{"id":8402,"date":"2022-01-30T06:03:17","date_gmt":"2022-01-30T06:03:17","guid":{"rendered":"http:\/\/141.23.68.248\/wp\/?page_id=8402"},"modified":"2022-02-15T14:58:23","modified_gmt":"2022-02-15T14:58:23","slug":"6-multi-objective-optimization","status":"publish","type":"page","link":"http:\/\/141.23.68.248\/wp\/?page_id=8402","title":{"rendered":"5. Multi-objective Optimization"},"content":{"rendered":"

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Scope<\/h2>\n

The aim of the optimization is to make a choice that (1) Minimizes the time the system is not in use to to the interventions defined\u00a0in the maintenance plans and (2) Minimizes the time that the system is down for maintenance\u00a0to occur., but also integrate the life cycle analysis which contains information for the costs and environmental impacts of the design choices.<\/p>\n

Discussion<\/h2>\n

This model is performed by modelling different configurations of the individual maintenance\u00a0plans and displaying the results as a pareto frontier to see the best design alternatives based on our goals, and integration of the life cycle assessments. The pareto frontier below shows the optimal solutions below in red, and the best solution would be in the bottom left corner for a\u00a0design that costs a little over 2,000,000 Euro and the maintenance strategy would last a little\u00a0 more than 2o days.<\/p>\n

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\u00a0Figure 1: Pareto Frontier<\/p>\n

Using a fitness function, a design space was also created that shows\u00a0accumulated impact of input parameters affect the performance criteria. On the left, the input parameters are shown and the output parameters are shown on the right for the defined criteria. The red paths demonstrate the optimal paths that lead to a pareto front of 1.<\/p>\n

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Figure 2: Final Design Space<\/p>\n

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Integration Context of the civil systems<\/a><\/p>\n