An actual case-study is worked out to verify the proposed framework. Multi-objective optimization, and decision-making are developed to decide the optimal maintenance frequencies that derive lowest maintenance costs and lowest risk of failure for each functional failure. Deterioration Modeling using deterministic and artificial intelligence models to estimate future conditions of subway components. Reliability Centered Maintenance Database where subway operating systems are decomposed to components levels showing their functions, operational failures, failure causes, failure effects, failure consequences, responsive maintenance actions, and the hourly maintenance costs. The research framework develops four main modules Reliability Centered Maintenance Database, Deterioration Modelling, Multi-objective Optimization, and Decision-Making. The framework targets the vital subway operating systems, namely: railway tracks, overhead catenary, lighting & power supply, centralized control, communication, signaling, rolling stock, and ticketing. This would extensively ease the decision-making process for subway asset managers by providing the optimal allocated budget for each subway system in an automated way. This research aims at introducing a new risk-based multi-objective optimization framework for optimizing performance and budget allocation among subway systems in fulfillment to maintenance planning development in this domain. The real case is that most governments are heavily facing a budget scarcity dilemma, and cannot withstand the acquired maintenance costs to maintain a proper operation for subway network. ![]() ![]() However, public transit infrastructures are generally facing extensive deterioration that requires extensive funds by governments to retain back the desired performance levels. Millions of citizens worldwide heavily rely on subways as their main public transportation approach.
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