Monica Rani

Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment

We present a multi-objective reliability optimization problem using intuitionistic fuzzy optimization.Reliability is considered as a triangular fuzzy number during formulation.Exponential membership and quadratic nonmembership functions are used for defining their fuzzy goals.We utilize the PSO algorithm to the solve the optimization problem.Examples are shown to illustrate the method. In designing phase of systems, design parameters such as component reliabilities and cost are normally under uncertainties. This paper presents a methodology for solving the multi-objective reliability optimization model in which parameters are considered as imprecise in terms of triangular interval data. The uncertain multi-objective optimization model is converted into deterministic multi-objective model including left, center and right interval functions. A conflicting nature between the objectives is resolved with the help of intuitionistic fuzzy programming technique by considering linear as well as the nonlinear degree of membership and non-membership functions. The resultants max-min problem has been solved with particle swarm optimization (PSO) and compared their results with genetic algorithm (GA). Finally, a numerical instance is presented to show the performance of the proposed approach.

An approach for analyzing the reliability of industrial systems using soft-computing based technique

The purpose of this paper is to present a novel technique for analyzing the behavior of an industrial system by utilizing vague, imprecise, and uncertain data. In this, two important tools namely traditional Lambda-Tau and artificial bee colony algorithm have been used to build a technique named as an artificial bee colony (ABC) algorithm based Lambda-Tau (ABCBLT). In real-life situation, data collected from various resources contains a large amount of uncertainties due to human errors and hence it is not easy to analyze the behavior of such system up to a desired accuracy. If somehow behavior of these systems has been calculated, then they have a high range of uncertainty. For handling this situation, a fuzzy set theory has been used in the analysis and an artificial bee colony has been used for determining their corresponding membership functions. To strengthen the analysis, various reliability parameters, which affects the system performance directly, have been computed in the form of fuzzy membership functions. Sensitivity as well as performance analysis has also been analyzed and their computed results are compared with the existing techniques result. The butter-oil processing plant, a complex repairable industrial system has been taken to demonstrate the approach.

An efficient two phase approach for solving reliability-redundancy allocation problem using artificial bee colony technique

The main goal of the present paper is to present a two phase approach for solving the reliability-redundancy allocation problems (RRAP) with nonlinear resource constraints. In the first phase of the proposed approach, an algorithm based on artificial bee colony (ABC) is developed to solve the allocation problem while in the second phase an improvement of the solution as obtained by this algorithm is made. Four benchmark problems in the reliability-redundancy allocation and two reliability optimization problems have been taken to demonstrate the approach and it is shown by comparison that the solutions by the new proposed approach are better than the solutions available in the literature.

An approach for reliability analysis of industrial systems using PSO and IFS technique.

The main objective of this paper is to present a technique for computing the membership functions of the intuitionistic fuzzy set (IFS) by utilizing imprecise, uncertain and vague data. In literature so far, membership functions of IFS are computed via using fuzzy arithmetic operations within collected data and hence contain a wide range of uncertainties. Thus it is necessary for optimizing these spread by formulating a nonlinear optimization problem through ordinary arithmetic operations instead of fuzzy operations. Particle swarm optimization (PSO) has been used for constructing their membership functions. Sensitivity as well as performance analysis has also been conducted for finding the critical component of the system. Finally the computed results are compared with existing results. The suggested framework has been illustrated with the help of a case.

Reliability Analysis of the Engineering Systems Using Intuitionistic Fuzzy Set Theory

The present paper investigates the reliability analysis of industrial systems by using vague lambda-tau methodology in which information related to system components is uncertain and imprecise in nature. The uncertainties in the data are handled with the help of intuitionistic fuzzy set (IFS) theory rather than fuzzy set theory. Various reliability parameters are addressed for strengthening the analysis in terms of degree of acceptance and rejection of IFS. Performance as well as sensitivity analysis of the system parameter has been investigated for accessing the impact of taking wrong combinations on its performance. Finally results are compared with the existing traditional crisp and fuzzy methodologies results. The technique has been demonstrated through a case study of bleaching unit of a paper mill.

