Abstract:

Spare parts is a key component of equipment support, and a reasonable allocation plan of spare parts is the key factor for improving the efficacy of equipment support. Spare parts allocation and optimization in a multi-echelon support system presents difficult problems, which involves non-linear objective function and integer variables to be optimized. A multi-objective optimization model is developed, which maximizes support probability and minimizes support costs. In order to obtain the solution of the model, an improved multi-objective particle swarm optimization (MOPSO) method is employed, based on the traditional particle swarm method. The design idea and optimization procedure of this algorithm are put forward, rule based and inertia weigh improving method are introduced. In this method, dimensions reduction and rules-based multi-objective optimization are employed, which can improve the solving efficiency for the MOPSO method. At last, a numerical example is given, which examines the feasibility and validity of this method.