Abstract
The present work investigates the reliability optimization problem of the repairable industrial systems by utilizing uncertain, limited, and imprecise data. In many practical situations where reliability enhancement is involved, the decision making is complicated because of the presence of several mutually conflicting objectives. Moreover, data collected or available for the systems are vague, ambiguous, qualitative, and imprecise in nature due to various practical constraints and hence create some difficulties in optimizing the design problems. To handle these problems, this work presents an interactive method for solving the fuzzy multiobjective optimization decision-making problem, which can be used for the optimization decision making of the reliability with two or more objectives. Based on the preference of the decision makers toward the objectives, fuzzy multi-objective optimization problem is converted into crisp optimization problem and then solved with evolutionary algorithm. The proposed approach has been applied to the decomposition unit of a urea fertilizer plant situated in the northern part of India producing 1500–2000 metric tons per day.
Highlights
Reliability in general can be defined as the ability of a system to perform its required functions under stated conditions for a specified period of time
In multiobjective optimization problems (MOOPs), it is difficult or rarely possible to find an optimal solution for all the objectives which simultaneously optimize the problem in fuzzy environment
For handling such types of situations, one usually tries to search for a solution which is as close to the decision makers (DMs) expectations as possible
Summary
Reliability in general can be defined as the ability of a system to perform its required functions under stated conditions for a specified period of time. It is difficult to describe the goals and constraints of such optimization problems by crisp relations through equations and/or descriptions In such situations, the traditional reliability theory, based on probabilistic and binary state assumptions, does not always provide useful information to the practitioners due to the limitation of being able to handle only quantitative information [3,4,5]. In multiobjective optimization problems (MOOPs), it is difficult or rarely possible to find an optimal solution for all the objectives which simultaneously optimize the problem in fuzzy environment For handling such types of situations, one usually tries to search for a solution which is as close to the decision makers (DMs) expectations as possible.
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