Juan A. Carretero

Differential Evolution for learning the classification method PROAFTN

This paper introduces a new learning technique for the multicriteria classification method PROAFTN. This new technique, called DEPRO, utilizes a Differential Evolution (DE) algorithm for learning and optimizing the output of the classification method PROAFTN. The limitation of the PROAFTN method is largely due to the set of parameters (e.g., intervals and weights) required to be obtained to perform the classification procedure. Therefore, a learning method is needed to induce and extract these parameters from data. DE is an efficient metaheuristic optimization algorithm based on a simple mathematical structure to mimic a complex process of evolution. Some of the advantages of DE over other global optimization methods are that it often converges faster and with more certainty than many other methods and it uses fewer control parameters. In this work, the DE algorithm is proposed to inductively obtain PROAFTNs parameters from data to achieve a high classification accuracy. Based on results generated from 12 public datasets, DEPRO provides excellent results, outperforming the most common classification algorithms.

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Abstract: The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.

The integration of quality management and continuous improvement methodologies with management systems

For organisations to be successful, the use of well-structured management systems (MSs), quality management (QM) approach and methodologies for continuous improvement (CI) are all essential. Total quality management (TQM) has been a dominant management concept for CI utilising Demings concepts of Plan-Do-Check-Act (PDCA). Lean Six Sigma (LSS) is a widely-accepted methodology for CI considered among most modern in the 2000s. Recently, different MSs have gained more attention as they form critical infrastructure for improving and controlling different operating areas of any organisation. In many industries, CI methodologies and MSs are separately implemented, either formally or informally. The lack of their proper integration is one of the main reasons why lots of implementation efforts of CI fail, since it ensures alignment of activities and provides industry with competitive advantage. Thus, the need and benefits for formulating such integration of QM and CI with a comprehensive MS are discussed in this study.

On the convergence and origin bias of the Teaching-Learning-Based-Optimization algorithm

Graphical abstractThe effects of origin bias on the Teaching-Learning-Based-Optimization algorithm. Display Omitted HighlightsA geometric interpretation is applied to explain population convergence.A property of TLBO introduces an origin bias in the Teacher Phase.A converged population will continue searching in the direction of the origin for better solutions.The origin bias is directly tied to convergence.A faster rate of convergence yields a higher success rate for problematic functions. Teaching-Learning-Based-Optimization (TLBO) is a population-based Evolutionary Algorithm which uses an analogy of the influence of a teacher on the output of learners in a class. TLBO has been reported to obtain very good results for many constrained and unconstrained benchmark functions and engineering problems. The choice for TLBO by many researchers is partially based on the study of TLBOs performance on standard benchmark functions. In this paper, we explore the performance on several of these benchmark functions, which reveals an inherent origin bias within the Teacher Phase of TLBO. This previously unexplored origin bias allows the TLBO algorithm to more easily solve benchmark functions with higher success rates when the objective function has its optimal solution as the origin. The performance on such problems must be studied to understand the performance effects of the origin bias. A geometric interpretation is applied to the Teaching and Learning Phases of TLBO. From this interpretation, the spatial convergence of the population is described, where it is shown that the origin bias is directly tied to spatial convergence of the population. The origin bias is then explored by examining the performance effect due to: the origin location within the objective function, and the rate of convergence. It is concluded that, although the algorithm is successful in many engineering problems, TLBO does indeed have an origin bias affecting the population convergence and success rates of objective functions with origin solutions. This paper aims to inform researchers using TLBO of the performance effects of the origin bias and the importance of discussing its effects when evaluating TLBO.

Implementation of Lean Six Sigma (LSS) in supply chain management (SCM): an integrated management philosophy

Supply chain management (SCM) is essential for any company to survive the increasing pressures of global competition. There have been continuous changes in global manufacturing and service markets, causing supply chain (SC) members to reassess their effectiveness individually and as a whole. A new evolution in quality management (QM) is Lean Six Sigma (LSS), which is a continuous improvement (CI) methodology that aims at customer satisfaction and system waste reduction. SCM can utilise the QM concepts as well as the LSS tools and CI principles to achieve high levels of customer satisfaction regarding cost, quality and delivery. Researchers have considered the integration of Lean and Six Sigma with SCM. This research extends the previous works and proposes the implementation of LSS in SCM. A case study provides an example of how LSS, utilising value stream mapping (VSM), can be used to improve an SC.

