Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552609
Mouna Ben Ishak, N. Ben Amor, Philippe Leray
With the widespread use of Internet, recommender systems are becoming increasingly adapted to resolve the problem of information overload and to deal with large amount of online information. Several approaches and techniques have been proposed to implement recommender systems. Most of them rely on flat data representation while most real world data are stored in relational databases. This paper proposes a new recommendation approach that explores the relational nature of the data in hand using relational Bayesian networks.
{"title":"A RBN-based recommender system architecture","authors":"Mouna Ben Ishak, N. Ben Amor, Philippe Leray","doi":"10.1109/ICMSAO.2013.6552609","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552609","url":null,"abstract":"With the widespread use of Internet, recommender systems are becoming increasingly adapted to resolve the problem of information overload and to deal with large amount of online information. Several approaches and techniques have been proposed to implement recommender systems. Most of them rely on flat data representation while most real world data are stored in relational databases. This paper proposes a new recommendation approach that explores the relational nature of the data in hand using relational Bayesian networks.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"165 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134323848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552643
Smaoui Soulef, J. Hichem
Supply Chain Management has been the focus of many researchers in recent years. One of the most important components of supply chain is supplier selection. Hence, the search of the best or suitable suppliers is the most capital decision for companies to improve their performance and make the greatest benefits for practitioners. Many approaches of supplier evaluation have been developed in recent year. The objective of this paper is to propose a mathematical model for the supplier selection problem based on the goal programming model incorporating explicitly the satisfaction functions. Indeed, this model involves the suppliers and company constraints as well as the decision maker's preferences. To verify its validity, the model has been applied to a vendor selection process in the field of computer technology and compared with some methods mentioned in the literature.
{"title":"Vendor selection using goal programming with satisfaction functions","authors":"Smaoui Soulef, J. Hichem","doi":"10.1109/ICMSAO.2013.6552643","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552643","url":null,"abstract":"Supply Chain Management has been the focus of many researchers in recent years. One of the most important components of supply chain is supplier selection. Hence, the search of the best or suitable suppliers is the most capital decision for companies to improve their performance and make the greatest benefits for practitioners. Many approaches of supplier evaluation have been developed in recent year. The objective of this paper is to propose a mathematical model for the supplier selection problem based on the goal programming model incorporating explicitly the satisfaction functions. Indeed, this model involves the suppliers and company constraints as well as the decision maker's preferences. To verify its validity, the model has been applied to a vendor selection process in the field of computer technology and compared with some methods mentioned in the literature.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132751874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552712
R. Hachicha
In order to make the workflows systems more adaptable to the dynamic changes, we depict a task formal model which makes possible to define the various relations between the tasks and particularly to ensure the feasibility of the workflow execution and to provide solutions whenever any change is met during its running.
{"title":"A formal task model for flexible workflows systems","authors":"R. Hachicha","doi":"10.1109/ICMSAO.2013.6552712","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552712","url":null,"abstract":"In order to make the workflows systems more adaptable to the dynamic changes, we depict a task formal model which makes possible to define the various relations between the tasks and particularly to ensure the feasibility of the workflow execution and to provide solutions whenever any change is met during its running.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116748893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552587
Sofiene Abidi, S. Krichen, E. Alba, J. M. Molina
We develop in the present paper a genetic algorithm for the one-dimensional bin packing problem. This algorithm performs a series of perturbations in an attempt to improve the current solution, applying some problem dependant genetic operators. Our procedure is efficient and easy to implement. We apply it to several benchmark instances taken from some problem sets and compare our results to those found in the literature. We find that our algorithm is able to generates competitive results compared to the best methods known so far and computes, for the first time, one optimal solution for one open benchmark instance.
{"title":"Improvement heuristic for solving the one-dimensional bin-packing problem","authors":"Sofiene Abidi, S. Krichen, E. Alba, J. M. Molina","doi":"10.1109/ICMSAO.2013.6552587","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552587","url":null,"abstract":"We develop in the present paper a genetic algorithm for the one-dimensional bin packing problem. This algorithm performs a series of perturbations in an attempt to improve the current solution, applying some problem dependant genetic operators. Our procedure is efficient and easy to implement. We apply it to several benchmark instances taken from some problem sets and compare our results to those found in the literature. We find that our algorithm is able to generates competitive results compared to the best methods known so far and computes, for the first time, one optimal solution for one open benchmark instance.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116835849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552668
Laila Messaoudi, A. Rebai
Earlier works on Goal Programming models for portfolio selection problem under uncertainty did not utilize the combination of the different types of uncertainty for a given problem and they only assumed the existence of stochastic or fuzzy uncertainty: These models may be too restrictive in modeling of real life decision making problems where randomness and fuzziness are often coexist. In this paper, we develop a novel fuzzy goal programming model for solving a stochastic multi-objective portfolio selection problem. In this model, the fuzzy chance-constrained goals are described along with the imprecise importance relations among them. The developed model will be utilized to build a new portfolio selection model that considers the tradeoffs between expected return, Value-at-Risk (VaR), the price earning ratio and the flexibility of investor's preferences.
