Abstract
Maximising profit is an important target for industries in a competitive world and it is possible to achieve this by improving the system availability. Engineers have employed many techniques to improve systems availability, such as adding redundant devices or scheduling maintenance strategies. However, the idea of using such techniques simultaneously has not received enough attention. The authors of the present paper recently studied the simultaneous optimisation of system design and maintenance strategy in order to achieve both maximum availability and minimum cost: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with Discrete Event Simulation in a real encoding environment in order to achieve a set of non-dominated solutions. In this work, that study is extended and a thorough exploration using the above-mentioned Multi-objective Evolutionary Algorithm is developed using an industrial case study, paying attention to the possible impact on solutions as a result of different encodings, parameter configurations and chromosome lengths, which affect the accuracy levels when scheduling preventive maintenance. Non-significant differences were observed in the experimental results, which raises interesting conclusions regarding flexibility in the preventive maintenance strategy.
Highlights
System Reliability (R(t)) can be defined as the probability of failure free operation under specified conditions over an intended period of time [1]
A period of time is needed to recover the system operating status (Time To Repair). This creates an interest in Time To Failure and Time To Repair, which can be modelled as random variables that can be represented by continuous probability distributions
The behaviour of its encoding is similar to the behaviour explained for the valve V1
Summary
System Reliability (R(t)) can be defined as the probability of failure free operation under specified conditions over an intended period of time [1]. System Maintainability (M(t)) can be defined as the probability of being restored to a fully operational condition within a specific period of time [2]. These definitions lead to interest both in time taken for a system to failure (Time To Failure) and in time taken to repair the system (Time To Repair). The concepts of Reliability and Maintainability are related to Availability in order to define the way in which the system is able to achieve the function for which it was designed, over a period of time. This creates an interest in Time To Failure and Time To Repair, which can be modelled as random variables that can be represented by continuous probability distributions
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