STOCHASTIC BEHAVIOR ANALYSIS OF AN INDUSTRIAL SYSTEMS USING PSOBLT TECHNIQUE

The purpose of this paper is to present a hybridized technique for analyzing the behavior of an industrial system stochastically by utilizing vague, imprecise, and uncertain data. If the collected data are used as such in the analysis, then they high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. For this, two important tools namely Lambda-Tau methodology and particle swarm optimization are used to formulate the hybridized technique PSOBLT (Particle swarm optimization based Lambda-Tau) to analyze the behavior of the complex industrial systems stochastically up to a desired degree of accuracy using available information. Six reliability indices namely failure rate, repair time, mean time between failures(MTBF), expected number of failures (ENOF), availability and reliability of the system are used for the analysis of systems behavior. Expressions of these reliability indices are obtained using Lambda-Tau methodology and particle swarm optimization (PSO) is used to construct their membership function utilizing the quantified information of the system in the form of triangular fuzzy number. The washing unit of a medium size paper plant situated in the northern part of India has been considered to demonstrate the approach. The behavior analysis results computed by PSOBLT technique have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties.

Cost minimization of washing unit in a paper mill using artificial bee colony technique

Due to the engineering requirements of products with better quality, the importance of designing reliable systems which normally present high availability is increasing. When the components of higher reliability are used, the associated cost of components also increases. Thus, the decision-makers have to consider both the profit and the quality requirements. The objective of this paper is to improve the design efficiency and to find the most optimal policy for MTBF (mean time between failures), MTTR (mean time to repair) and related costs. Artificial bee colony algorithm has been used to obtained the MTBF and MTTR of various components, in a cost effective manner and results are shown to be statistically significant by means of pooled t-test with other evolutionary algorithm results. The application of the proposed framework has been demonstrated through the washing unit of a paper mill situated in a northern part of India which produces approximately 200 tons of paper per day. Sensitivity analysis has also been addressed to rank the components of the system based on its performance. The system analyst or decision maker may use these optimal values to increase the performance as well as productivity of the system.

Fuzzy RAM Analysis of the Screening Unit in a Paper Industry by Utilizing Uncertain Data

Reliability, availability, and maintainability (RAM) analysis has helped to identify the critical and sensitive subsystems in the production systems that have a major effect on system performance. But the collected or available data, reflecting the system failure and repair patterns, are vague, uncertain, and imprecise due to various practical constraints. Under these circumstances it is difficult, if not possible, to analyze the system performance up to desired degree of accuracy. For this, Artificial Bee Colony based Lambda-Tau (ABCBLT) technique has been used for computing the RAM parameters by utilizing uncertain data up to a desired degree of accuracy. Results obtained are compared with the existing Fuzzy Lambda-Tau results and we conclude that proposed results have a less range of uncertainties. Also ranking the subcomponents for improving the performance of the system has been done using RAM-Index. The approach has been illustrated through analyzing the performance of the screening unit of a paper industry.

Weibull fuzzy probability distribution for analysing the behaviour of pulping unit in a paper industry

The purpose of this paper is to present a technique for analysing the behaviour of an industrial system stochastically by utilising vague, imprecise, and uncertain data. The technique utilises Petri nets and fuzzy Lambda-Tau method for analysing the reliability indices of time varying failure rate instead of the constant failure rate. Petri nets are used for modelling the system while fuzzy set theory is used for representing the failure rate and repair time because fuzzy numbers allows expert opinions, operating conditions, uncertainty and imprecision in reliability information. Various expressions of reliability indices like failure rate, repair time, mean time between failures (MTBF), reliability, availability and maintainability for the system in terms of Weibull distribution is computed. The pulping unit of a paper mill situated in a northern part of India, producing approximately 200 tons of paper per day, has been considered to demonstrate the proposed approach.

Performance Analysis of Repairable Industrial Systems Using Artificial Bee Colony and Fuzzy Methodology

For an industrial system reliability, availability and maintainability (RAM) analysis play an important role in any design modification for achieving its optimum performance. However, it is difficult to predict these parameters by using available and imprecise data up to a desired degree of accuracy. For this, a novel technique named as an artificial bee colony based Lambda-Tau has been presented for computing these parameters by utilizing available or collected data up to a desired degree of accuracy. In this technique expression of RAM parameters are calculated by Lambda-Tau methodology and their corresponding membership functions are computed by formulating a nonlinear programming problem. A generalized RAM-Index has been used for ranking the components of the system based on its performance for improving the system productivity. The presented approach has been investigated through a case study of washing unit of paper industry and computed results are compared with existing Lambda-Tau and evolutionary algorithm techniques.