Articles de revista
http://hdl.handle.net/2117/3321
Thu, 11 Feb 2016 01:00:20 GMT
20160211T01:00:20Z

On the collaboration uncapacitated arc routing problem
http://hdl.handle.net/2117/81939
On the collaboration uncapacitated arc routing problem
Fernández Aréizaga, Elena; Fontana, Dario; Speranza, M. Grazia
© 2015 Elsevier Ltd. All rights reserved.
This paper introduces a new arc routing problem for the optimization of a collaboration scheme among carriers. This yields to the study of a profitable uncapacitated arc routing problem with multiple depots, where carriers collaborate to improve the profit gained. In the first model the goal is the maximization of the total profit of the coalition of carriers, independently of the individual profit of each carrier. Then, a lower bound on the individual profit of each carrier is included. This lower bound may represent the profit of the carrier in the case no collaboration is implemented. The models are formulated as integer linear programs and solved through a branchandcut algorithm. Theoretical results, concerning the computational complexity, the impact of collaboration on profit and a game theoretical perspective, are provided. The models are tested on a set of 971 instances generated from 118 benchmark instances for the Privatized Rural Postman Problem, with up to 102 vertices. All the 971 instances are solved to optimality within few seconds.
Mon, 25 Jan 2016 09:33:18 GMT
http://hdl.handle.net/2117/81939
20160125T09:33:18Z
Fernández Aréizaga, Elena
Fontana, Dario
Speranza, M. Grazia
© 2015 Elsevier Ltd. All rights reserved.
This paper introduces a new arc routing problem for the optimization of a collaboration scheme among carriers. This yields to the study of a profitable uncapacitated arc routing problem with multiple depots, where carriers collaborate to improve the profit gained. In the first model the goal is the maximization of the total profit of the coalition of carriers, independently of the individual profit of each carrier. Then, a lower bound on the individual profit of each carrier is included. This lower bound may represent the profit of the carrier in the case no collaboration is implemented. The models are formulated as integer linear programs and solved through a branchandcut algorithm. Theoretical results, concerning the computational complexity, the impact of collaboration on profit and a game theoretical perspective, are provided. The models are tested on a set of 971 instances generated from 118 benchmark instances for the Privatized Rural Postman Problem, with up to 102 vertices. All the 971 instances are solved to optimality within few seconds.

A BRILS metaheuristic for nonsmooth flowshop problems with failurerisk costs
http://hdl.handle.net/2117/81738
A BRILS metaheuristic for nonsmooth flowshop problems with failurerisk costs
Ferrer Biosca, Alberto; Guimarans, Daniel; Ramalhino Lourenço, Helena; Juan Pérez, Ángel Alejandro
This paper analyzes a realistic variant of the Permutation FlowShop Problem (PFSP) by considering a nonsmooth objective function that takes into account not only the traditional makespan cost but also failurerisk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathematical model to describe this new PFSP variant. Then, a BiasedRandomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the wellknown NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of nonsmooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failurerisk costs during the exploration of the solution space are analyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other stateoftheart metaheuristic approaches, which have been conveniently adapted to consider failurerisk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar nonsmooth objective functions.
Wed, 20 Jan 2016 13:51:16 GMT
http://hdl.handle.net/2117/81738
20160120T13:51:16Z
Ferrer Biosca, Alberto
Guimarans, Daniel
Ramalhino Lourenço, Helena
Juan Pérez, Ángel Alejandro
This paper analyzes a realistic variant of the Permutation FlowShop Problem (PFSP) by considering a nonsmooth objective function that takes into account not only the traditional makespan cost but also failurerisk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathematical model to describe this new PFSP variant. Then, a BiasedRandomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the wellknown NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of nonsmooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failurerisk costs during the exploration of the solution space are analyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other stateoftheart metaheuristic approaches, which have been conveniently adapted to consider failurerisk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar nonsmooth objective functions.

Mathematical programming approaches for classes of random network problems
http://hdl.handle.net/2117/81111
Mathematical programming approaches for classes of random network problems
Castro Pérez, Jordi; Nasini, Stefano
Random simulations from complicated combinatorial sets are often needed in many classes of stochastic problems. This is particularly true in the analysis of complex networks, where researchers are usually interested in assessing whether an observed network feature is expected to be found within families of networks under some hypothesis (named conditional random networks, i.e., networks satisfying some linear constraints). This work presents procedures to generate networks with specified structural properties which rely on the Solution of classes of integer optimization problems. We show that, for many of them, the constraints matrices are totally unimodular, allowing the efficient generation of conditional random networks by specialized interiorpoint methods. The computational results suggest that the proposed methods can represent a general framework for the efficient generation of random networks even beyond the models analyzed in this paper. This work also opens the posSibility for other applications of mathematical programming in the analysis of complex networks. (C) 2015 Elsevier B.V. All rights reserved.
Thu, 07 Jan 2016 16:32:22 GMT
http://hdl.handle.net/2117/81111
20160107T16:32:22Z
Castro Pérez, Jordi
Nasini, Stefano
Random simulations from complicated combinatorial sets are often needed in many classes of stochastic problems. This is particularly true in the analysis of complex networks, where researchers are usually interested in assessing whether an observed network feature is expected to be found within families of networks under some hypothesis (named conditional random networks, i.e., networks satisfying some linear constraints). This work presents procedures to generate networks with specified structural properties which rely on the Solution of classes of integer optimization problems. We show that, for many of them, the constraints matrices are totally unimodular, allowing the efficient generation of conditional random networks by specialized interiorpoint methods. The computational results suggest that the proposed methods can represent a general framework for the efficient generation of random networks even beyond the models analyzed in this paper. This work also opens the posSibility for other applications of mathematical programming in the analysis of complex networks. (C) 2015 Elsevier B.V. All rights reserved.

Calidad y transparencia de Fisioterapia mediante guías de publicación
http://hdl.handle.net/2117/79037
Calidad y transparencia de Fisioterapia mediante guías de publicación
Cobo Valeri, Erik; Moher, David; Boutron, Isabelle; González Alastrué, José Antonio
Wed, 11 Nov 2015 13:37:28 GMT
http://hdl.handle.net/2117/79037
20151111T13:37:28Z
Cobo Valeri, Erik
Moher, David
Boutron, Isabelle
González Alastrué, José Antonio

Parking slot assignment for urban distribution: models and formulations
http://hdl.handle.net/2117/78886
Parking slot assignment for urban distribution: models and formulations
Roca Riu, Mireia; Fernández Aréizaga, Elena; Estrada Romeu, Miguel Ángel
A key element to enhance urban distribution is the adequate management of parking space, particularly for loading and unloading operations. An inadvance booking system able to be adjusted to users needs can be a very useful tool for city councils. Such a tool should be fed with criteria for allocating requests to time slots. In this paper we discuss alternative criteria for the parking slot assignment problem for urban distribution and we propose the use of mathematical programming formulations to model them. Several models are proposed, analyzed and compared among them. Extensive computational experience is presented with a detailed analysis and comparison, which provides quantitative indicators of the quality of each of the proposed models.
Fri, 06 Nov 2015 11:48:44 GMT
http://hdl.handle.net/2117/78886
20151106T11:48:44Z
Roca Riu, Mireia
Fernández Aréizaga, Elena
Estrada Romeu, Miguel Ángel
A key element to enhance urban distribution is the adequate management of parking space, particularly for loading and unloading operations. An inadvance booking system able to be adjusted to users needs can be a very useful tool for city councils. Such a tool should be fed with criteria for allocating requests to time slots. In this paper we discuss alternative criteria for the parking slot assignment problem for urban distribution and we propose the use of mathematical programming formulations to model them. Several models are proposed, analyzed and compared among them. Extensive computational experience is presented with a detailed analysis and comparison, which provides quantitative indicators of the quality of each of the proposed models.

