Search

As a product of modern development, logistics plays a significant role in economic growth with its advantages of integrated management, unified operations, and speed. With the rapid advancement of technology and economy, traditional manual storage and retrieval methods can no longer meet industry demands. Achieving efficient storage and retrieval of goods on densely packed, symmetrically shaped logistics shelves has become a critical issue that needs urgent resolution. The brain storm optimization (BSO) algorithm, introduced in 2010, has found extensive applications across various fields. This paper presents a modified BSO algorithm (MBSO) aimed at addressing the scheduling challenges of double-end automated storage and retrieval systems (DE-AS/RSs). Traditional AS/RSs suffer from slow scheduling efficiency and the current heuristic algorithms exhibit low accuracy. To overcome these limitations, we propose a new scheduling strategy for the stacker to select I/O stations in DE-AS/RSs. The MBSO incorporates two key enhancements to the basic BSO algorithm. First, it employs an objective space clustering method in place of the standard k-means clustering to achieve more accurate solutions for AS/RS scheduling problems. Second, it utilizes a mutation operation based on a greedy strategy and an improved crossover operation for updating individuals. Extensive comparisons were made between the well-known heuristic algorithms NIGA and BSO in several specific enterprise warehouse scenarios. The experimental results show that the MBSO has significant accuracy, optimization speed, and robustness in solving scheduling of AS/RSs.

Logistics plays an important role in the field of global economy, and the storage and retrieval of tasks in a warehouse which has symmetry is the most important part of logistics. Generally, the shelves of a warehouse have a certain degree of symmetry and similarity in their structure. The storage and retrieval efficiency directly affects the efficiency of logistics. The efficiency of the traditional storage and retrieval mode has become increasingly inconsistent with the needs of the industry. In order to solve this problem, this paper proposes a greedy algorithm based on cost matrix to solve the path planning problem of the automatic storage and retrieval system (AS/RS). Firstly, aiming at the path planning mathematical model of AS/RS, this paper proposes the concept of cost matrix, which transforms the traditional storage and retrieval problem into the element combination problem of cost matrix. Then, a more efficient backtracking algorithm is proposed based on the exhaustive method. After analyzing the performance of the backtracking algorithm, combined with some rules, a greedy algorithm which can further improve efficiency is proposed; the convergence of the improved greedy algorithm is also proven. Finally, through simulation, the time consumption of the greedy algorithm is only 0.59% of the exhaustive method, and compared with the traditional genetic algorithm, the time consumption of the greedy algorithm is about 50% of the genetic algorithm, and it can still maintain its advantage in time consumption, which proves that the greedy algorithm based on cost matrix has a certain feasibility and practicability in solving the path planning of the automatic storage and retrieval system.