Application of an adaptive neural fuzzy inference system to thermal comfort and group technology problems

AbstractThe Adaptive Neural Fuzzy Inference System (ANFIS) is used to design two vague systems, namely thermal comfort and group technologies in production and operations management. Results show that both systems can be modeled successfully by the combined use of a fuzzy approach and neural network learning

Application of invariant imbedding to the estimation of process duration

AbstractThis work deals with the application of invariant imbedding to solve a particularly important design problem, namely, the duration of the process. A numerical example is used to illustrate the approach. The advantage of this approach is its straightforward nature and uses only the usual design data. It avoids any iterations and thus no convergence problems need to be considered

Fuzzy clustering in cell formation with multiple attributes

AbstractAn approach based on fuzzy clustering and aggregation operators is proposed to design cell formation involving multiple criteria or multiple attributes. The three most basic attributes in cell formation, namely, number of machines required, processing time, and common tools required on machines, are considered. The results are compared with the single attribute results of Chu and Hayya (1991) [27]

Effective program planning for multiple projects under limited resources

AbstractThis work deals with an analytical approach to effective planning based upon the relative economics of projects contained in a program. A mathematical model is formulated to maximize the total return over the planning horizon subject to various annual cash flow and resource constraints. Stochastic mixed-integer programming is applied to determine the optimal sequence of project planning activities. To illustrate the use of the proposed procedure, a numerical example is solved using the mathematical programming software package SCICONIC/VM in a VAX-11/750 computer system