Due to the national policy of encouraging the development of power exchange modes, the reasonable planning of vehicle distribution paths to meet the demand of lithium battery power exchange points has become a topic of considerable research interest. In this study, we propose the “centralized charging + unified distribution” power exchange mode for optimizing the charging and transporting of lithium batteries. Considering lithium batteries are dangerous goods, the vehicle path problem of simultaneous pickup and delivery of lithium batteries with vehicle load and soft time window constraints is studied. The model objective is to minimize the transportation risk and total cost of delivery. By performing crossover and mutation operations on the initial solutions generated by the ant colony algorithm, a hybrid ant colony genetic algorithm (ACO-GA) is designed to solve the model. The results of ACO-GA are compared with the GA, ACO, and SAA methods using the Solomon dataset; the results show that the optimized ant colony algorithm can achieve a smaller total cost in solving the model. Finally, taking a lithium battery leasing business in Company A, we determine the optimal path under different preferences by setting different weights for distribution cost and transportation risk in the model transformation, which provides a reference for the company to select the distribution route. Thus, the model provides a reference for companies that intend to develop power exchange businesses.

The era of digital economy has spawned new factors of production represented by big data. Data-driven sustainable growth and innovative development is the main line of enterprise digital transformation. In view of the uncertainty of supply chain demand, the optimization of supply chain management under the influence of carbon tax policy is studied from the low-carbon perspective. By introducing the fuzzy theory and a membership function to describe the need for uncertainty, a supply chain optimization model considering the influence of carbon emissions is constructed. Carbon tax policy is regarded as the constraint condition of external production. In the case of fuzzy cost changes, the optimization decision of enterprise supply chain management under carbon tax policy is explored. A trapezoidal fuzzy number is used to determine model fuzziness, and the trapezoidal fuzzy number combined with a genetic algorithm is used to solve the model. Furthermore, taking a certain enterprise as an example, the validity of the model is verified by the mathematical programming model method, considering the data driven and taking the data as the means of production.

Major emergencies in recent years have greatly challenged the rapid response capability of the emergency supplies logistics system. In order to minimize the damage brought by emergencies to the society, the emergency supplies should be timely to the place where the emergencies occur, so as to reduce the losses brought by the emergencies. In order to further study the vehicle scheduling of emergency supplies, this paper establishes a multi-distribution centre emergency logistics vehicle scheduling model in the context of emergency logistics, considering the number of distribution centres in different cases, with the goal of reaching the demand point in terms of the total time and the total cost, and giving the time goal a higher weight, and solving it by using the genetic algorithm. The simulation solution is carried out through the arithmetic example, and the following conclusions are drawn: (1) in the ideal situation, the more the number of distribution centres is, the total time for emergency supplies to reach the demand point is the smallest, but the total cost tends to be higher; (2) the model in this paper can find the optimal solution that meets the conditions for the different number of distribution centres, which improves the distribution tenacity of the distribution centres. Therefore, the research in this paper provides scientific and effective suggestions for emergency logistics decision makers.