Pub Date : 2013-04-28DOI: 10.1109/ICMSAO.2013.6552544
O. Dridi, S. Krichen, A. Guitouni
The resource-constrained project scheduling problem is a general scheduling problem which involving activities need to be scheduled such that the makespan is minimized. However, the RCPSP is confirmed to be an NP-hard combinatorial problem. Restated, it is hard to be solved in a reasonable computational time. Therefore, numerous metaheuristics-based approaches have been developed for finding near-optimal solution for RCPSP. Genetic algorithms have been applied to a wide variety of combinatorial optimization problems and have proved their efficiency. However, prematurely convergence may lead to search stagnation on restricted regions of the search space. To deal with this drawback and beside the good performances attained by local search procedures, a genetic local search algorithm for solving the RCPSP is proposed. Simulation results demonstrate that the proposed GLSA provides an effective and efficient approach for solving RCPSP.
{"title":"Solving resource-constrained project scheduling problem by a genetic local search approach","authors":"O. Dridi, S. Krichen, A. Guitouni","doi":"10.1109/ICMSAO.2013.6552544","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552544","url":null,"abstract":"The resource-constrained project scheduling problem is a general scheduling problem which involving activities need to be scheduled such that the makespan is minimized. However, the RCPSP is confirmed to be an NP-hard combinatorial problem. Restated, it is hard to be solved in a reasonable computational time. Therefore, numerous metaheuristics-based approaches have been developed for finding near-optimal solution for RCPSP. Genetic algorithms have been applied to a wide variety of combinatorial optimization problems and have proved their efficiency. However, prematurely convergence may lead to search stagnation on restricted regions of the search space. To deal with this drawback and beside the good performances attained by local search procedures, a genetic local search algorithm for solving the RCPSP is proposed. Simulation results demonstrate that the proposed GLSA provides an effective and efficient approach for solving RCPSP.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"22 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":"129458771","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.2013.6552707
F. Rahmoune, M. Radjef, Sofiane Ziani
In the present paper we investigate the equilibrium customer behavior in a single server Markovian M2/M/1 queue with batch arrivals of two customers. We examine the various cases with respect to the level of information available to customers before they make this decision. More specifically, at their arrival epoch, the customers may or may not know the number of customers present in the system. In each of the two cases (observable case and unobservable case), we define the corresponding game, characterize customer equilibrium strategies, analyze the stationary behavior of the corresponding system. We also explore the effect of the information level on the equilibrium behavior and the social benefit via numerical comparisons. In other words, we analyze if the server is motivated to reveal information about the system state to the customers.
{"title":"Equilibrium customers strategies in a single server M2/M/1 queue","authors":"F. Rahmoune, M. Radjef, Sofiane Ziani","doi":"10.1109/ICMSAO.2013.6552707","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552707","url":null,"abstract":"In the present paper we investigate the equilibrium customer behavior in a single server Markovian M2/M/1 queue with batch arrivals of two customers. We examine the various cases with respect to the level of information available to customers before they make this decision. More specifically, at their arrival epoch, the customers may or may not know the number of customers present in the system. In each of the two cases (observable case and unobservable case), we define the corresponding game, characterize customer equilibrium strategies, analyze the stationary behavior of the corresponding system. We also explore the effect of the information level on the equilibrium behavior and the social benefit via numerical comparisons. In other words, we analyze if the server is motivated to reveal information about the system state to the customers.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"3 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":"131318319","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.2013.6552701
Sondes Fayech, N. Essoussi, M. Limam
Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machines (SVM), to focus on the prediction of the secondary structures. In this paper a new method, DM-pred, was proposed based on a protein clustering method to detect homologous sequences, a sequential pattern mining method to detect frequent patterns, features extraction and quantification approaches to prepare features and SVM method to predict structures. When tested on the most popular secondary structure datasets, DM-pred achieved a Q3 accuracy of 78.20% and a SOV of 76.49% which illustrates that it is one of the top range methods for protein secondary structure prediction.
{"title":"Data mining techniques to predict protein secondary structures","authors":"Sondes Fayech, N. Essoussi, M. Limam","doi":"10.1109/ICMSAO.2013.6552701","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552701","url":null,"abstract":"Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machines (SVM), to focus on the prediction of the secondary structures. In this paper a new method, DM-pred, was proposed based on a protein clustering method to detect homologous sequences, a sequential pattern mining method to detect frequent patterns, features extraction and quantification approaches to prepare features and SVM method to predict structures. When tested on the most popular secondary structure datasets, DM-pred achieved a Q3 accuracy of 78.20% and a SOV of 76.49% which illustrates that it is one of the top range methods for protein secondary structure prediction.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"21 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":"126866344","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.2013.6552628
A. Smiti, Zied Elouedi
The success of the Case Based Reasoning system depends on the quality of case data and the speed of the retrieval process that can be expensive in time especially when the number of cases gets large. To guarantee this quality, maintaining the contents of a case base becomes necessary. This paper presents two case base maintenance methods. They are mainly based on the idea that the clustering analysis to a large case base can efficiently build new case bases, which are smaller in size and can easily use simpler maintenance operations. One of method is based on partitioning clustering technique and the other one on density clustering technique. Experiments are provided to show the effectiveness of our methods taking into account the performance criteria of the case base. In addition, we support our empirical evaluation with using a new criterion called “competence” in order to show the efficiency of our methods in building high-quality case bases while preserving the competence of the case bases.
