Integrated cell formation and layout (CFLP) is an extended application of the group technology philosophy in which machine cells and cell layout are addressed simultaneously. The aim of this technological innovation is to improve both productivity and flexibility in modern manufacturing industry. However, due to its combinatorial complexity, the cell formation and layout problem is best solved by heuristic and metaheuristic approaches. As CFLP is prevalent in manufacturing industry, developing robust and efficient solution methods for the problem is imperative. This study seeks to develop a fuzzy simulated evolution algorithm (FSEA) that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. Deriving from the classical simulated evolution algorithm, the search efficiency of the major phases of the algorithm is enhanced, including initialization, evaluation, selection and reconstruction. Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. It is anticipated in this study that the application of the algorithm can be extended to other complex combinatorial problems in industry.

The development of human resources recruitment and training strategies in a dynamic environment poses a challenge to many policy makers in various organisations. The goal of every human resource manager is to recruit, train and deploy the right personnel at the right place and at the right time in order to meet organizational requirements. We develop a system dynamics simulation model that captures the dynamic behaviour of a typical corporate manpower system. Three major strategies are indentified and simulated under different manpower demand scenarios. Based on a set of performance indices, the impact of the strategies is simulated under assumed demand scenarios including steady increasing, fluctuating, and s-shaped demand. Useful managerial insights are derived from the study. The model is a decision support tool for developing reliable dynamic manpower policies in terms of recruitment, training capacity, available skills, and attrition. This approach can assist organizations to design effective manpower strategies.

Renewable energy technologies (RET) have faced a number of constraints that have affected their rate of adoption. This can be attributed to the presence of complex dynamic factors associated with the technology adoption process. This paper simulates the dynamic behaviour of the RET adoption process, from a systems dynamics point of view. Complex dynamic interactions between technology adopters, policy makers and policies are captured based on systems thinking concepts. Based on a set of input policy parameters and variables, the behaviour of RET adoption is investigated and analyzed. Sensitivity experiments and further “what-if” experiments are carried out to obtain in-depth understanding of RET adoption process. Useful managerial insights are drawn from the simulation results, relevant to decision makers concerned with renewable energy technology innovations and their adoption.