Automatic parameter settings for the PROAFTN classifier using hybrid particle swarm optimization

In this paper, a new hybrid metaheuristic learning algorithm is introduced to choose the best parameters for the classification method PROAFTN PROAFTN is a multi-criteria decision analysis (MCDA) method which requires values of several parameters to be determined prior to classification These parameters include boundaries of intervals and relative weights for each attribute The proposed learning algorithm, identified as PSOPRO-RVNS as it integrates particle swarm optimization (PSO) and Reduced Variable Neighborhood Search (RVNS), is used to automatically determine all PROAFTN parameters The combination of PSO with RVNS allows to improve the exploration capabilities of PSO by setting some search points to be iteratively re-explored using RVNS Based on the generated results, experimental evaluations show that PSOPRO-RVNS outperforms six well-known machine learning classifiers in a variety of problems.

A New Method to Calculate the Force and Moment Workspaces of Actuation Redundant Spatial Parallel Manipulators

A new method for obtaining the force and moment workspaces of spatial parallel manipulators (PMs) is presented. Force and moment workspaces are regions within which a manipulator can sustain/apply at least a certain value of force or moment in all directions. Here, the force and moment workspaces are found using a method, which explicitly sets the largest possible number of actuators to their maximum limits ensuring that the manipulator is performing at its best possible wrench capabilities. Two cases for obtaining these workspaces are used. The first gives the applicable/sustainable force with a prescribed moment whereas the second one gives the applicable/sustainable moment with a prescribed force. For illustration purposes, the method is applied to a six-degree-of freedom (DOF) redundantly-actuated spatial PM, the 3- RRR S. The results are represented graphically as the boundaries of the workspace in the three-dimensional Cartesian space. These workspaces can be used as a powerful tool for path/task planning and PM design.

A Concept for Docking a UUV With a Slowly Moving Submarine Under Waves

Docking an unmanned underwater vehicle (UUV) with a submerged submarine in littoral waters in high sea states requires more dexterity than either the submarine or streamlined UUV possess. The proposed solution uses an automated active dock to correct for transverse relative motion between the vehicles. Acoustic, electromagnetic, and optical sensors provide position sensing redundancy in unpredictable conditions. The concept is being evaluated by building and testing individual components to characterize their performance, errors, and limitations, and then simulating the system to establish its viability at low cost.

Comparing Cognitive Efficiency of Experienced and Inexperienced Designers in Conceptual Design Processes

Design cognition research aims to investigate the cognitive mechanisms and thought processes of human designers. In previous research, the cognitive activity of experienced and inexperienced designers has been compared in order to identify design strategies leading to design creativity. However, it is still unknown whether the design strategies applied are effective and whether the design processes are efficiently improved. In this paper, cognitive efficiency, describing how designers optimize mental resources to achieve creativity in conceptual design processes, was directly measured by the mental effort of designers and the creativity level of design outcomes. The results showed that the experienced designers generated more design concepts with higher quality and variety than did the inexperienced designers. The cognitive efficiency measures indicated that design expertise contributed to improving cognitive efficiency scores of quality. In addition, the systematic design method used by some designers was found to be related to high cognitive efficiency. It can be seen that the evaluation of cognitive efficiency has practical applications for designer training, design methodology evaluation, and design process improvement.

Six Sigma and Total Quality Management (TQM): similarities, differences and relationship

Industries are continuously facing fierce competition and the challenge of meeting increasing demands for higher quality products at economic costs. The success of an organisation is directly related to how effective its implementation of continuous improvement (CI) is. For any manufacturing system, Total Quality Management (TQM) and Six Sigma are important CI methodologies. Effective understanding of these methodologies and their relationship will provide an industry with a competitive advantage. Many industrial organisations today are using either TQM or Six Sigma as the core for their CI efforts. There is a lot of dispute on which methodology is superior, how they relate to each other, what the common grounds are and what their differences are. As such, the relationship between TQM and Six Sigma is worth further investigation. In this paper, TQM and Six Sigma are introduced followed by a thorough comparison. More particularly, this work investigates their similarities, differences and how they relate to each other. Finally, this research introduces how they fit together in order to develop a new structure for integrating them together which will provide an improved approach for CI.