The recoverable robust facility location problem
http://hdl.handle.net/2117/77975
The recoverable robust facility location problem
Alvarez Miranda, Eduardo; Fernández Aréizaga, Elena; Ljubic, Ivana
This work deals with a facility location problem in which location and allocation (transportation) policy is defined in two stages such that a firststage solution should be robust against the possible realizations (scenarios) of the input data that can only be revealed in a second stage. This solution should be robust enough so that it can be recovered promptly and at low cost in the second stage. In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty; it can address situations in which uncertainty may be present in any of the following four categories: providerside uncertainty, receiverside uncertainty, uncertainty inbetween, and uncertainty with respect to the cost parameters.; For this novel problem, a sophisticated branchandcut framework based on Benders decomposition is designed and complemented by several nontrivial enhancements, including scenario sorting, dual lifting, branching priorities, matheuristics and zerohalf cuts. Two large sets of instances that incorporate spatial and demographic information of countries such as Germany and US (transportation) and Bangladesh and the Philippines (disaster management) are introduced. They are used to analyze in detail the characteristics of the proposed model and the obtained solutions as well as the effectiveness, behavior and limitations of the designed algorithm. (C) 2015 Elsevier Ltd. All rights reserved.
Tue, 20 Oct 2015 12:49:02 GMT
http://hdl.handle.net/2117/77975
20151020T12:49:02Z
Alvarez Miranda, Eduardo
Fernández Aréizaga, Elena
Ljubic, Ivana
This work deals with a facility location problem in which location and allocation (transportation) policy is defined in two stages such that a firststage solution should be robust against the possible realizations (scenarios) of the input data that can only be revealed in a second stage. This solution should be robust enough so that it can be recovered promptly and at low cost in the second stage. In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty; it can address situations in which uncertainty may be present in any of the following four categories: providerside uncertainty, receiverside uncertainty, uncertainty inbetween, and uncertainty with respect to the cost parameters.; For this novel problem, a sophisticated branchandcut framework based on Benders decomposition is designed and complemented by several nontrivial enhancements, including scenario sorting, dual lifting, branching priorities, matheuristics and zerohalf cuts. Two large sets of instances that incorporate spatial and demographic information of countries such as Germany and US (transportation) and Bangladesh and the Philippines (disaster management) are introduced. They are used to analyze in detail the characteristics of the proposed model and the obtained solutions as well as the effectiveness, behavior and limitations of the designed algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

When centers can fail: a close second opportunity
http://hdl.handle.net/2117/76239
When centers can fail: a close second opportunity
Albareda Sambola, Maria; Hinojosa, Yolanda; Marín, Alfredo; Puerto Albandoz, Justo
This paper presents the pnext center problem, which aims to locate p out of n centers so as to minimize the maximum cost of allocating customers to backup centers. In this problem it is assumed that centers can fail and customers only realize that their closest (reference) center has failed upon arrival. When this happens, they move to their backup center, i.e., to the center that is closest to the reference center. Hence, minimizing the maximum travel distance from a customer to its backup center can be seen as an alternative approach to handle humanitarian logistics, that hedges customers against severe scenario deteriorations when a center fails.
For this extension of the pcenter problem we have developed several different integer programming formulations with their corresponding strengthenings based on valid inequalities and variable fixing. The suitability of these formulations for solving the pnext center problem using standard software is analyzed in a series of computational experiments. These experiments were carried out using instances taken from the previous discrete location literature.
Tue, 21 Jul 2015 09:22:42 GMT
http://hdl.handle.net/2117/76239
20150721T09:22:42Z
Albareda Sambola, Maria
Hinojosa, Yolanda
Marín, Alfredo
Puerto Albandoz, Justo
This paper presents the pnext center problem, which aims to locate p out of n centers so as to minimize the maximum cost of allocating customers to backup centers. In this problem it is assumed that centers can fail and customers only realize that their closest (reference) center has failed upon arrival. When this happens, they move to their backup center, i.e., to the center that is closest to the reference center. Hence, minimizing the maximum travel distance from a customer to its backup center can be seen as an alternative approach to handle humanitarian logistics, that hedges customers against severe scenario deteriorations when a center fails.
For this extension of the pcenter problem we have developed several different integer programming formulations with their corresponding strengthenings based on valid inequalities and variable fixing. The suitability of these formulations for solving the pnext center problem using standard software is analyzed in a series of computational experiments. These experiments were carried out using instances taken from the previous discrete location literature.

