Aiming at the truck scheduling problem between the outer yard and multi-terminals, the appointment optimization model of truck is established. In this model, the queue time and the operation time of truck during the appointment period of different terminals are different. Under the restriction of given appointment quotas of each appointment period, determine the arrival amount of trucks in each appointment period. The goal is to reduce carbon emissions and total costs, improve the efficiency of truck scheduling. To solve this model, hybrid genetic algorithm with variable neighborhood search was designed. Firstly, generate chromosomes, and the front part of the chromosome represents the demand for 40 ft containers and the back part represents the demand for 20 ft containers. Then, the route is generated according to the time constraint and appointment quotas of each appointment period. Finally, the neighborhood search strategy is adopted to improve the solution quality. The validity of the model and algorithm were verified by an example. A low-carbon scheduling scheme was obtained under truck appointment system. The results show that the scheduling scheme under truck appointment system uses fewer trucks, improves the efficiency of delivery, reduces the total costs, and it takes into account the requirements of low carbon.

This paper presents one method and one hybrid genetic algorithm for multi-depot open vehicle routing problem with fuzzy time windows (MDOVRPFTW) without maximum time windows. For the method, the degree of customers’ willingness to accept goods (DCWAG) is firstly proposed, it’s one fuzzy vague and determines maximum time windows. Referring to methods to determine fuzzy membership function, the function between DCWAG and the starting service time is constructed. By setting an threshold for DCWAG, the starting service time that the threshold corresponds can be treated as the maximum time window, which meets the actual situation. The goal of the model is to minimize the total cost. For the algorithm, MDOVRPFTW without maximum time windows is an extension of the NP-hard problem, the hybrid genetic algorithm was designed, which is combination of genetic algorithm and Hungarian algorithm. When the hybrid genetic algorithm applied to one pharmaceutical logistics company in Beijing City, China, one optimal scheme is determined. Then the rationality and the stability of solutions by the hybrid genetic algorithm are proved. Finally, sensitivity analyses are performed to investigate the impact of someone factor on DCWAG and some suggestions are proposed.

Aiming at the multi-depot heterogeneous vehicle routing problem under the time-dependent road network and soft time window, considering vehicle fixed cost, time window penalty cost and vehicle transportation cost, an optimization model of time-dependent multi-depot heterogeneous vehicle routing problem is established with the objective of minimizing distribution cost. According to the characteristics of the problem, a hybrid genetic algorithm with variable neighborhood search considering the temporal–spatial distance is designed. Customers are clustered according to the temporal–spatial distance to generate initial solutions, which improves the quality of the algorithm. The depth search capability of the variable neighborhood search algorithm is applied to the local search strategy of the genetic algorithm to enhance the local search capability of the algorithm. An adaptive neighborhood search number strategy and a new acceptance mechanism of simulated annealing are proposed to balance the breadth and depth required for population evolution. The validity of the model and algorithm is verified by several sets of examples of different scales. The research results not only deepen and expand the relevant research on vehicle routing problem, but also provide theoretical basis for logistics enterprises to optimize distribution scheme.

With the continuous improvement of users' expectation of transportation quality and the continuous improvement of the transportation system in China, the inland collection system plays an increasingly important role in port development. Based on the demand change, this paper measures the disturbance of the demand change to the inland collection and distribution system from ports and customers, it establishes bi-level programming model for transportation route and transportation mode selection. The upper layer establishes the stochastic opportunity constrained programming model with the minimum cost of collection and distribution, the lower layer builds an optimization model with the goal of maximum customer satisfaction. The genetic simulated annealing algorithm is used to solve the bi-level programming model combined with specific examples and compared with genetic algorithm. The result shows that genetic simulated annealing algorithm can not only obtain the optimal solution, but also improve the speed of global convergence. The genetic simulated annealing algorithm is an effective algorithm to solve the bi-level programming model with multiple targets.

Aiming at the dynamic multicompartment refrigerated vehicle routing problem with multigraph based on real-time traffic information, this study, based on the idea of preoptimization followed by real-time adjustment, establishes a two-stage mathematical model with minimizing delivery cost. In the preoptimization phase, this study, based on historical traffic information, designed a hybrid chaotic genetic algorithm with variable neighborhood search (HCGAVNS) to obtain the initial delivery scheme. In the real-time adjustment phase, the order in which customers are served remains the same and a path selection strategy is proposed to solve the problem according to the real-time traffic information of different paths. The validity of the model and the algorithm are verified through the analysis of instances. The research results can enrich the related research on cold chain vehicle routing problem and provide a theoretical basis for logistics companies to optimize their delivery scheme.