Railway Line Frequency and Size Setting Problem

Author

Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat, Junta de Andalucía, European Regional Development Fund, Universitat Politècnica de València, Ministerio de Economía y Competitividad, Ministerio de Economía, Industria y Competitividad, Natural Sciences and Engineering Research Council of Canada, De-Los-Santos, Alicia, Laporte, Gilbert, Mesa, Juan A., Perea Rojas Marcos, Federico, Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat, Junta de Andalucía, European Regional Development Fund, Universitat Politècnica de València, Ministerio de Economía y Competitividad, Ministerio de Economía, Industria y Competitividad, Natural Sciences and Engineering Research Council of Canada, De-Los-Santos, Alicia, Laporte, Gilbert, Mesa, Juan A., and Perea Rojas Marcos, Federico

Abstract

[EN] The problem studied in this paper takes as input data a set of lines forming a railway network, and an origin¿destination (OD) matrix. The OD pairs may use either the railway network or an alternative transportation mode. The objective is to determine the frequency/headway of each line as well as its number of carriages, so that the net profit of the railway network is maximized. We propose a mixed integer non-linear programming formulation for this problem. Because of the computational intractability of this model, we develop four algorithms: a mixed integer linear programming (MIP) model, a MIP-based iterative algorithm, a shortest-path based algorithm, and a local search. These four algorithms are tested and compared over a set of randomly generated instances. An application over a case study shows that only the local search heuristic is capable of dealing with large instances.

Published
2017

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