The reliable pmedian problem with atfacility service
http://hdl.handle.net/2117/28343
The reliable pmedian problem with atfacility service
Albareda Sambola, Maria; Hinojosa, Yolanda; Puerto Albandoz, Justo
This paper deals with a discrete facility location model where service is provided at the facility sites. It is assumed that facilities can fail and customers do not have information on failures before reaching them. As a consequence, they may need to visit more than one facility, following an optimized search scheme, in order to get service. The goal of the problem is to locate p facilities in order to minimize the expected total travel cost. The paper presents two alternative mathematical programming formulations for this problem and proposes a matheuristic based on a network flow model to provide solutions to it. The computational burden of the presented formulations is tested and compared on a testbed of instances.
Thu, 18 Jun 2015 14:05:36 GMT
http://hdl.handle.net/2117/28343
20150618T14:05:36Z
Albareda Sambola, Maria
Hinojosa, Yolanda
Puerto Albandoz, Justo
This paper deals with a discrete facility location model where service is provided at the facility sites. It is assumed that facilities can fail and customers do not have information on failures before reaching them. As a consequence, they may need to visit more than one facility, following an optimized search scheme, in order to get service. The goal of the problem is to locate p facilities in order to minimize the expected total travel cost. The paper presents two alternative mathematical programming formulations for this problem and proposes a matheuristic based on a network flow model to provide solutions to it. The computational burden of the presented formulations is tested and compared on a testbed of instances.

Fixandrelax approaches for controlled tabular adjustment
http://hdl.handle.net/2117/28326
Fixandrelax approaches for controlled tabular adjustment
Baena, Daniel; Castro Pérez, Jordi; González Alastrué, José Antonio
Controlled tabular adjustment (CIA) is a relatively new protection technique for tabular data protection. CTA formulates a mixed integer linear programming problem, which is challenging for tables of moderate size. Even finding a feasible initial solution may be a challenging task for large instances. On the other hand, end users of tabular data protection techniques give priority to fast executions and are thus satisfied in practice with suboptimal solutions. This work has two goals. First, the fixandrelax (FR) strategy is applied to obtain good feasible initial solutions to large CTA instances. FR is based on partitioning the set of binary variables into clusters to selectively explore a smaller branchandcut tree. Secondly, the FR solution is used as a warm start for a block coordinate descent (BCD) heuristic (approach named FR+BCD); BCD was confirmed to be a good option for large CTA instances in an earlier paper by the second and third coauthors (Comput Oper Res 2011;38:182635 [23]). We report extensive computational results on a set of realworld and synthetic CTA instances. FR is shown to be competitive compared to CPLEX branchandcut in terms of quickly finding either a feasible solution or a good upper bound. FR+BCD improved the quality of FR solutions for approximately 25% and 50% of the synthetic and realworld instances, respectively. FR or FR+BCD provided similar or better solutions in less CPU time than CPLEX for 73% of the difficult realworld instances. (C) 2015 Elsevier Ltd. All rights reserved.
Tue, 16 Jun 2015 16:38:50 GMT
http://hdl.handle.net/2117/28326
20150616T16:38:50Z
Baena, Daniel
Castro Pérez, Jordi
González Alastrué, José Antonio
Controlled tabular adjustment (CIA) is a relatively new protection technique for tabular data protection. CTA formulates a mixed integer linear programming problem, which is challenging for tables of moderate size. Even finding a feasible initial solution may be a challenging task for large instances. On the other hand, end users of tabular data protection techniques give priority to fast executions and are thus satisfied in practice with suboptimal solutions. This work has two goals. First, the fixandrelax (FR) strategy is applied to obtain good feasible initial solutions to large CTA instances. FR is based on partitioning the set of binary variables into clusters to selectively explore a smaller branchandcut tree. Secondly, the FR solution is used as a warm start for a block coordinate descent (BCD) heuristic (approach named FR+BCD); BCD was confirmed to be a good option for large CTA instances in an earlier paper by the second and third coauthors (Comput Oper Res 2011;38:182635 [23]). We report extensive computational results on a set of realworld and synthetic CTA instances. FR is shown to be competitive compared to CPLEX branchandcut in terms of quickly finding either a feasible solution or a good upper bound. FR+BCD improved the quality of FR solutions for approximately 25% and 50% of the synthetic and realworld instances, respectively. FR or FR+BCD provided similar or better solutions in less CPU time than CPLEX for 73% of the difficult realworld instances. (C) 2015 Elsevier Ltd. All rights reserved.

