This paper addresses an asset management problem in the context of the wind energy industry. Asset management decisions (including operation and maintenance, retrofitting and purchasing) for assets reached their end-of-life are explicitly examined in a linear programming model over a planning horizon. Unfortunately, almost all important generic classes of integer programming problems are NP-hard and many of these problems are large-size. Therefore, in order to solve practical integer programming problems we may need to use problem specific algorithms which can exploit some special structures of the problem at hand. We propose a solution approach based on a lagrangian relaxation and the subgradient method for a large size parallel asset management problem, which originally solved by using mixed integer linear programming (MILP). The decomposition approach considers the relaxation of different sets of constraints, including the budget and energy constraints. The computational results show that the incorporation of langrangian relaxation significantly improves the duality gap and solution time of a case study from wind turbine (WT) sector

This paper addresses an asset management problem in the context of the wind energy industry. Asset management decisions (including operation and maintenance, retrofitting and purchasing) for assets reached their end-of-life are explicitly examined in a linear programming model over a planning horizon. Unfortunately, almost all important generic classes of integer programming problems are NP-hard and many of these problems are large-size. Therefore, in order to solve practical integer programming problems we may need to use problem specific algorithms which can exploit some special structures of the problem at hand. We propose a solution approach based on a lagrangian relaxation and the subgradient method for a large size parallel asset management problem, which originally solved by using mixed integer linear programming (MILP). The decomposition approach considers the relaxation of different sets of constraints, including the budget and energy constraints. The computational results show that the incorporation of langrangian relaxation significantly improves the duality gap and solution time of a case study from wind turbine (WT) sector