Inventory Parameter Optimization for Power Plant Management Based on MOEA/DD Algorithm

The optimization of spare parts inventory parameters is a crucial challenge in power plant management. Based on historical usage data of spare parts, this paper first constructs an innovative multi-dimensional classification system to categorize spare parts into four classes based on their importance towards power plant. For the characteristics of different categories of spare parts, differentiated target functions for fill rate and Spare Parts Inventory cost are established, and the advanced MOEA/DD multi-objective optimization algorithm is employed to solve the problem, ultimately obtaining a set of Pareto optimal solutions. To facilitate practical procurement decisions, this paper further introduces a single-objective optimization method to select the reorder point parameters with the best performance from the optimal solution set. Experimental results demonstrate that the proposed MOEA/DD-based optimization algorithm can formulate differentiated inventory strategies for spare parts of varying importance levels. Under the strict premise of meeting the fill rate requirements for all categories of spare parts, it achieves the minimization of operational costs. Moreover, compared to the traditional NSGA-III algorithm, the MOEA/DD algorithm exhibits superior convergence performance when handling high-dimensional, multi-objective optimization problems with complex constraints.