Performance Analysis of Picking Routing Strategies in the Leaf Layout Warehouse

Abstract

The routing strategy for order picking is an important factor in the efficiency of warehouse picking, and improvements to the warehouse layout provide more routing options for picking. The number of storage locations to be visited during the picking operation also has an impact on the selection of routing strategies. In order to achieve an effective improvement in the efficiency of picking operations in warehouse distribution centers, this paper focuses on the leaf warehouse layout based on the previous single-command operation strategy and extends it to study the multi-command operation strategy, in which three heuristic routing strategies, the S-shape, the return, and the composite, are introduced to solve the walking distance problem of picking operations, with the study of the selection of the routing strategy for different numbers of storage locations to be visited. Based on the distance equation between any two storage locations to be visited in the leaf layout warehouse, travel distance models corresponding to the three routing strategies in the picking operation are constructed, and the cuckoo search algorithm is introduced to solve and calculate the travel distance of the composite strategies for the experiments. In addition, the computational experiments of the three path strategies are carried out according to the different numbers of storage locations to be visited in the picking operation. By analyzing the numerical results, we find that the composite strategy has the best overall results among the three routing strategies, with the average values of optimization rates exceeding 30% (the S-shape) and 40% (the return), respectively. At the same time, the return strategy outperforms the S-shape strategy when the number of locations to be visited is less than seven. As the number of locations to be visited increases, the S-shape strategy gradually outperforms the return strategy. From a managerial and practical perspective, compared to the single-command operation strategy that is the focus of the current research, the multi-command operation strategy we studied is more relevant to the actual situation of order merging picking in warehouses and can effectively improve the efficiency of picking operations, the competitiveness of enterprises, and customer satisfaction of e-commerce enterprises.

Model to Increase the Level of Service Using the 5S Tools, Work Standardization, SLP and Demand Forecasting in a Peruvian Quick Commerce SME

“…Currently, among the most influential factors for consumer purchase decisions in the e-commerce industry are delivery time [7] and product availability [8]. According to Zhou et al [9], picking time accounts for more than 50% of the total operational time, which also means 55% of the total cost of warehouse operations. On the other hand, authors such as Marques et al [10] state that the fulfillment rate or level of service provided by companies in this sector depends directly on the Out of Stock (OOS) rate.…”

Traveling Route Derivation Method Allowing an Arbitrary Layout of Warehouses Using A* and Genetic Algorithms

“…For this reason, considerable research is currently being conducted to improve the efficiency of picking operations. The main approaches to reduce walking time in picking operations include the optimization of commodity placement [3]∼ [5], order slip assignment methods [6]∼ [8], and traveling routes [9]∼ [11]. In addition, owing to the high demand and tight deadlines in the logistics industry, an increasing number of warehouses are adopting autonomous mobile robots (AMRs).…”