In order to effectively solve the problem of vehicle routing, a design and implementation method of an energy-saving logistics management system oriented to routing optimization is proposed. From the perspective of optimal calculation, this research uses the improved Dixie algorithm and clustering algorithm to design and implement a logistics company’s distribution center location and distribution path planning system. First of all, the authors analyze the common models of the LRP problem in detail and give the mathematical model and calculation method of positioning rationing and the transportation route planning problem. Secondly, in view of the shortcomings of traditional evolutionary algorithms, the authors propose a series of improvement measures. The authors adopt a natural number coding scheme combined with an adaptive crossover mutation operator to improve the search ability of the solution space; the authors also introduce a penalty function to deal with constraints and take corresponding measures for illegal individuals generated in the evolution process, reducing premature convergence. Possibility. It has been verified that the design and development of the system saves investment costs for small and medium-sized logistics enterprises and reduces the cost of goods distribution by 80%. The effect is remarkable, which verifies the effectiveness, accuracy, and superiority of the algorithm.

With the rapid development of Internet technology, the network information data is exploding, and society has entered the era of cloud data. Cloud data provides a lot of data support for people's life and work, but because of its large number, multifarious types and huge value, there are some problems such as private data leakage and abuse of sensitive information. Stocking of biomedical material at one place only, can cause delay in sending important lifesaving material to needy patients. On the one hand, it is an important content of engineering scheduling theory and method; On the other hand, in medical field, a lot of materials are usually consumed to protect the life of patients. Biomedical informatics and computer vision techniques have been combined in a variety of inter-multidisciplinary disciplines during the past few decades. We are all aware that inadequate or insufficient cloud access management and controls can expose a corporation to a range of issues. The work that follows offers intelligent supply chain, an upgraded service management control telephone network architecture. If we can acquire according to the patient demands and retain the ideal inventory such that the patient requirement and interest can be safe, the overall cost will be lowered greatly. The experimental results show that R1 orders 31 times, and the total cost is 1,259,520 CNY. R2 ordered 24 times, with a total cost of 982,034 CNY; R3 orders 22 times, and the total cost is 990,146 CNY. Finally, the optimal solution of the total cost after schedule optimization is 3,231,700 CNY. It is proved that the optimal cost finally obtained by forecasting supply chain inventory information based on cloud data environment is also more practical for engineering practice.