{"title":"A fuzzy stochastic Goal Programming approach for solving portfolio selection problem","authors":"Laila Messaoudi, A. Rebai","doi":"10.1109/ICMSAO.2013.6552668","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552668","url":null,"abstract":"Earlier works on Goal Programming models for portfolio selection problem under uncertainty did not utilize the combination of the different types of uncertainty for a given problem and they only assumed the existence of stochastic or fuzzy uncertainty: These models may be too restrictive in modeling of real life decision making problems where randomness and fuzziness are often coexist. In this paper, we develop a novel fuzzy goal programming model for solving a stochastic multi-objective portfolio selection problem. In this model, the fuzzy chance-constrained goals are described along with the imprecise importance relations among them. The developed model will be utilized to build a new portfolio selection model that considers the tradeoffs between expected return, Value-at-Risk (VaR), the price earning ratio and the flexibility of investor's preferences.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115312584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552629
S. Chargui, Hana Gharbi, M. Slimani
A general research subject in rainfall runoff modeling is assessment of space time variability in event time series. A MATLAB program is developed for taking account of the space and time distribution. We focus on central Tunisia (Merguellil and Skhira basin), where rainfall is known by its high variability for over a decade. We introduce a variability matrix on a geomorphologybased transfer function. Robustness of the developed program is checked for some real events from the Skhira basin data. Its potential is especially interesting in datasparse regions where the geomorphologybased approach can be applied in a vigorous and adjustable way, and where the accounting of rainfall space and time variability is much supple.
{"title":"A MATLAB program for identifying the rainfall variability in rainfall-runoff modeling in Semi arid region (Merguellil basin: Central Tunisia)","authors":"S. Chargui, Hana Gharbi, M. Slimani","doi":"10.1109/ICMSAO.2013.6552629","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552629","url":null,"abstract":"A general research subject in rainfall runoff modeling is assessment of space time variability in event time series. A MATLAB program is developed for taking account of the space and time distribution. We focus on central Tunisia (Merguellil and Skhira basin), where rainfall is known by its high variability for over a decade. We introduce a variability matrix on a geomorphologybased transfer function. Robustness of the developed program is checked for some real events from the Skhira basin data. Its potential is especially interesting in datasparse regions where the geomorphologybased approach can be applied in a vigorous and adjustable way, and where the accounting of rainfall space and time variability is much supple.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121722683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552664
Hana Sulieman
D-optimal experimental designs for precise parameter estimation are designs which minimize the determinant of the variance-covariance matrix of the parameter estimates based on the conventional parametric sensitivity coefficients. These coefficients are local measures of sensitivity defined by the first-order derivative of system model function with respect to parameters of interest. For nonlinear models, linear sensitivity information fail to gouge the sensitivity behavior of the model and hence, the resulting determinant of variance-covariance matrix may not give a true indication of the volume of the joint inference region for system parameters. In this article, we employ the profile-based sensitivity coefficients developed by Sulieman et.al. (2001, 2004)in the D-optimal experimental designs. Profile-based sensitivity coefficients account for both model nonlinearity and parameter estimate correlations and are, therefore, expected to yield better precision of parameter estimates when used in the optimization of particular experimental design criteria. Some characteristics of the profile-based designs and related computational aspects are discussed. Application of the new designs to nonlinear model case is also presented.
{"title":"Profile-based sensitivity in the design of experiments for parameter precision","authors":"Hana Sulieman","doi":"10.1109/ICMSAO.2013.6552664","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552664","url":null,"abstract":"D-optimal experimental designs for precise parameter estimation are designs which minimize the determinant of the variance-covariance matrix of the parameter estimates based on the conventional parametric sensitivity coefficients. These coefficients are local measures of sensitivity defined by the first-order derivative of system model function with respect to parameters of interest. For nonlinear models, linear sensitivity information fail to gouge the sensitivity behavior of the model and hence, the resulting determinant of variance-covariance matrix may not give a true indication of the volume of the joint inference region for system parameters. In this article, we employ the profile-based sensitivity coefficients developed by Sulieman et.al. (2001, 2004)in the D-optimal experimental designs. Profile-based sensitivity coefficients account for both model nonlinearity and parameter estimate correlations and are, therefore, expected to yield better precision of parameter estimates when used in the optimization of particular experimental design criteria. Some characteristics of the profile-based designs and related computational aspects are discussed. Application of the new designs to nonlinear model case is also presented.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123914624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552580
Ketfi Nadhir, Djabali Chabane, B. Tarek
This paper propose a Firefly algorithm (FA) for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total real power losses and to improve the voltage profile. FA is a metaheuristic algorithm which is inspired by the flashing behavior of fireflies. The primary purpose of firefly's flash is to act as a signal system to attract other fireflies. Metaheuristic algorithms are widely recognized as one of the most practical approaches for hard optimization problems. The most attractive feature of a metaheuristic is that its application requires no special knowledge on the optimization problem. In this paper, IEEE 33-bus distribution test system is used to show the effectiveness of the FA. Comparison with Shuffled Frog Leaping Algorithm (SFLA) is also given.
