A Bibliographic Survey of the Activities and International Nature of Multiple Criteria Decision Making

Steuer, Ralph E.; Gardiner, Lorraine R.; Gray, J. A. Muir

doi:10.1002/(sici)1099-1360(199609)5:3<195::aid-mcda81>3.0.co;2-d

Cited by 56 publications

(25 citation statements)

References 53 publications

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“…The 16 refereed MCDM journals were selected according to two criteria: (1) journals appeared frequently in the Multiple Criteria Decision Aid bibliography on the International society on MCDM website [13]; (2) the most relevant and top-rated MCDM journals listed by [14] and [15]. Each article collected in the dataset is stored in Microsoft Excel as one row with five fields: article title, author(s), journal name, year of publication, and abstract.…”

Section: Data Sourcesmentioning

confidence: 99%

An Application of Latent Semantic Analysis for Text Categorization

Kou

Peng

2015

INT J COMPUT COMMUN

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It is a challenge task to discover major topics from text, which provide a better understanding of the whole corpus and can be regarded as a text categorization problem. The goal of this paper is to apply latent semantic analysis (LSA) approach to extract common factors that representing concepts hidden in a large group of text. LSA involves three steps: the first step is to set up a term-document matrix; the second step is to transform the term frequencies into a term-document matrix using various weighting schemes; the third step performs singular value decomposition (SVD) on the matrix to reduce the dimensionality. The reduced-order SVD is the best k-dimensional approximation to the original matrix. The experiment uses more than fifteen hundreds research paper abstracts from a specific field. Because different factor solutions of the LSA suggest different levels of aggregation, this work examines thirteen solutions in the experiment. The results show that LSA is able to identify not only principle categories, but also major themes contained in the text.

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Section: Data Sourcesmentioning

confidence: 99%

An Application of Latent Semantic Analysis for Text Categorization

Kou

Peng

2015

INT J COMPUT COMMUN

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“…Clearly it does not make sense to choose a solution that is not Pareto-optimal. A large number of references for MOP are available in the literature [104,105,108].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Planning and Optimization of Tracking Areas for Long TermEvolution Networks

Razavi¹

2014

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Along with the evolution of network technologies, user's expectations on performance and mobile services are rising rapidly. To fulfill customer demands, the operators are facing a great amount of network signaling overhead in terms of Location Management (LM), which tracks and pages User Equipments (UEs) in the network. Hence, sustaining a reliable and cost-efficient LM system for future mobile broadband networks has become one of the major challenges in mobile telecommunications. Tracking Area (TA) in Long Term Evolution (LTE), is a logical grouping of cells that manages and represents the locations of UEs. This dissertation deals with planning and optimization of TAs.TA design must be revised over time in order to adapt to changes and trends in UE location and mobility patterns. Re-optimization of a once optimized design subject to different cost budgets is one of the problems considered in this dissertation. By re-optimization, the design is successively improved by reassigning some cells to TAs other than their original ones. An algorithm based on repeated local search is developed in order to solve the resulting problem.The next topic of research is the trade-off between the performance in terms of the total signaling overhead of the network and the reconfiguration cost. This trade-off is modeled as a bi-objective optimization problem to which the solutions are characterized by Paretooptimality. Solving the problem delivers a host of potential tradeoffs, among which the selection can be based on the preferences of a decision-maker. An integer programming model and a genetic algorithm heuristic are developed for solving the problem in large-scale networks.In comparison to previous generations of cellular networks, LTE systems allow for a more flexible configuration of TA design by means of Tracking Area List (TAL). How to utilize this flexibility in applying TAL to large-scale networks is still an open problem. In this dissertation, three approaches for allocating and assigning TALs are presented, and their performances are compared with each other, as well as with the conventional TA scheme. Moreover, a linear programming model is developed to minimize the total signaling overhead of the network based on overlapping TALs.In this dissertation, the problem of mitigating signaling congestion is thoroughly studied both for the specific train scenario and also for the general network topology. For each signaling congestion scenario, a related linear programming model based on minimizing the maximum signaling due to tracking area update or paging is developed. As a major advantage of the modified overlapping TAL scheme iii for signaling congestion avoidance, information of individual UE mobility is not required.Automatic reconfiguration of LM is an important element in LTE. The network continuously collects UE statistics, and the management system adapts the network configuration to changes in UE distribution and demand. In this dissertation an evaluation of dynamic configuration of TA design, including ...

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“…Nevertheless, traditional combinatorial optimization considers a single objective to be optimized, while in many reallife applications decision makers have to deal with several, usually conflicting, objectives. The increasing interest to theory and methodology of multicriteria decision making (MCDM) over the last thirty years is witness of this fact (see for example Steuer et al (1996)). Surprisingly, the combination of both i.e.…”

Section: Introductionmentioning

confidence: 99%

Two-phase method and Lagrangian relaxation to solve the Bi-Objective Set Covering Problem

Prins¹,

Prodhon²,

Calvo³

2006

Ann Oper Res

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This paper deals with the Bi-Objective Set Covering Problem, which is a generalization of the well-known Set Covering Problem. The proposed approach is a two-phase heuristic method which has the particularity to be a constructive method using the primal-dual Lagrangian relaxation to solve single objective Set Covering problems. The results show that this algorithm finds several potentially supported and unsupported solutions. A comparison with an exact method (up to a medium size), shows that many Pareto-optimal solutions are retrieved and that the other solutions are well spread and close to the optimal ones. Moreover, the method developed compares favorably with the Pareto Memetic Algorithm proposed by Jaszkiewicz.

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A Bibliographic Survey of the Activities and International Nature of Multiple Criteria Decision Making

Cited by 56 publications

References 53 publications

An Application of Latent Semantic Analysis for Text Categorization

An Application of Latent Semantic Analysis for Text Categorization

Planning and Optimization of Tracking Areas for Long TermEvolution Networks

Two-phase method and Lagrangian relaxation to solve the Bi-Objective Set Covering Problem

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