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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.

Abstract With the continuous and rapid growth of transport demand, scheduling strategy of warehouse has become a key issue in the field of logistics transportation. The structural differences of the warehouse, the automated storage and retrieval system (AS/RS) model and the two‐end dual stackers scheduling model (TDSM) are considered, and a new improved genetic algorithm (NIGA) is proposed. It can adjust the algorithm structure according to the density of population fitness value, and effectively optimize the stacker path. In the TDSM, an improved anti‐collision principle is proposed to avoid collision of two stackers. Besides, combined with the optimal anti‐collision boundary inspection mechanism, the best working area for the two stackers is allocated by using NIGA. Finally, the new improved GA is compared with GA and the adaptive GA on specific storage and retrieval tasks. The simulation results show that the proposed NIGA well outperforms other GAs in most instances, which indicates that it is an effective approach for the AS/RS and the TDSM scheduling optimization problem.

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.

The influence of the varied thickness of antiferromagnetic (AFM) and ferrimagnetic (FiM) nanostructure in MnO@Mn3O4 core-shell nanoparticles (NPs) on the exchange bias effect has been investigated. The MnO@Mn3O4 core-shell NPs with varied radius of MnO core (RMnO) and thickness of Mn3O4 shell (TMn3O4) have been synthesized by in-situ oxidation of the pure MnO NPs in organic solution containing a mild oxidant at different temperatures. The exchange bias effect can be tuned by changing the RMnO (TMn3O4) of the MnO@Mn3O4 core-shell NPs. The strongest exchange coupling effect of both 17.2 and 25.0 nm MnO@Mn3O4 core-shell NPs occur when RMnO=TMn3O4=R/2. It is expected that the finding of the control of exchange bias effect by varied RMnO and TMn3O4 in inverted core-shell NPs is helpful for the further understanding of the exchange bias mechanism.

The influence of the varied thickness of antiferromagnetic (AFM) and ferrimagnetic (FiM) nanostructure in MnO@Mn3O4 core-shell nanoparticles (NPs) on the exchange bias effect has been investigated. The MnO@Mn3O4 core-shell NPs with varied radius of MnO core (RMnO) and thickness of Mn3O4 shell (TMn3O4) have been synthesized by in-situ oxidation of the pure MnO NPs in organic solution containing a mild oxidant at different temperatures. The exchange bias effect can be tuned by changing the RMnO (TMn3O4) of the MnO@Mn3O4 coreshell NPs. The strongest exchange coupling effect of both 17.2 and 25.0 nm MnO@Mn3O4 core-shell NPs occur when RMnO=TMn3O4 = R/2. It is expected that the finding of the control of exchange bias effect by varied RMnO and TMn3O4 in inverted core-shell NPs is helpful for the further understanding of the exchange bias mechanism. [ABSTRACT FROM AUTHOR]