Industrial management
Mohsen Kochaki; Behnam Vahdani
Abstract
The correct storage and arrangement of products in the warehouse increases the efficiency in responding to requests, accelerates the identification of products, increases the accessibility of the items in the warehouse, makes more use of the available space in the warehouse, determines the position of ...
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The correct storage and arrangement of products in the warehouse increases the efficiency in responding to requests, accelerates the identification of products, increases the accessibility of the items in the warehouse, makes more use of the available space in the warehouse, determines the position of the products in the warehouse and does not damage it. maximum flexibility and optimal storage conditions are provided. By examining the studies that have been conducted in the field of warehousing and arrangement of products in the warehouse, and before arranging the products in the warehouse, comprehensively and comprehensively, according to the characteristics of the goods, they have not been categorized. Therefore, in this article, by using machine learning algorithms and data mining, according to the characteristics that are considered for the goods in the warehouse of Farasan industrial-production factory, the products are categorized and then the products are arranged in the warehouse using We have paid mathematical programming model. The purpose of the investigated problem in the field of warehousing and arrangement of products, in addition to categorizing products based on their characteristics, is to minimize the cost function obtained in relation to the mathematical planning model. Therefore, for the category Classification of goods is done using density-based algorithm (DB), self-organizing mapping neural network (SOM) and hierarchical clustering method (AGNES). The obtained results show that the SOM network has a better performance than the density-based algorithm and also the density-based algorithm has a better performance than the hierarchical clustering method.
Amir Daneshvar; Mostafa Zandieh; Jamshid Nazemi
Volume 13, Issue 39 , January 2016, , Pages 1-34
Abstract
Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the ...
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Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming.Preference Disaggregation approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline.Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all , ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by inferred parameters. Experimental analysis on credit data shows high quality and competitive results compared with some standard classification methods.
Jamshid Salehi Sadaghiani; Seyed Amir Reza Abtahi
Volume 4, Issue 13 , June 2006, , Pages 89-122
Abstract
The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty.
In this paper, we describe the ...
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The purpose of this article is about soft computing and its different methods for modeling phenomena. Soft Computing refers to the evolving collection of methodologies used to build intelligent systems exhibiting human-like reasoning and capable of tackling uncertainty.
In this paper, we describe the neural networks approach in soft computing at first. Then, other approaches such as genetic algorithm and machine learning will be described. Since the main goal of building the model is knowledge extraction, finally, we will describe the various methods of knowledge and rule extraction from neural networks.