{"title":"Firefly algorithm based energy loss minimization approach for optimal sizing & placement of distributed generation","authors":"Ketfi Nadhir, Djabali Chabane, B. Tarek","doi":"10.1109/ICMSAO.2013.6552580","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552580","url":null,"abstract":"This paper propose a Firefly algorithm (FA) for optimal placement and sizing of distributed generation (DG) in radial distribution system to minimize the total real power losses and to improve the voltage profile. FA is a metaheuristic algorithm which is inspired by the flashing behavior of fireflies. The primary purpose of firefly's flash is to act as a signal system to attract other fireflies. Metaheuristic algorithms are widely recognized as one of the most practical approaches for hard optimization problems. The most attractive feature of a metaheuristic is that its application requires no special knowledge on the optimization problem. In this paper, IEEE 33-bus distribution test system is used to show the effectiveness of the FA. Comparison with Shuffled Frog Leaping Algorithm (SFLA) is also given.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124172973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552641
Reza Soosahabi, N. Nasirian, M. Naraghi-Pour, M. Bayoumi
We consider the problem of inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) signals stemming from nonlinearities of power amplifiers (PA). OFDM signals often provoke amplifier nonlinearities due to their high peak-to-average power ratio. Predistortion is often considered in order to mitigate the resulting ICI. We consider a digital baseband predistorter based on the memory polynomial model. The predistorter is designed in the frequency domain using the the indirect training and the linear minimum mean-squared error (LMMSE) estimation method. It is shown that the proposed algorithm has a very low computation complexity and is scalable for systems with a large number of subcarriers. The simulation results show that for similar computational complexities, the proposed method has a significant performance improvement in the sense of total degradation compared to the methods in [1] and [2].
{"title":"A fast new method to mitigate amplifier-induced ICI in OFDM systems based on predistortion in DFT domain","authors":"Reza Soosahabi, N. Nasirian, M. Naraghi-Pour, M. Bayoumi","doi":"10.1109/ICMSAO.2013.6552641","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552641","url":null,"abstract":"We consider the problem of inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) signals stemming from nonlinearities of power amplifiers (PA). OFDM signals often provoke amplifier nonlinearities due to their high peak-to-average power ratio. Predistortion is often considered in order to mitigate the resulting ICI. We consider a digital baseband predistorter based on the memory polynomial model. The predistorter is designed in the frequency domain using the the indirect training and the linear minimum mean-squared error (LMMSE) estimation method. It is shown that the proposed algorithm has a very low computation complexity and is scalable for systems with a large number of subcarriers. The simulation results show that for similar computational complexities, the proposed method has a significant performance improvement in the sense of total degradation compared to the methods in [1] and [2].","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124330342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552685
Saima Dhouib, S. Dhouib, H. Chabchoub
In this paper, an Artificial Bee Colony (ABC) metaheuristic is adapted to find Pareto optimal solutions set for Goal Programming (GP) Problems. At first, the GP model is converted to a multi-objective optimization problem (MOO) of minimizing deviations from fixed goals. At second, the ABC is personalized to support the MOO by means of a weighted sum formulation for the objective function: solving several scalarization of the objective function according to a weight vector with non-negative components. The efficiency of the proposed approach is demonstrated by nonlinear engineering design problems. In all problems, multiple solutions to the goal programming problem are found in short computational time using very few user-defined parameters.
{"title":"Artificial bee colony metaheuristic to find pareto optimal solutions set for engineering design problems","authors":"Saima Dhouib, S. Dhouib, H. Chabchoub","doi":"10.1109/ICMSAO.2013.6552685","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552685","url":null,"abstract":"In this paper, an Artificial Bee Colony (ABC) metaheuristic is adapted to find Pareto optimal solutions set for Goal Programming (GP) Problems. At first, the GP model is converted to a multi-objective optimization problem (MOO) of minimizing deviations from fixed goals. At second, the ABC is personalized to support the MOO by means of a weighted sum formulation for the objective function: solving several scalarization of the objective function according to a weight vector with non-negative components. The efficiency of the proposed approach is demonstrated by nonlinear engineering design problems. In all problems, multiple solutions to the goal programming problem are found in short computational time using very few user-defined parameters.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124815979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：