Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552573
Said Toumi, B. Jarboui, M. Eddaly, A. Rebai
The paper presents an exact approach for a permutation flowshop scheduling problem with blocking constraint. The objective is to minimize the total completion time of jobs. The solution method is a branch and bound procedure where lower bounds based on problem characteristics are derived. Whithin an experimental performance analysis, this approach is evaluated.
{"title":"New lower bounds for the blocking flowshop scheduling problem to minimize the total completion time criterion","authors":"Said Toumi, B. Jarboui, M. Eddaly, A. Rebai","doi":"10.1109/ICMSAO.2013.6552573","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552573","url":null,"abstract":"The paper presents an exact approach for a permutation flowshop scheduling problem with blocking constraint. The objective is to minimize the total completion time of jobs. The solution method is a branch and bound procedure where lower bounds based on problem characteristics are derived. Whithin an experimental performance analysis, this approach is evaluated.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"250 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":"134053074","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.2013.6552553
A. Lasfer, H. El-Baz, I. Zualkernan
Neural Networks (NN) have been used extensively by researchers and practitioners to forecast financial time series. The forecasting accuracy of NN depends on several design parameters, and fine-tuning them to suit a particular financial time series is essential for attaining lower error levels and minimizing running time. This paper presents the results of a two-level full-factorial Design of Experiment developed to investigate the significant factors that influence the performance of NN in forecasting financial time series. The factors considered in this paper are NN type, number of neurons in the hidden layer, the learning rate of LM algorithm, and the type of output layer transfer function. The methodology is applied to the Morgan Stanley Capital International Index for United Arab Emirates.
{"title":"Neural Network design parameters for forecasting financial time series","authors":"A. Lasfer, H. El-Baz, I. Zualkernan","doi":"10.1109/ICMSAO.2013.6552553","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552553","url":null,"abstract":"Neural Networks (NN) have been used extensively by researchers and practitioners to forecast financial time series. The forecasting accuracy of NN depends on several design parameters, and fine-tuning them to suit a particular financial time series is essential for attaining lower error levels and minimizing running time. This paper presents the results of a two-level full-factorial Design of Experiment developed to investigate the significant factors that influence the performance of NN in forecasting financial time series. The factors considered in this paper are NN type, number of neurons in the hidden layer, the learning rate of LM algorithm, and the type of output layer transfer function. The methodology is applied to the Morgan Stanley Capital International Index for United Arab Emirates.","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":"129299394","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.2013.6552717
M. Ghanim, G. Abu-Lebdeh, K. Ahmed
The Design-Hour Volume (DHV), which is defined as the 30th highest hour volume in a year, is a significant concept in transportation engineering and planning. Finding the DHV requires hourly traffic counts for an entire year. However, this becomes a challengeable task when part of the data is not collected because of different reasons, such as construction activities or hardware failure. In this paper, an Artificial Neural Network (ANN) approach is used to develop a DHV prediction model based on historical traffic counts. The model takes into account the correlation between DHV and other variables such as AADT, functional classification, and number of lanes. Results show that the ANN model is capable of providing accurate and reliable DHV estimates.
{"title":"Modeling historical traffic data using artificial neural networks","authors":"M. Ghanim, G. Abu-Lebdeh, K. Ahmed","doi":"10.1109/ICMSAO.2013.6552717","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552717","url":null,"abstract":"The Design-Hour Volume (DHV), which is defined as the 30th highest hour volume in a year, is a significant concept in transportation engineering and planning. Finding the DHV requires hourly traffic counts for an entire year. However, this becomes a challengeable task when part of the data is not collected because of different reasons, such as construction activities or hardware failure. In this paper, an Artificial Neural Network (ANN) approach is used to develop a DHV prediction model based on historical traffic counts. The model takes into account the correlation between DHV and other variables such as AADT, functional classification, and number of lanes. Results show that the ANN model is capable of providing accurate and reliable DHV estimates.","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":"116361606","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.2013.6552682
Fatemeh Marzbani, A. Osman, Mohamed S. Hassan, T. Landolsi
Integration of wind power generation with existing power systems requires accurate forecasting of wind power due to its intermittent nature. Accurate wind power forecast can help system operators to include wind power in economic dispatch and unit commitment problems. In this paper, a simple window-based, adaptive-parameter regression model for short-term wind power forecasting that can be used in system operation is developed. In order to assess the accuracy of the forecasted wind power, the output of the prediction is used to solve the economic dispatch problem using a genetic algorithm (GA) technique.
{"title":"Short-term wind power forecast for economic dispatch","authors":"Fatemeh Marzbani, A. Osman, Mohamed S. Hassan, T. Landolsi","doi":"10.1109/ICMSAO.2013.6552682","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552682","url":null,"abstract":"Integration of wind power generation with existing power systems requires accurate forecasting of wind power due to its intermittent nature. Accurate wind power forecast can help system operators to include wind power in economic dispatch and unit commitment problems. In this paper, a simple window-based, adaptive-parameter regression model for short-term wind power forecasting that can be used in system operation is developed. In order to assess the accuracy of the forecasted wind power, the output of the prediction is used to solve the economic dispatch problem using a genetic algorithm (GA) technique.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"104 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":"115760098","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.2013.6552678
A. Belaid, Jamil Razmak
The Decision Support Systems (DSS) were widely applied to the field of Multi-Criteria Decision Aid (MCDA). In fact, the most popular MCDA aggregating procedures are those supported by a DSS. Our literature review reveals that the MultiCriteria Decision Support Systems(MCDSS) have applied in several fields such as : Production and Operation Management , Finance, Education, Human Resources, Real Estate and Multi Media. The aim of this paper is to provide a state-of-the Art and a classification by field of MCDSS applications.
{"title":"Multi-Criteria Decision Support Systems: A glorious history and a promising future","authors":"A. Belaid, Jamil Razmak","doi":"10.1109/ICMSAO.2013.6552678","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552678","url":null,"abstract":"The Decision Support Systems (DSS) were widely applied to the field of Multi-Criteria Decision Aid (MCDA). In fact, the most popular MCDA aggregating procedures are those supported by a DSS. Our literature review reveals that the MultiCriteria Decision Support Systems(MCDSS) have applied in several fields such as : Production and Operation Management , Finance, Education, Human Resources, Real Estate and Multi Media. The aim of this paper is to provide a state-of-the Art and a classification by field of MCDSS applications.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"69 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":"114512300","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.2013.6552647
Hela Masri, S. Krichen, A. Guitouni
Multicommodity flow problems (MCFPs) arise when several commodities are to be transmitted within a capacitated network. MCFP has received a great attention in the literature for the single objective case, while only few works addressed the problem in a multiobjective framework. In this paper, we study the MCFP with multiple objectives. This problem is modeled as multiobjective linear program with continuous decision variables. In order to solve this problem, we propose to apply an exact solution approach operating in the objective space, called the Efficient Solutions Adjacency based Method (ESAM) to generate all the maximal efficient faces and extreme points. An experimental study is conducted to test the efficiency of the ESAM on solving small and medium sized multiobjective MCFPs.
{"title":"Generating maximal efficient faces for the multiobjective multicommodity flow problem","authors":"Hela Masri, S. Krichen, A. Guitouni","doi":"10.1109/ICMSAO.2013.6552647","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552647","url":null,"abstract":"Multicommodity flow problems (MCFPs) arise when several commodities are to be transmitted within a capacitated network. MCFP has received a great attention in the literature for the single objective case, while only few works addressed the problem in a multiobjective framework. In this paper, we study the MCFP with multiple objectives. This problem is modeled as multiobjective linear program with continuous decision variables. In order to solve this problem, we propose to apply an exact solution approach operating in the objective space, called the Efficient Solutions Adjacency based Method (ESAM) to generate all the maximal efficient faces and extreme points. An experimental study is conducted to test the efficiency of the ESAM on solving small and medium sized multiobjective MCFPs.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"34 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":"127595884","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.2013.6552574
Samah Yahia, S. Fterich, Mohamed Nacer Abdelkrim
This paper attempts to show and apply the principle of information fusion of the belief function theory to the localization context. In fact, the main objective is to use imprecise and uncertain information stemming from two categories of sensors that are used to estimate the position of an object. What is more, we try to benefit from the advantages presenting the belief function theory, especially when they are compared to the probability approach, so as to consider the disjunctions and model the notions of uncertainty and imprecision.
