Optimization of cold chain distribution path of fresh agricultural products under carbon tax mechanism: A case study in China

A multi-center joint distribution optimization model considering carbon emissions and customer satisfaction

Logistics enterprises are searching for a sustainable solution between the economy and the environment under the concept of green logistics development. Given that, this study integrates carbon emission as one of the costs into the vehicle routing problem with time window (VRPTW) and establishes a multi-center joint distribution optimization model taking into account distribution cost, carbon emission, and customer satisfaction. In the study of carbon emissions, this paper selected the vehicle load rate and vehicle distance as the main indicators. An improved ant colony algorithm is designed to solve the model by introducing the elite strategy, the saving strategy, vehicle service rules, and customer selection rules. Simulation results show that compared with the traditional ant colony optimization and genetic algorithm, the improved ant colony algorithm can effectively reduce the distribution cost and carbon emission and, improve customer satisfaction.

Research on the Optimization of Cold Chain Logistics Distribution Path of Agricultural Products E-Commerce in Urban Ecosystem From the Perspective of Carbon Neutrality

In order to further cope with climate change, achieve green and low-carbon economic development and further improve the construction of ecological civilization, a research method for optimization of cold chain logistics distribution path of agricultural products e-commerce from the perspective of carbon neutrality was proposed. This method takes full account of the requirements of distribution path optimization, it is based on ecological theory and applies ecological methods to study the structure, function and dynamics of human-centered urban ecosystems, as well as the laws of interaction between system components and between the system and the surrounding ecosystems, which is in line with the optimization goal of minimizing distribution operating costs, and the ant colony algorithm is used to construct the cost optimization model of cold chain logistics distribution path based on the weight limit of distribution vehicles and the time window limit. By comparison of simulation experiments, the best parameter solution is 2,873.9931. The difference with other combinations is only 1–2%, and the optimal parameter matching of the algorithm isα = 1,β = 2,ρ = 0.5. Experiments show that the optimal parameter combination is determined on the basis of reflecting different distribution results.

Distribution Path Optimization by an Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy

The multivehicle routing problem (MVRP) is a variation of the classical vehicle routing problem (VRP). The MVRP is to find a set of routes by multiple vehicles that serve multiple customers at a minimal total cost while the travelling-time delay due to traffic congestion is tolerated. It is an NP problem and is conventionally solved by metaheuristics such as evolutionary algorithms. For the MVRP in a distribution network, we propose an optimal distribution path optimization method that is composed of a distribution sequence search stage and a distribution path search stage that exploits a divide-and-conquer strategy, inspired by the idea of dynamic programming. Several optimization objectives subject to constraints are defined. The search for the optimal solution of the number of distribution vehicles, distribution sequence, and path is implemented by using an improved genetic algorithm (GA), which is characterized by an operation for preprocessing infeasible solutions, an elitist’s strategy, a sequence-related two-point crossover operator, and a reversion mutation operator. The improved GA outperforms the simple GA in terms of total cost, route topology, and route feasibility. The proposed method can help to reduce costs and increase efficiency for logistics and transportation enterprises and can also be used for flow-shop scheduling by manufacturing enterprises.

Deep Vision Servo Hand-Eye Coordination Planning Study for Sorting Robots

Within the context of large-scale symmetry, a study on deep vision servo hand-eye coordination planning for sorting robots was conducted according to the problems of low recognition-sorting accuracy and efficiency in existing sorting robots. In order to maintain the symmetry of the picking robot, a small telescopic sorting robot with RealSense depth vision servo embedded in the manipulator was developed. The workspace and posture of picking parcels were analyzed, and the coordinate transformation model of hand-eye coordination was established for the “Eye-in-hand” mode. The hand-eye coordinated sorting test shows that the average positioning accuracy of the end in the X, Y and Z directions is 3.49 mm, 2.76 mm and 3.32 mm respectively, and the average time is 19.19 s. Among them, the average time for the mechanical arm to pick up the package from the initial position is 12.02 s, the average time for intermediate identification and calculation is 3.79 s, and the average time for placing the package is 6.9 s. The time consumed by robot arm’s action accounts for 79.8% of the whole cycle. The robot structure and the hand-eye coordination strategy with RealSense depth vision servo embedded in the robot can meet picking operation requirements, and the design of a picking robot proposed in this paper can greatly improve the coordination symmetry of fruit target recognition, detection, and picking.