Improving maintenance decisions for offshore wind farms using fuzzy multi-objective linear programming in a fuzzy environment

This study presents a fuzzy multi-objective linear programming (FMOLP) framework for offshore wind farm maintenance, integrated with an interactive two-phase possibilistic linear programming (ITPPLP) approach. Trapezoidal membership functions are employed to capture fuzziness in cost, reliability and carbon emissions, while chance-constrained reliability and supervisory control and data acquisition (SCADA)-based updates enhance practical relevance. The framework simultaneously minimizes costs and emissions while maximizing reliability, offering a more realistic decision-support tool compared with traditional models. A case study on an offshore wind farm is conducted to demonstrate the effectiveness and applicability of the proposed approach, providing decision makers with a set of balanced and satisfactory solutions.