In order to explore the application of robots in intelligent supply-chain and digital logistics, and to achieve efficient operation, energy conservation, and emission reduction in the field of warehousing and sorting, we conducted research in the field of unmanned sorting and automated warehousing. Under the guidance of the theory of sustainable development, the ESG (Environmental Social Governance) goals in the social aspect are realized through digital technology in the storage field. In the picking process of warehousing, efficient and accurate cargo identification is the premise to ensure the accuracy and timeliness of intelligent robot operation. According to the driving and grasping methods of different robot arms, the image recognition model of arbitrarily shaped objects is established by using a convolution neural network (CNN) on the basis of simulating a human hand grasping objects. The model updates the loss function value and global step size by exponential decay and moving average, realizes the identification and classification of goods, and obtains the running dynamics of the program in real time by using visual tools. In addition, combined with the different characteristics of the data set, such as shape, size, surface material, brittleness, weight, among others, different intelligent grab solutions are selected for different types of goods to realize the automatic picking of goods of any shape in the picking list. Through the application of intelligent item grabbing in the storage field, it lays a foundation for the construction of an intelligent supply-chain system, and provides a new research perspective for cooperative robots (COBOT) in the field of logistics warehousing.

Order picking is a crucial operation in the storage industry, with a significant impact on storage efficiency and cost. Responding quickly to customer demands and shortening picking time is crucial given the random nature of order arrival times and quantities. This paper presents a study on the order-picking process in a distribution center, employing a “parts-to-picker” system, based on dynamic order batching and task optimization. Firstly, dynamic arriving orders with uncertain information are transformed into static picking orders with known information. A new method of the hybrid time window is proposed by combining fixed and variable time windows, and an order consolidation batch strategy is established with the aim of minimizing the number of target shelves for picking. A heuristic algorithm is designed to select a shelf selection model, taking into account the constraint condition that the goods on the shelf can meet the demand of the selection list. Subsequently, task division of multi-AGV is carried out on the shelf to be picked, and the matching between the target shelf and the AGVs, as well as the order of the AGVs to complete the task of picking, is determined. A scheduling strategy model is constructed to consider the task completion time as the incorporation of moving time, queuing time, and picking time, with the shortest task completion time as the objective function and AGV task selection as the decision variable. The improved ant colony algorithm is employed to solve the problem. The average response time of the order batching algorithm based on a hybrid time window is 4.87 s, showing an improvement of 22.20% and 40.2% compared to fixed and variable time windows, respectively. The convergence efficiency of the improved ant colony algorithm in AGV task allocation is improved four-fold, with a better convergence effect. By pre-selecting the nearest picking station for the AGVs, the multi-AGV picking system can increase the queuing time. Therefore, optimizing the static picking station selection and dynamically selecting the picking station queue based on the queuing situation are proposed. The Flexsim simulation results show that the queue-waiting and picking completion times are reduced to 34% of the original, thus improving the flexibility of the queuing process and enhancing picking efficiency.

In order to improve the picking efficiency of warehouses, shorten the time cost and promote the development of the logistics industry, this study analyzes the routing strategies in fishbone layout warehouses under the class-based storage strategy. The fishbone layout was divided into three storage areas for class A, class B, and class C items according to the proportion using the straight line, to meet the classification requirements of items. Under the class-based storage strategy, to evaluate the performance of the return routing strategy and the S-shape routing strategy, the stochastic models of the expected walking distance of the two routing strategies in the fishbone layout warehouse are established by calculating the sum of the expected walking distances in diagonal cross-aisles and picking aisles. Finally, the stochastic models of the two routing strategies are simulated and verified, and the impacts of the two routing strategies on walking distances are analyzed by comparing the expected distances under different ordering frequencies and space allocation strategies. The numerical results show that the return routing strategy has an advantage over the S-shape routing strategy when determining the relevant parameters of the fishbone layout and picking orders. Meanwhile, it also provides a theoretical basis for research on stochastic models of routing strategies in fishbone layout warehouses under the class-based storage strategy.

Order picking is the part with the highest proportion of operation cost and time in the warehouse. The characteristics of small-batch and multi-frequency current orders reduce the applicability of the traditional layout in the warehouse. Besides this, the improvement of the layout will also affect the picking path, such as the Chevron warehouse layout, and at present, there is a lack of research on order picking with multiple picking locations under non-traditional layouts. In order to minimize the order picking cost and time, and expand the research in this field, this paper selects the Chevron layout to design and describe the warehouse layout, constructs the picking walking distance model of Return-type, S-type and Mixed-type path strategies in the random storage Chevron layout warehouse, and uses the Cuckoo Search (CS) algorithm to solve the picking walking distance generated by the Mixed-type path. Compared with the existing single-command order picking research, the order picking problem of multi picking locations is more suitable for the reality of e-commerce warehouses. Moreover, numerical experiments are carried out on the above three path strategies to study the impact of different walking paths on the picking walking distance, and the performance of different path strategies is evaluated by comparing the order picking walking distance with the different number of locations to be picked. The results show that, among the three path strategies, the Mixed-type path strategy is better than the Return-type path strategy, and the average optimization proportion is higher than 20%. When the number of locations to be picked is less than 36, the Mixed-type path is better than the S-type path. With the increase of the number of locations to be picked, the Mixed-type path is gradually worse than the S-type path. When the number of locations to be picked is less than 5, the Return-type path is better than the S-type path. With the increase of the number of locations to be picked in the order, the S-type path is gradually better than the Return-type path.