Mohammad Ishak Desa

Kernel based regression and genetic algorithms for estimating cutting conditions of surface roughness in end milling machining process

We observe a surface roughness in end milling machining process which is influenced by machine parameters, namely radial rake angle, speed and feed rate cutting condition. In this machining, we need to minimize and to obtain as low as possible the surface roughness by determining the optimum values of the three parameters. In previous works, some researchers used a response surface methodology (RSM) and a soft-computing approach, which was based on ordinary linear regression and genetic algorithms (GAs), to estimate the minimum surface roughness and its corresponding values of the parameters. However, the construction of the ordinary regression models was conducted without considering the existence of multicollinearity which can lead to inappropriate prediction. Beside that it is known the relation between the surface roughness and the three parameters is nonlinear, which implies that a linear regression model can be inappropriate model to approximate it. In this paper, we present a technique developed using hybridization of kernel principal component analysis (KPCA) based nonlinear regression and GAs to estimate the optimum values of the three parameters such that the estimated surface roughness is as low as possible. We use KPCA based regression to construct a nonlinear regression and to avoid the effect of multicollinearity in its prediction model. We show that the proposed technique gives more accurate prediction model than the ordinary linear regressions approach. Comparing with the experiment data and RSM, our technique reduces the minimum surface roughness by about 45.3% and 54.2%, respectively.

Condition diagnosis of multiple bearings using adaptive operator probabilities in genetic algorithms and back propagation neural networks

Abstract Condition diagnosis of bearings is one of the most common plant maintenance activities in manufacturing industries. It is essential to detect bearing faults early to avoid unexpected breakdown of plant due to undetected faulty bearings. Many meta-heuristics techniques for condition diagnosis of single bearing systems have been developed. The techniques, however, are not effectively applicable for multiple bearing systems. In this paper, a new hybrid technique of genetic algorithms (GAs) with adaptive operator probabilities (AGAs) and back propagation neural networks (BPNNs), called AGAs–BPNNs, is proposed specifically for condition diagnosis of multiple bearing systems. In this technique, AGAs are integrated with BPNNs to attain better initial weights for the BPNNs and hence reduce their learning time. We tested the proposed technique on a two bearing systems, and used ten extracted features from the system’s vibration signals data as input and sixteen bearing condition classes as target output. The experimental results show that the AGAs–BPNNs technique obtains much higher classification accuracy in shorter CPU time and number of iterations compared with the standard BPNNs, and the hybrid of standard GAs and BPNNs.

A polar-based guided multi-objective evolutionary algorithm to search for optimal solutions interested by decision-makers in a logistics network design problem

In practical multi-objective optimization problems, respective decision-makers might be interested in some optimal solutions that have objective values closer to their specified values. Guided multi-objective evolutionary algorithms (guided MOEAs) have been significantly used to guide their evolutionary search direction toward these optimal solutions using by decision makers. However, most guided MOEAs need to be iteratively and interactively evaluated and then guided by decision-makers through re-formulating or re-weighting objectives, and it might negatively affect the algorithms performance. In this paper, a novel guided MOEA that uses a dynamic polar-based region around a particular point in objective space is proposed. Based on the region, new selection operations are designed such that the algorithm can guide the evolutionary search toward optimal solutions that are close to the particular point in objective space without the iterative and interactive efforts. The proposed guided MOEA is tested on the multi-criteria decision-making problem of flexible logistics network design with different desired points. Experimental results show that the proposed guided MOEA outperforms two most effective guided and non-guided MOEAs, R-NSGA-II and NSGA-II.

Improvement of adaptive GAs and back propagation ANNs performance in condition diagnosis of multiple bearing system using grey relational analysis

Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown. Conditions of bearings commonly are reflected by vibration signals data. In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis. However, large number of features extraction will increase the complexity of the diagnosis system. Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system. AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy. In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction. The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA.

The need of Information Systems (IS) Integration Complexity Model for IS integration project

There is a need to address issues related to IS integration since integration are essential to organizations to move forward and supporting their business functions. However, there is no tool available to assess the complexity of IS integration, before thinking of measuring the success of integration initiative. If we can device a framework that depict the scenario of IS integration in organisation, it will be the first step to assist organisation understanding the IS integration status, whether they are on the right track, knowing what they are lacking of to proceed with integration, and whether they are using the right technology as solution to their integration problems. Thus, a construction of complexity model that depict the IS integration scenarios is proposed to assist in assessment of IS integration project.

A non-linear model for the classification of stored items in supply chain management

With the significant role that warehouse plays in connection to suppliers, distributors, and clients, and considering the costliness of storage systems, efforts have focused on the reduction of procedural expenses. Among the most significant expenses, one involves the distance traveled by operators or S/R machinery for the selection of ordered items; the common solution to which has been through the classification of stored items. Previous articles have focused on linear solutions to the abovementioned problem, such as P-Median. This article shall focus on the problems arising from the said solution, and instead a non-linear method proposed. Further, a heuristic algorithm is produced for the solution that will be compared to similar solutions through Lingo, proving acceptable results.

Logistics and information technology: Previous research and future research expension

This paper discusses some of the research that has been done previously by researchers towards the issues of information technology in logistics. The purpose of this paper is to show that there is a lot of research that had been done by many researchers in logistics that related to information technology and can be seen as an opportunity to take into consideration to search any gap on it and discover any potential for future research expansion.