Abstract: Equipment procurement constitutes an important aspect of equipment development, and it is the last link in the whole generation process of equipment from putting forward demand to forming combat capability. The success or failure of this work directly affects the quality of military equipment and the generation of combat effectiveness. At present, empirical decision making, which depends on the experience, knowledge and preferences of decision makers, can no longer meet the demands of equipment purchasing decisions. Therefore, it is urgent to explore and build a reasonable model in accordance with the characteristics of purchasing decisions, making full use of the intelligent optimization algorithm to solve the problems in realizing the optimal decision-making of equipment procurement. On the basis of synthetically considering the various factors of equipment procurement, the approach is put forward to transform the equipment procurement problem into a multi-objective fuzzy assignment problem under multi-constraint conditions, and a solution based on genetic algorithm is given. Finally, a case study is carried out to verify the feasibility and effectiveness of the algorithm. At the same time, the efficiency of the genetic algorithm and the Hungarian algorithm is compared. The experimental results show that the efficiency of genetic algorithm is much better than that of Hungarian algorithm, and with the increase of the scale of the problem, the difference of solving time is even more obvious. The complex problem of equipment purchasing decision optimization is solved, which is difficult to achieve by the traditional optimization method, and it has good application effect in practical situations, so it has significant practical value for equipment purchasing decision making. At the same time, the model put forward in this paper also has some reference value for constructing and solving other assignment problems.

Key words: equipment procurement, Genetic Algorithm (GA), multi-objective fuzzy assignment, optimal decision