{"title":"The belief function theory within the framework of localizing an object","authors":"Samah Yahia, S. Fterich, Mohamed Nacer Abdelkrim","doi":"10.1109/ICMSAO.2013.6552574","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552574","url":null,"abstract":"This paper attempts to show and apply the principle of information fusion of the belief function theory to the localization context. In fact, the main objective is to use imprecise and uncertain information stemming from two categories of sensors that are used to estimate the position of an object. What is more, we try to benefit from the advantages presenting the belief function theory, especially when they are compared to the probability approach, so as to consider the disjunctions and model the notions of uncertainty and imprecision.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"15 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":"125601382","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.2013.6552550
M. Rabbani, Saeed Zameni, Seyed Mahmood Kazemi
Generally, P-hub center problem aims to establish a hub network wherein the maximum travel times between any given pair of nodes is minimized. The distinctive feature of this paper is proposing a new mathematical formulation for modeling costs in a p-hub center problem. Here, instead of seeing costs as a linear function of distance, we, for the first time, formulated costs that consist of different parts: fixed cost, Health, Safety and Environment cost, energy cost and personnel cost. Such an integrated model results in a hard-to-solve nonlinear formulation. For a small scale standard data set the model is solved by Lingo 8.0 software but the area for solving the formulation for large scale instances is yet to be hunted.
{"title":"Proposing a new mathematical formulation for modeling costs in a p-hub center problem","authors":"M. Rabbani, Saeed Zameni, Seyed Mahmood Kazemi","doi":"10.1109/ICMSAO.2013.6552550","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552550","url":null,"abstract":"Generally, P-hub center problem aims to establish a hub network wherein the maximum travel times between any given pair of nodes is minimized. The distinctive feature of this paper is proposing a new mathematical formulation for modeling costs in a p-hub center problem. Here, instead of seeing costs as a linear function of distance, we, for the first time, formulated costs that consist of different parts: fixed cost, Health, Safety and Environment cost, energy cost and personnel cost. Such an integrated model results in a hard-to-solve nonlinear formulation. For a small scale standard data set the model is solved by Lingo 8.0 software but the area for solving the formulation for large scale instances is yet to be hunted.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"9 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":"126402666","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.2013.6552694
El-Fayedh Rimeh, F. Ben Abdelaziz, A. Belaid
Scenario generation allows Stochastic programming the expression of uncertainty. In fact scenarios allow the representation of stochastic elements by discrete distributions with many outcomes. In [15] authors proposed to use a nonlinear program to satisfy specified statistical properties. In this paper, we formulate a goal programming model for scenario generation for several random objectives. Scenarios generation help decision maker in solving stochastic goal programming model for portfolio selection problem. We applied our models on some Tunisian stock market security's data where we consider two random objectives that are the rate of return and liquidity.
{"title":"Scenario generation for multi-objective stochastic portfolio selection","authors":"El-Fayedh Rimeh, F. Ben Abdelaziz, A. Belaid","doi":"10.1109/ICMSAO.2013.6552694","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552694","url":null,"abstract":"Scenario generation allows Stochastic programming the expression of uncertainty. In fact scenarios allow the representation of stochastic elements by discrete distributions with many outcomes. In [15] authors proposed to use a nonlinear program to satisfy specified statistical properties. In this paper, we formulate a goal programming model for scenario generation for several random objectives. Scenarios generation help decision maker in solving stochastic goal programming model for portfolio selection problem. We applied our models on some Tunisian stock market security's data where we consider two random objectives that are the rate of return and liquidity.","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":"121584336","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}
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