This work focuses on the optimization of a last-mile delivery system with multiple transportation modes. In this scenario, parcels need to be delivered to each customer point. The major feature of the problem is the combination of a fleet of road vehicles (vans) with a drone. Each van visits a subset of demand nodes to be determined according to the route of the van. The drone serves the customers not served by vans. At the same time, considering the safety, policy and terrain as well as the need to replace the battery, the drone needs to be transported by truck to the identified station along with the parcel. From each such station, the drone serves a subset of customers according to a direct assignment pattern, i.e., every time the drone is launched, it serves one demand node and returns to the station to collect another parcel. Similarly, the truck is used to transport the drone and cargo between stations. This is somewhat different from the research of other scholars. In terms of the joint distribution of the drone and road vehicle, most scholars will choose the combination of two transportation tools, while we use three. The drone and vans are responsible for distribution services, and the trucks are responsible for transporting the goods and drone to the station. The goal is to optimize the total delivery cost which includes the transportation costs for the vans and the delivery cost for the drone. A fixed cost is also considered for each drone parking site corresponding to the cost of positioning the drone and using the drone station. A discrete optimization model is presented for the problem in addition to a two-phase heuristic algorithm. The results of a series of computational tests performed to assess the applicability of the model and the efficiency of the heuristic are reported. The results obtained show that nearly 10% of the cost can be saved by combining the traditional delivery mode with the use of a drone and drone stations.

With the rapid development of e-commerce, customers could order online to ensure timeliness. Therefore, e-commerce enterprises need to pick and distribute customers’ orders. These two operations are interdependent. Order picking needs to consider the vehicle route planning. At the same time, the vehicle route planning is also based on the batching of orders. Considering the demand surge scenario of food cold chain, with the shortest time and lowest cost to complete all distribution tasks as the objective, this paper aims at the integrated optimization of distribution scheduling and route planning, and establishes a mixed integer programming mathematical model. Finally, we design a three-stage heuristic algorithm to solve this problem, and use the actual data to carry out numerical experiments to verify the reliability and effectiveness of the mathematical model and heuristic algorithm.

This paper focuses on the effect of demand surge on the food cold chain, where orders arrive online. The demand surge has successively affected the order batching, batch sequencing, and route planning, compared to regular demand. This research studies the integrated optimization of food cold chain order picking and vehicle routing of online orders, where mixed integer programming model is formulated to minimize time-consuming and cost. We firstly use K-means++ algorithm to cluster all customers, and then an online batch processing algorithm is designed in each region. Finally, a genetic algorithm is used to complete the joint optimization of the picking and delivery. We use X enterprises e-commerce platform as a case to collect actual operating data to verify the effectiveness of the model and algorithm. And comparing the analysis results between phased optimization and integrated optimization, reasonable suggestions are put forward for management decisions.