Dynamic adjustment of age distribution in Human Resource Management by genetic algorithms.

Adjustment of a given age distribution to a desired age distribution within a required time frame is dynamically performed for the purpose of Human Resource (HR) planning in Human Resource Management (HRM). The adjustment process is carried out by adding the adjustment magnitudes to the existing number of employees at the selected age groups on the yearly basis. A model of a discrete dynamical system is employed to emulate the evolution of the age distribution used under the adjustment process. Genetic Algorithms (GA) is applied for determining the adjustment magnitudes that influence the dynamics of the system. An interesting aspect of the problem lies in the high number of constraints; though the constraints are fundamental, they are considerably higher in number than in many other optimization problems. An adaptive penalty scheme is proposed for handling the constraints. Numerical examples show that GA with the utilized adaptive penalty scheme provides potential means for HR planning in HRM

Enhancing university research activities with knowledge management.

In the new economy, innovation is regarded as one of the solutions for almost every organisation to survive in the new business era. Universities, especially in terms of research activities, are no difference since they strive for novelties which potentially lead to innovation. An experienced researcher in the university has continually created tacit knowledge in a specific domain, but typically found it difficult to share this tacit knowledge among other researchers for the problem solving purpose. To overcome this problem and to better stimulate knowledge sharing activities among university researchers, Knowledge Management and Knowledge Engineering, particularly KADS, are utilised in this paper to assist a group of different domain researchers in putting their experiences together. In this way, each researcher can make explicit his or her tacit knowledge into KADS task, inference and domain knowledge models. The structured knowledge models captured from different researchers can then be merged together. In this paper, the research in Knowledge Management is selected as a case study, and the results show that the relevant tacit knowledge has been made explicit from a researcher and allow other researchers to share the knowledge as well as to add their own knowledge. Hence, their common research theme is effectively created, and also maintained by a group of researchers

Genetic algorithms-aided reliability analysis

A hybrid procedure of Genetic Algorithms (GAs) and reliability analysis is described, discussed, and summarized. The procedure is specifically referred to as a Genetic Algorithms-aided (GAs-aided) reliability analysis. Two classes of GAs, namely simple GAs and multimodal GAs, are introduced to solve a number of important problems in reliability analysis. The problems cover the determination of Point of Maximum Likelihood in failure domain (PML), the computation of failure probability using the GAs-determined PML, and the determination of multiple design points. The MCS-based method using the GAs-determined PML is specifically implemented in the so-called an Importance Sampling around PML (ISPML). The application of GAs to each respective problem is then demonstrated via numerical examples in order to clarify the procedures. With an aid from GAs, reliability analysis is possible even if there is no information about the geometry or landscape of limit state surfaces and the total number of crucial likelihood points. In addition, GAs significantly improve the computational efficiency and realize the analysis of rare events under constrained computational resources. The implementation of GAs to reliability analysis for building up the hybrid procedure is readily because of their algorithmic simplicity