{"title":"Using clustering for maintaining case based reasoning systems","authors":"A. Smiti, Zied Elouedi","doi":"10.1109/ICMSAO.2013.6552628","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552628","url":null,"abstract":"The success of the Case Based Reasoning system depends on the quality of case data and the speed of the retrieval process that can be expensive in time especially when the number of cases gets large. To guarantee this quality, maintaining the contents of a case base becomes necessary. This paper presents two case base maintenance methods. They are mainly based on the idea that the clustering analysis to a large case base can efficiently build new case bases, which are smaller in size and can easily use simpler maintenance operations. One of method is based on partitioning clustering technique and the other one on density clustering technique. Experiments are provided to show the effectiveness of our methods taking into account the performance criteria of the case base. In addition, we support our empirical evaluation with using a new criterion called “competence” in order to show the efficiency of our methods in building high-quality case bases while preserving the competence of the case bases.","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":"127726841","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.2013.6552665
A. Adamou-Mitiche, L. Mitiche, V. Sima
We use the two-dimensional windowing method to design a digital 2D-FIR filter with linear phase, circularly symmetric with respect to the origin of the frequency plane. To get an economical filter with high information efficiency, an interesting way is applying the balanced realization method to this full-order filter. As a result, a linear phase IIR filter is obtained whose frequency response is very close to that of the initial filter.
{"title":"On the synthesis of digital two dimensional filters based on FIR filters approximation","authors":"A. Adamou-Mitiche, L. Mitiche, V. Sima","doi":"10.1109/ICMSAO.2013.6552665","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552665","url":null,"abstract":"We use the two-dimensional windowing method to design a digital 2D-FIR filter with linear phase, circularly symmetric with respect to the origin of the frequency plane. To get an economical filter with high information efficiency, an interesting way is applying the balanced realization method to this full-order filter. As a result, a linear phase IIR filter is obtained whose frequency response is very close to that of the initial filter.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"1 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":"127304940","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.2013.6552562
Salma Mezghani, H. Chabchoub, B. Aouni
The bin packing problem (BPP) a have many practical applications. The general single-objective formulation consists of allocating all objects in the minimum number of bins. However, BPP can be seen as a bi-objectives problem where the following objectives can be optimized simultaneously, the total cost and conflicts among the items within the bins. These objectives are conflicting. Their aggregation requires some compromise from the managers. In this paper, we will be proposing a goal programming model and the satisfaction functions to aggregate the objectives and explicitly integrates the manager's preferences.
{"title":"Manager's preferences in the Bi-Objectives Bin Packing Problem","authors":"Salma Mezghani, H. Chabchoub, B. Aouni","doi":"10.1109/ICMSAO.2013.6552562","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552562","url":null,"abstract":"The bin packing problem (BPP) a have many practical applications. The general single-objective formulation consists of allocating all objects in the minimum number of bins. However, BPP can be seen as a bi-objectives problem where the following objectives can be optimized simultaneously, the total cost and conflicts among the items within the bins. These objectives are conflicting. Their aggregation requires some compromise from the managers. In this paper, we will be proposing a goal programming model and the satisfaction functions to aggregate the objectives and explicitly integrates the manager's preferences.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"24 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":"130504735","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.2013.6552560
M. Qais, Zeyad AbdulWahid
In this paper, we introduced some modifications in the standard particles swarm optimization algorithm to get better results. We modified the velocity equation by inserting triangular functions (cosine and sine), increasing inertia weight and introducing a new method to avoid the stagnation problem. The modified algorithm named as Triangular Particle Swarm Optimization (TriPSO) was tested by five well-known benchmark functions (Sphere, Ackley, Rastrigin, Rosenbrock and Schwefel p2.26). The obtained results are compared with those of standard PSO and different published improved PSO algorithms (SPSO, PSO-XD, CPSO-S and PSO-P5), the comparison showed that TriPSO has the best results.
{"title":"A new method for improving particle swarm optimization algorithm (TriPSO)","authors":"M. Qais, Zeyad AbdulWahid","doi":"10.1109/ICMSAO.2013.6552560","DOIUrl":"https://doi.org/10.1109/ICMSAO.2013.6552560","url":null,"abstract":"In this paper, we introduced some modifications in the standard particles swarm optimization algorithm to get better results. We modified the velocity equation by inserting triangular functions (cosine and sine), increasing inertia weight and introducing a new method to avoid the stagnation problem. The modified algorithm named as Triangular Particle Swarm Optimization (TriPSO) was tested by five well-known benchmark functions (Sphere, Ackley, Rastrigin, Rosenbrock and Schwefel p2.26). The obtained results are compared with those of standard PSO and different published improved PSO algorithms (SPSO, PSO-XD, CPSO-S and PSO-P5), the comparison showed that TriPSO has the best results.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"1 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":"130653142","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.2013.6552643
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.2013.6552609
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}
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