Research on Benchmarking Management Decision Support Model Based on Multi-objective Optimization

Benchmarking management helps enterprises to identify their own gaps and make improvement plans by comparing and analyzing the best practices inside and outside the industry, but the traditional benchmarking management often focuses on the optimization of a single goal. In this paper, a decision support model for benchmarking management based on multi-objective optimization is proposed, which aims to help enterprises solve many conflicting objective problems in the actual decision-making process. The model considers two objectives: minimizing cost and maximizing customer satisfaction, and establishes the corresponding mathematical model. In order to solve the complexity of the model, a hybrid algorithm combining genetic algorithm (GA) and particle swarm optimization (PSO) is proposed in this paper. Through case analysis, the validity and practicability of the model are verified. The results of case analysis show that the production cost, transportation cost and storage cost have been significantly reduced through the implementation of the optimization scheme, and the overall cost has been reduced by 11.87%. At the same time, customer satisfaction has also increased from the original 3.876 points to 4.412 points, an increase of 13.88%. The optimization scheme mainly reduces the waste of resources and idle time of equipment in the production process by improving production efficiency and equipment utilization, thus shortening the delivery time of products and improving product quality and after-sales service. The results show that the model can effectively help enterprises to make optimization plans, reduce costs and improve customer satisfaction, thus enhancing the market competitiveness of enterprises. By introducing the multi-objective optimization theory into benchmarking management, this study constructs a decision support model that comprehensively considers multiple objectives, and verifies the effectiveness and practicability of the model through case analysis, which provides scientific support for enterprise decision-making.