Investigation of multi-phase tubular permanent magnet linear generator for wave energy converters

In this article, an investigation into different magnetization topologies for a long stator tubular permanent magnet linear generator is performed through a comparison based on the cogging force disturbance, the power output, and the cost of the raw materials of the machines. The results obtained from finite element analysis simulation are compared with an existing linear generator described in [1]. To ensure accurate results, the generator developed in [1] is built with 3D CAD and simulated using the finite-element method, and the obtained results are verified with the source.The PRIMaRE project

An artificial immune algorithm for ergonomic product classification using anthropometric measurements

© 2016 Elsevier Ltd Product classification using anthropometric measurements leads to ergonomic product design and user satisfaction. We propose an effective artificial immune algorithm (AIA) to classify ergonomic products with multi-criteria anthropometric measurements and tune the AIA parameters with a full factorial experimental design approach. We demonstrate the applicability and efficacy of the proposed algorithm by considering the anthropometric measurements of the hand, developing an ergonomic computer mouse, and classifying consumers into three categories. The resulting classifications are compared with expert opinions to facilitate the conformity of the computer mouse to user requirements

An Integrated Fuzzy Framework for Analyzing Barriers to the Implementation of Continuous Improvement in Manufacturing

Purpose – Delivering premium services and quality products are critical strategies for success in manufacturing. Continuous improvement (CI), as an underlying foundation for quality management, is an ongoing effort allowing manufacturing companies to see beyond the present to create a bright future. We propose a novel integrated fuzzy framework for analyzing the barriers to the implementation of CI in manufacturing companies. Design/methodology/approach – We use the fuzzy failure mode and effect analysis (FMEA) and a fuzzy Shannon’s entropy to identify and weigh the most significant barriers. We then use fuzzy multi-objective optimization based on ratio analysis (MOORA), the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), and fuzzy simple additive weighting (SAW) methods for prioritizing and ranking the barriers with each method. Finally, we aggregate these results with Copeland’s method and extract the main CI implementation barriers in manufacturing. Findings – We show “low cooperation and integration of the team in CI activities” is the most important barrier in CI implementation. Other important barriers are “limited management support in CI activities,” “low employee involvement in CI activities,” “weak communication system in the organization,” and “lack of knowledge in the organization to implement CI projects.” Originality/value – We initially identify the barriers to the implementation of CI through rigorous literature review and then apply a unique integrated fuzzy approach to identify the most important barriers based on the opinions of industry experts and academics

A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria

The best–worst method (BWM) is a multiple criteria decision-making (MCDM) method for evaluating ≤a set of alternatives based on a set of decision criteria where two vectors of pairwise comparisons are used to calculate the importance weight of decision criteria. The BWM is an efficient and mathematically sound method used to solve a wide range of MCDM problems by reducing the number of pairwise comparisons and identifying the inconsistencies derived from the comparison process. In spite of its simplicity and efficiency, the BWM does not consider the decision-makers’ (DMs’) confidence in their pairwise comparisons. We propose a neutrosophic enhancement to the original BWM by introducing two new parameters as the DMs’ confidence in the best-to-others preferences and the DMs’ confidence in the others-to-worst preferences. We present two real-world cases to illustrate the applicability of the proposed neutrosophic enhanced BWM (NE-BWM) by considering confidence rating levels of the DMs

A SOA web-based group decision support system considering affective aspects

The topic of Group Decision Support Systems (GDSS) has been studied over the last decades. Supporting decision-makers that participate in group decision-making processes is a complex task, especially when decision-makers have no opportunity to gather at the same place and at the same time. In this work, we propose a Web based Group Decision Support System (WebGDSS) which intends to support decision-makers anywhere, anytime and through almost any kind of devices. Our system was developed under a SOA architecture and we used a multi criteria algorithm that features decision-makers’ cognitive aspects, as well as a component of generation of intelligent reports to feedback the results of decision-making processes to the decision-makers.This work was supported by GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) with the João Carneiro Ph.D. Grant with the Reference SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio