Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

Multi-objective optimization in machining of GFRP and MMC composites: two case experimental research

Composite materials like GFRP and MMCs having more importance in various manufacturing industries mainly in aerospace and automotive industries and many engineering application, because of their unique mechanical properties as compare to the conventional material. Drilling is the most common machining process in manufacturing industries for assembly of components but drilling of composite may possesses many difficulties such as fiber pull out, delamination and circularity etc. which affects the quality of drilled hole. To overcome these difficulties the effect of machining parameters on different machining responses should be investigated for attaining high product quality as well as satisfactory machining process performance. Therefore, the main objective of this dissertation is to investigate the various machining performance characteristics with different machining condition in drilling of GFRP and MMCs composites by using various integrated multi objective optimization methodologies. In this presented thesis, Deng’s similarity method integrated with Taguchi, TOPSIS integrated with Taguchi method (in drilling of GFRP composite) and PCA-Grey method integrated with Taguchi, Grey-TOPSIS Integrated with Taguchi method (in drilling of MMCs), have been implemented for obtaining the optimal machining conditions