scholarly journals Joint X- and S2 Control Charts Optimal Design Using Genetic Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Genetic Algorithm ◽  
Control Charts ◽  
Weighted Average ◽  
Statistical Design ◽  
Type I ◽  
Membership Functions ◽  
Control Cost ◽  
Weighted Average Method ◽  
Fuzzy Membership Functions ◽  
Flexible Model

A simple and flexible model for economic statistical design of joint X- and S2 control charts was proposed. The design problem was approached by constrained fuzzy multiobjective modeling for three objectives: joint power, joint Type I error, and joint total control cost. Fuzzy membership functions were created to measure the satisfaction levels of the objectives, and the overall satisfaction level of the design was calculated using a weighted-average method. A genetic algorithm was designed to solve this model. The strength of this model lies in its effectiveness in detecting the assignable causes through the joint design and in its simplicity and flexibility in dealing with uncertainties in the design.

Research on Optimization Model for Power Coal Blending Based on Genetic Algorithm

2005 ◽  
Author(s):  
Yanfen Liao ◽  
Changhong Wu ◽  
Xiaoqian Ma
Keyword(s):  
Genetic Algorithm ◽  
Average Method ◽  
Power Plants ◽  
Weighted Average ◽  
Weighted Average Method ◽  
Blended Coal ◽  
Coal Blending ◽  
Two Factors ◽  
Great Pressure ◽  
Coal Properties

The slagging process is a popular problem in coal-fired power plants because the coal properties deviate from designed condition, at the same time, power plants is enduring a great pressure with the increasing of coal prices. Power coal blending provides an effective way to solve these two problems. In some traditional methods, blended-coal properties were usually treated by the weighted average method which induced the optimization solutions deviating from the actual results. The reason is that different coal property indexes are based on different benchmarks; for example, the sulphur content in coal is based on applied basis, while the slagging properties of blended-coal are calculated on air-dried basis, which was influenced by the contents of moisture and ash in each coal. In order to study the effects, based on the genetic algorithm, a model considering these two factors was build up to optimum the coal-blending scheme. Compared with the traditional weighted average method, the new model got higher slagging property indexes, as means the former method may include some coal blending schemes into the optimizing process, in which the real slagging parameters go beyond constraint standards. Therefore, in the case of coal-blending optimization to prevent slagging in furnace, these two factors are especially important and should be considered carefully to ensure the precise of slagging parameters, so as to obtain the optimum results both in the prices of coals and in slagging property.

scholarly journals A Novel Nonadditive Collaborative-Filtering Approach Using Multicriteria Ratings

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yi-Chung Hu
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Average Method ◽  
Choquet Integral ◽  
Weighted Average ◽  
Multicriteria Decision Making ◽  
Rating Systems ◽  
Group Buying ◽  
Weighted Average Method ◽  
Filtering Approach

Although single-criterion recommender systems have been successfully used in several applications, multicriteria rating systems which allow users to specify ratings for various content attributes of individual items are gaining importance in recommendation context. An overall rating of an unrated item is often obtained by the weighted average method (WAM) when criterion weights are available. However, the assumption of additivity for the WAM is not always reasonable. For this reason, this paper presents a new collaborative-filtering approach using multicriteria ratings, in which a nonadditive technique in Multicriteria decision making (MCDM), namely, the Choquet integral, is used to aggregate multicriteria ratings for unrated items. Subsequently, the system can recommend items with higher overall ratings for each user. The degrees of importance of the respective criteria are determined by a genetic algorithm. In contrast to the additive weighted average aggregation, the Choquet integral does not ignore the interaction among criteria. The applicability of the proposed approach to the recommendation of the initiators on a group-buying website is examined. Experimental results demonstrate that the generalization ability of the proposed approach performs well compared with other similarity-based collaborative-filtering approaches using multicriteria ratings.

scholarly journals Fuzzy logic-based moving obstacle avoidance method

2019 ◽  
Vol 9 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Suat Karakaya ◽  
Hasan Ocak
Keyword(s):  
Fuzzy Logic ◽  
Average Method ◽  
Dynamic Environment ◽  
Weighted Average ◽  
Rule Base ◽  
Control Input ◽  
Membership Functions ◽  
Weighted Average Method ◽  
Moving Obstacles ◽  
Moving Obstacle

In this study, a fuzzy logic-based collision avoidance method is studied. Mobile robots operating in a dynamic environment may encounter moving obstacles. The distance from the obstacles to the robot is provided as a control input. The change of distance is provided as the secondary input. The output speed value is expressed as ‘stop’, ‘very slow’, ‘slow’, ‘medium’ and ‘fast’, respectively. Each of these expressions has an exact numerical value, but it depends on the inputs and the rule base, and therefore the numerical values belong to the variable ratios. The input parameters do not have an exact value and they are assigned to different numerical ranges at different ratios. The input membership functions defined in this study are defined as triangular functions. The defuzzification process of the output parameter is performed by the weighted average method. Keywords: fuzzy; logic-based; mobile; avoidance method

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