Methodology for the economic optimisation of energy storage systems for frequency support in wind power plants
http://hdl.handle.net/2117/28095
Methodology for the economic optimisation of energy storage systems for frequency support in wind power plants
Johnstone, Lewis; Díaz González, Francisco; Gomis Bellmunt, Oriol; Corchero García, Cristina; Cruz Zambrano, Miguel
This paper proposes a methodology for the economic optimisation of the sizing of Energy Storage Systems (ESSs) whilst enhancing the participation of Wind Power Plants (WPP) in network primary frequency control support. The methodology was designed flexibly, so it can be applied to different energy markets and to include different ESS technologies. The methodology includes the formulation and solving of a Linear Programming (LP) problem.; The methodology was applied to the particular case of a 50 MW WPP, equipped with a Vanadium Redox Flow battery (VRB) in the UK energy market. Analysis is performed considering real data on the UK regular energy market and the UK frequency response market. Data for wind power generation and energy storage costs are estimated from literature.; Results suggest that, under certain assumptions, ESSs can be profitable for the operator of a WPP that is providing frequency response. The ESS provides power reserves such that the WPP can generate close to the maximum energy available. The solution of the optimisation problem establishes that an ESS with a power rating of 5.3 MW and energy capacity of about 3 MW h would be enough to provide such service whilst maximising the incomes for the WPP operator considering the regular and frequency regulation UK markets. (C) 2014 Elsevier Ltd. All rights reserved.
Thu, 28 May 2015 12:55:30 GMT
http://hdl.handle.net/2117/28095
20150528T12:55:30Z
Johnstone, Lewis
Díaz González, Francisco
Gomis Bellmunt, Oriol
Corchero García, Cristina
Cruz Zambrano, Miguel
This paper proposes a methodology for the economic optimisation of the sizing of Energy Storage Systems (ESSs) whilst enhancing the participation of Wind Power Plants (WPP) in network primary frequency control support. The methodology was designed flexibly, so it can be applied to different energy markets and to include different ESS technologies. The methodology includes the formulation and solving of a Linear Programming (LP) problem.; The methodology was applied to the particular case of a 50 MW WPP, equipped with a Vanadium Redox Flow battery (VRB) in the UK energy market. Analysis is performed considering real data on the UK regular energy market and the UK frequency response market. Data for wind power generation and energy storage costs are estimated from literature.; Results suggest that, under certain assumptions, ESSs can be profitable for the operator of a WPP that is providing frequency response. The ESS provides power reserves such that the WPP can generate close to the maximum energy available. The solution of the optimisation problem establishes that an ESS with a power rating of 5.3 MW and energy capacity of about 3 MW h would be enough to provide such service whilst maximising the incomes for the WPP operator considering the regular and frequency regulation UK markets. (C) 2014 Elsevier Ltd. All rights reserved.