Group decision-making model with incomplete fuzzy preference relations based on additive consistency.

In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree.     

An approach for automatic generation of adaptive hypermedia in education with multilingual knowledge discovery techniques

This work describes a framework that combines techniques from Adaptive Hypermedia and Natural Language processing in order to create, in a fully automated way, on-line information systems from linear texts in electronic format, such as textbooks. The process is divided into two steps: an off-line processing step, which analyses the source text, and an on-line step, which executes when a user connects to the system with a web browser, moment at which the contents and hyperlinks are generated. The framework has been implemented as the Welkin system, which has been used to build three adaptive on-line information sites in a quick and easy way. Some controlled experiments have been performed with real users aimed to provide positive feedback on the implementation of the system.     

选择正确的MOSFET:工程师所需要知道的

随着制造技术的发展和进步,系统设计人员必须跟上技术的发展步伐,才能为其设计挑选最合适的电子器件.MOSFET是电气系统中的基本部件,工程师需要深入了解它的关键特性及指标才能做出正确选择.本文将讨论如何根据RDS(ON)、热性能、雪崩击穿电压及开关性能指标来选择正确的MOSFET.     

Iterative Heuristic Response Surface Method for Transmission Expansion Planning

Transmission expansion planning has become a central challenge in the design of competitive electricity markets. The underlying optimization/decision problem exhibits a high level of complexity, being nonconvex, dynamic, and nonlinear, with multiple objective functions and a wide range of uncertainties. In this paper we propose a methodology for handling the expansion problem that involves a novel adaptation of an iterative optimization method based on response surface models already used successfully in the design and manufacture of integrated circuits. It is implemented in a distributed environment using Web services, and is validated and compared with a genetic algorithm based methodology. An application of the proposed methodology to the Chilean Central Interconnected System is then presented and analyzed. The results demonstrate its potential in the fields of risk analysis and decision support systems in power markets.     

Scheduling problems with two competing agents to minimized weighted earliness–tardiness

We study scheduling problems with two competing agents, sharing the same machines. All the jobs of both agents have identical processing times and a common due date. Each agent needs to process a set of jobs, and has his own objective function. The objective of the first agent is total weighted earliness–tardiness, whereas the objective of the second agent is maximum weighted deviation from the common due date. Our goal is to minimize the objective of the first agent, subject to an upper bound on the objective value of the second agent. We consider a single machine, and parallel (both identical and uniform) machine settings. An optimal solution in all cases is shown to be obtained in polynomial time by solving a number of linear assignment problems. We show that the running times of the single and the parallel identical machine algorithms are O(n^m+3), where n is the number of jobs and m is the number of machines. The algorithm for solving the problem on parallel uniform machine requires O(n^m+3m³) time, and under very reasonable assumptions on the machine speeds, is reduced to O(n^m+3). Since the number of machines is given, these running times are polynomial in the number of jobs.     

TWO GRAMMATICAL INFERENCE APPLICATIONS IN MUSIC PROCESSING

This article reviews our work in the field of music processing (MP) using grammatical inference (GI), where regular grammars are used for modeling musical style. These models can be used to generate automatic composition (AC) and classify music by style (musical style identification) with their resulting applications. The latter, for instance, would improve content-based retrieval in multimedia databases, joining indexing by musical style to other suitable indexes. In this work, several GI techniques are used to learn from examples of melodies, stochastic grammars for different musical styles. Then, each of the learned grammars is used to generate new melodies (composition) or to classify test melodies (style identification). Our studies in this field show the need of proper music coding schemes, so different coding schemes are presented and compared. Results from our previous studies have been improved, achieving in style identification a classification error rate that ranges from 0.5 to 1.7%, depending on the corpus used.     

Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems

In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.