Two phase approach for performance analysis and optimisation of industrial systems using uncertain data

In real life situation, it is difficult to achieve optimum performance of industrial systems for desired industrial goals using available resources. This is due to the complexity of industrial systems and nonlinearity in their behaviour. In this paper, a two-phase approach has been developed for achieving optimum performance of any industrial system. In the first phase of the presented approach, system performance is analysed in terms of six well known reliability indices by applying fuzzy lambda-tau (FLT) and genetic algorithms-based lambda-tau (GABLT) techniques utilising available uncertain data. In the second phase, a fuzzy multi-objective optimisation problem (FMOOP) has been formulated using first phase results. The obtained FMOOP is further reformulated to an equivalent crisp optimisation problem by taking care of preferences as suggested by decision maker (DM)/management personnel and then solved by using genetic algorithms (GA). The presented two-phase approach is applied to a bleaching system of a paper mill. A decision support system has been developed for achieving system optimum performance. The obtained results may be used for planning the future course of action to optimise system performance.