A mathematical model is proposed to minimize the sum of vehicle fixed cost, fuel cost, carbon-emission cost, cooling cost, time-penalty cost and split-compensation cost, on the basis of considering the three-level cold-chain-logistics network of manufacturer, distribution center, and seller. The model is constructed based on the constraints of customer time window, vehicle load, demand-splitable, and semi-open driving of multiple distribution centers. We to divide the customer areas according to geographical locations and to carry out the transportation processes in stages. The target solution, which includes vehicle routing, service time and type, cargo details, etc., has been formulated. A two-stage hybrid-heuristic-path-scheme solution algorithm that combines a taboo table, a genetic algorithm, an optimal-path-generation algorithm, a load-capacity-constraint algorithm, and a time-window-constraint algorithm is designed in view of the complexity of the model and the uniqueness of the solution scheme. This paper aims to reasonably plan the resource allocation of cold chain logistics enterprises, reduce the comprehensive cost of cold chain transportation, improve customer satisfaction, and respond to the green logistics policy advocated by the state by reducing vehicle transit time and fuel consumption, and promote energy conservation and emission reduction.

The seriousness of vessel air pollution has forced the International Maritime Organization (IMO) to introduce a series of relevant laws and regulations. This paper proposes a monitoring scheme based on the synergistic operation of motherships and UAVs. This scheme innovatively adopts a harbor sea patrol vessel or the other official vessel (mothership) as the mobile power supply base for UAVs and realizes efficient and accurate monitoring of vessel air pollution in the pre-monitored area at sea by carrying multiple UAVs. The focus of this paper is on the path optimization problem for multi-UAV collaboration with mothership (MUCWM) monitoring, where the objective is to minimize the total monitoring time for MUCWM. The following three main aspects are studied in this paper: (1) multi-UAV monitoring path optimization; (2) the collaboration mechanism between the mothership and multiple UAVs; and (3) mothership traveling path optimization. In order to effectively solve the above problems, this thesis constructs a path optimization model for multi-UAV collaborative mothership monitoring of air pollution from vessels in port waters; solves the model using the improved adaptive differential evolution (IADE) algorithm; and verifies the effectiveness of the model and the algorithm by using the position data in the Automatic Identification System (AIS) of vessels in Ningbo Zhoushan Port. Through the performance comparison and sensitivity analysis of the algorithm, it is confirmed that the algorithm can effectively solve the path planning problem of the collaborative operation between the mothership and multiple UAVs. The research results in this paper not only help to reduce the air pollution level of harbor vessels and improve the efficiency of sea cruising but also play an important supporting role in the enforcement of relevant emission regulations.