# Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks
## 1 Introduction
analyzed the factors affecting RMC distribution and built a model based on the GA to solve the optimal distribution scheme with the objective of minimizing the total waiting time of RMC distribution vehicles at the construction site, and, later, they tried to build an RMC-scheduling model by the FMGA and simulation techniques to find the scheduling scheme with the minimum total pouring waiting time of RMC distribution vehicles [19; 20], respectively. [PERSON] et al. established a vehicle-scheduling model with the goal of minimizing the interrupted pouring and waiting pouring time at each mixing station, and solved the model with a GA [21]. [PERSON] [22], aiming at minimizing the total fuel consumption of vehicles, the authors developed a time-dependent VRP (TDVRP), considering the vehicle speed, delivery distances, and load factors, and, also, used a simulated annealing (SA) algorithm to solve the problem. A general concrete delivery model was proposed in [23], but it ignored the time window and vehicle failure factors. Considering vehicles can break down during the distribution of concrete, [PERSON] et al. proposed a model for concrete companies to adjust their plans in time to reduce production costs [24]. However, the difficulty of solving the problem obviously increases when considering the vehicle distribution failure problem. [PERSON] et al. divided the RMC distribution solution process into two parts to reduce the complexity of the problem [25]. Although, for [26], from the perspective of multiple mixing plants supplying multiple sites and considering the work of mixers, a mathematical model with the objective of minimum fuel during RMC distribution is established and solved by a bee algorithm (BA), the article defaults to a consistent volume for all concrete trucks. The summary of the vehicle-routing problem is shown in Table 1.  
While scholars have made significant research achievements, there is still a lack of studies on how different concrete mixer trucks can collaborate to supply concrete and reduce total fuel consumption during vehicle distribution. In this paper, based on a comprehensive consideration of the impact of vehicle type, vehicle load, vehicle travel speed, and vehicle travel distance on vehicle fuel consumption, a green path model for the concrete truck mixer with the goal of minimizing total fuel is established and solved with an improved genetic algorithm. The main research objective of this paper is to reduce fuel consumption during the concrete distribution process in order to realize low-carbon concrete distribution. The main contributions of this paper are as follows:  
1. The vehicle fuel consumption of a concrete tanker was modeled;
2. A low-carbon collaborative distribution model of multi-mixing stations was established for the current situation of concrete multi-mixing station distribution;
3. Adaptive elite retention strategies, adaptive crossover, mutation operators, and immune selection have been added to the standard genetic algorithm to improve it;
4. The established low-carbon co-operative distribution model for multiple mixing stations was solved using improved genetic algorithms in combination with concrete enterprise orders.