Article: Two phase approach for performance analysis and optimisation of industrial systems using uncertain data Journal: International Journal of Operational Research (IJOR) 2018 Vol.31 No.1 pp.88 - 111 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Komal; S.P. Sharma

Addresses: Department of Mathematics, School of Physical Sciences, Doon University, Dehradun-248001, Uttarakhand, India ' Department of Mathematics, Indian Institute of Technology Roorkee (IITR), Roorkee-247667, Uttarakhand, India

Abstract: 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.

Keywords: performance optimisation; system behaviour; reliability indices; GABLT technique; fuzzy multi-objective optimisation problem; FMOOP; genetic algorithm.

DOI: 10.1504/IJOR.2018.088558

International Journal of Operational Research, 2018 Vol.31 No.1, pp.88 - 111

Received: 27 Mar 2014
Accepted: 30 Mar 2015
Published online: 12 Dec 2017 *

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

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