Pareto Optimality of Centralized Procurement Based on Genetic Algorithm

Abstract

In the process of purchasing materials, small enterprises are often unable to meet the minimum availability of suppliers in the process of purchasing due to the lack of economic strength and storage capacity of goods. Therefore, they will encounter difficulties in the process of purchasing.To solve this problem, the group-led centralized procurement strategy for small enterprises has become a new craze. In this paper, we transform the problem of centralized procurement lot into a multi-objective optimization problem by establishing a multi-objective optimization model with cost, quality and logistics as sub-objectives, and use genetic algorithms to solve the multiobjective optimization problem in order to achieve Pareto optimality among each purchaser and supplier. Finally, an example of procurement by the China Energy Investment Corporation is used to verify that the multi-objective optimization model for the collection of lots constructed in this paper can effectively promote the cooperation between purchasers and suppliers, and stimulate the competitive vitality of enterprises in the market.

The Pitfalls of Reverse Engineering Topology Optimised Load-Carrying Structural Parts into Parametric Models

“…Achieving optimum design of mechanical systems requires the use of a variety of methods and tools. Advanced numerical methods such as the Finite Element Method (FEM) and genetic algorithms [3] allow engineers to simulate the behavior of mechanical systems under different loads and conditions. These tools allow an iterative process to find the optimal design over several iterations, with the parameters of the mechanical systems being changed and adjusted according to the results of the analysis.…”