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In warehouse operations, stock allocation that reduces shipment-work man-hours is necessary. The existing stock allocation recommended method (i.e., Sequential Storage Location Recommendation (SSLR) method) evaluates the storage period considering lot order and the delivery frequency of each product on the storing target pallet (PL), and stores the PL with a high delivery frequency and short storage period in a high-priority area. On the other hand, in SSLR, even if the PL is stored in a low-priority area, there is a problem that picking occurs from the low-priority area when days elapse and delivery from the PL is started. Therefore, we have developed a stock movement method in which the stored PL in the warehouse is evaluated with the evaluation value that considers the delivery frequency and storage period similar to SSLR, and which moves PLs with a short remaining storage period and high delivery frequency to a high-priority area. By combining SSLR and the proposed method, it is possible to reduce the number of deliveries from low-priority areas and prevent unnecessary occupancy of PLs having a long storage period in high-priority areas. Using simulations, it was confirmed that, by combining the method with SSLR, the man-hours for shipping can be reduced when compared to the case of SSLR alone. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigates the problems faced by an agricultural wholesaler arising from an inefficient random storage assignment policy. Research was conducted on warehouse operations and storage policies. The ABC classification technique was used to create a class-based storage assignment policy. This policy was then input into an algorithm that packs products according to how frequently they are ordered. The aim of the algorithm is to store fast-moving products closest to the shipping area. If this is done, the order picking times would decrease by an average of 3 hours 20 mins per day. This would increase the warehouse's throughput and profitability. [ABSTRACT FROM AUTHOR]

The use of shared storage warehouses in Norwegian museums has emerged in recent years as an effective way of saving resources and ensuring that cultural assets are adequately protected. This article and case study describes the work carried out in one of the biggest shared storage warehouses in Norway, located in a former munitions store near Stavanger, in the Rogaland region. It describes the efforts of the Samlingsteam Rogaland (Rogaland Collections Team), a travelling support unit assisting museums with cataloguing, digitisation, storage and conservation, created expressly to support museums and establish best practices in collection and storage management. Both offer innovative examples of the efficient use of resources dedicated to collection management and storage, which have been used as models to set up similar working systems in other areas of Norway. The existing literature is sparse both in reference to the storage warehouse and its transformation process as described in this case study, and to the Samlingsteam. The data used to assemble this case study was drawn from the professionals directly involved in the project, and their first-hand experiences as part of the working team. As such, this article represents a unique way of discovering the project and its completed work. [ABSTRACT FROM AUTHOR]

Businesses may save money and make better use of their resources by exercising reasonable control over the storage of power materials. Poor timeliness and ineffectiveness are the result of the old model's inability to fully use a significant volume of acquired data and information. This research presents a reinforcement learning-based material storage control model for the power grid to solve these issues. In order to achieve zero storage, real-time tracking of consumables is implemented. Material for large disasters need manufacturer coordination for storage. The reinforcement learning approach is used to dynamically stock and distribute emergency supplies. The three-tiered dynamic storage approach based on reinforcement learning that is suggested in this study is shown to be very efficient and significantly cuts operational expenses for power grid firms by verifying particular data. [ABSTRACT FROM AUTHOR]

A production inventory model developed for time dependent decaying items and demand rate are function of time with two-warehose. Production rate is time dependent. The holding cost for OW is taken as time dependent and for RW is constant. Shortages are not permitted. Cost minimization technique is used in this study. The aplications of the theoretical results are illustrated by the numerical example. [ABSTRACT FROM AUTHOR]

To effectively manage the automated warehouse, aiming at the optimization of goods location assignment, an optimization function for the assignment of storage location was established with the shelf stability and access efficiency as the goal according to the principle of storage space allocation. To optimize the picking location of the roadway stacker when exiting the warehouse, a mathematical function model was established by analyzing the picking strategy of the warehouse delivery operation. The research results showed that the optimization of the cargo space and the optimization of the stacker crane operation route can improve the overall operating efficiency. It is concluded that the model can reduce the cost of goods handling and storage, the time lost in the storage process and handling process, thereby decreasing logistics costs and increasing revenue. [ABSTRACT FROM AUTHOR]