{"id":11488,"date":"2023-02-02T15:43:20","date_gmt":"2023-02-02T15:43:20","guid":{"rendered":"http:\/\/141.23.68.248\/wp\/?page_id=11488"},"modified":"2023-02-13T12:23:22","modified_gmt":"2023-02-13T12:23:22","slug":"multi-objective-optimization","status":"publish","type":"page","link":"http:\/\/141.23.68.248\/wp\/?page_id=11488","title":{"rendered":"Multi-Objective Optimization"},"content":{"rendered":"

<\/h2>\n

Goal<\/h3>\n

Multi-objective optimization is a problem-solving technique that involves finding the most efficient solution among several possible options that must be optimized simultaneously. For this assignment, the goal is to find an optimal maintenance strategy in order to reduce costs, system downtime, greenhouse gases, etc. Therefore, a genetic algorithm needs to be implemented in order to identify a set of Pareto-optimal solutions.<\/p>\n

Multi-Objective Optimization<\/h3>\n

To implement a\u00a0multi-objective optimization for our system, the nsga2() function, part of the mco package, was used. It was necessary to define the function to be minimized, the number of input variables, the number of output measures, and the lower and upper bounds for the input variables.<\/p>\n

To create the function to be minimized, it is important\u00a0to look at the input and output variables. For this purpose, different maintenance strategies had to be analyzed.\u00a0Therefore, the durations of the interventions were considered in a specific range, as defined in the page: Maintenance Strategies<\/a>.<\/p>\n

Kindergarden interventions<\/span><\/p>\n