No abstract

This paper proposes a green logistics location-routing optimization problem based on improved genetic algorithm (GA) from the perspective of low-carbon and environmental protection. First, considering the cost factor, time window, deterioration rate of agricultural products, inventory and distribution capacity, carbon trading mechanism, and other factors, and with the total cost minimization as the optimization goal, a low-carbon and environmental protection logistics location-routing optimization model is constructed. Then, the adaptive operator and cataclysm operator are introduced to improve the GA algorithm, which can adjust crossover and mutation probability according to the needs, reducing the influence of parameters and running time. Furthermore, the improved GA algorithm is used to solve the location-routing optimization problem in green logistics, so as to obtain a low-carbon, economical, and efficient distribution path. Finally, perform experimental analysis of the proposed method using the relevant data of U company. The results show that the total distribution cost is 6771.3 yuan, which meets the design requirements of economy and environmental protection.

The contradiction between limited network resources and a large number of user demands in vehicle environment will cause a lot of system delay and energy consumption. To solve the problem, this paper proposes an efficient resource management optimization scheme for Internet of Vehicles in edge computing environment. Firstly, we give a detailed formulation description of communication and computing cost incurred in the resource optimization process. Then, the optimization objective of this paper is clarified by considering the constraints of computing resources, and system delay and energy consumption are considered comprehensively. Secondly, considering dynamic, random, and time-varying characteristics of vehicle network, the optimal resource management scheme of Internet of Vehicles is given by using distributed reinforcement learning algorithm to optimize total system overhead to the greatest extent. Finally, experiments show that when bandwidth = 40 MHz, the total system cost of the proposed algorithm is only 3.502, while that of comparison algorithms is 4.732 and 4.251, respectively. It is proved that the proposed method can effectively reduce the total system overhead.