System reliability optimisation via quantifying uncertainty

Improving system reliability is becoming an important criterion for engineering industries. In addition to its importance, reliability improvement imposes cost on industries. Therefore, a balance should be found between system reliability and system cost. System reliability optimisation (SRO) models are helpful in reaching this goal. In this study, an approach is proposed to translate system reliability into financial terms. This translation is used to develop mathematical models in order to evaluate and optimise systems. As a result, optimal decisions are found in terms of both reliability improvement and cost reduction. The system structure, for which models are developed, is a multi-state weighted k-out-of-n system with repairable components. Since the reliability of components varies over time, the developed mathematical models simulate the dynamic behaviour of the system over operational periods discretely. Thus, new system reliability evaluation and optimisation models have been presented in this thesis. As another goal, uncertainty associated with system reliability assessment is considered in optimisation models. Linguistic terms of Failure Mode and Effects Analysis (FMEA) are used for system reliability assessment when there is not sufficient data. Based on the qualitative data, an evaluation and optimisation model is proposed to quantify the uncertainty

Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning

We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in machine learning. Every machine-learning technique has hyperparameters that can be tuned using evolutionary computation and optimization, considering normally multiple criteria, such as bias, variance, complexity, and fairness in model selection. Multiobjective evolutionary optimization can help meet these criteria for optimizing machine-learning models. Some of the existing approaches address these multiple criteria by transforming the problem into a single-objective optimization problem. However, multiobjective optimization models are able to outperform single-objective ones in contributing to multiple intended objectives (criteria). In recent years, evolutionary computation has been shown to be the premier method for solving multiobjective optimization problems (MOPs), producing both optimal and diverse solutions beyond the capabilities of other heuristics. This is particularly true for very large solution spaces, which is the case in real-world machine-learning problems with many features

A HYBRID MATHEMATICAL MODELLING APPROACH FOR ENERGY GENERATION FROM HAZARDOUS WASTE DURING THE COVID-19 PANDEMIC

The COVID-19 virus in a short time has caused a terrible crisis that has been spread around the world. This crisis has affected human life in several dimensions, one of which is a sharp increase in urban waste. This increase in waste volume during the pandemic, in addition to the intense increase in costs associated with the risks of virus contagion through infectious waste. In this study, a hybrid mathematical modelling approach including a Bi-level programming model for infectious waste management has been proposed. At the higher level of the model, government decisions regarding the total costs related to infectious waste must be minimized. At this level, the collected infectious waste is converted into energy, the revenue of which is returned to the system. The lower level relates to the risks of virus contagion through infectious waste, which can be catastrophic if ignored. This study has considered the low, medium, high and very high prevalence scenarios as key parameters for the production of waste. In addition, the uncertainty in citizens’ demand for waste collection was also included in the proposed model. The results showed that by energy production from waste during the COVID-19 pandemic, 34% of the total cost of collecting and transporting waste can be compensated. Finally, this paper obtained useful managerial insights using the data of Kermanshah city as a real case