This paper addresses an algorithm for optimizing the storage location assignment problem in a warehouse operating under a mixed-shelves strategy. Under this strategy, each SKU (Stock Keeping Unit) can be assigned to multiple storage locations, allowing various SKUs to be positioned close to one another and thus improving order-picking efficiency. The mixed-shelves strategy is particularly effective for e-commerce retailers where many kinds of products are ordered in small quantities. However, the number of storage positions to be considered increases substantially, making the problem more complex.

To address this issue, this paper proposes a simulated annealing-based algorithm (SA) for simultaneously solving the storage location assignment problem and the order picker routing problem. The proposed SA employs a neighborhood structure based on a normal distribution that enables the algorithm to consider SKU demand efficiently; in this mechanism, high-demand SKUs are assigned to shelves near the depot with higher probability. In addition, a correlation-based neighborhood is introduced to account for the frequency with which SKUs are ordered together. In this neighborhood, pairs of SKUs with high correlation are assigned to the same shelf with higher probability. Moreover, to accommodate various types of orders, both neighborhoods are selected probabilistically and adaptively during the search process.

The effectiveness of the proposed algorithm was validated using real-sized datasets. In numerical experiments, compared with existing methods that do not consider SKU demand or SKU correlations, the proposed algorithm significantly reduced total travel distance. These findings highlight the importance of simultaneously considering both demand and correlation in the storage location assignment problem.

The purpose of this study is to investigate the presence of moderation effects caused by social capital and psychological safety on the relationship between the social sharing of emotional experiences and the flexibility of organizations. Based on a review of the literature on the flexibility of organizations, social sharing of emotional experiences, social capital and psychological safety, hypotheses were derived regarding the moderations of social capital and psychological safety onto the relationship between the social sharing of emotional experiences and the flexibility of organizations. A questionnaire survey was conducted with employees in a variety of industries as participants. Participants' practices regarding the social sharing of emotional experiences, their perceptions of the flexibility of organizations of their own institutes, social capital, their perception of the psychological safety of the institutes, as well as their demographic information were measured using the questionnaires. The results suggest the possibility of moderation effects on the relationship between the social sharing of emotional experiences and the flexibility of organizations, caused by psychological safety, the bonding social capital, and the bridging social capital. This study implies the importance of cultivating social capital and psychological safety from the viewpoint of the social sharing of emotional experiences being able to improve the flexibility of organizations.

In recent years, the impacts on the manufacturing industry of natural disasters and rapid changes in market environments, which affect procurement and supply activities that are essential for production, have caused an increase in the importance of supply chain disruption risk management. This study proposes a cooperative supply chain model that focuses on the trading conditions between manufacturers and suppliers under uncertain fluctuations in component procurement and product supply. Based on this model, the present study develops a discrete-event simulation that incorporates supply chain disruption risks that arise from insufficient procurement quantities caused by natural disasters or unexpected demand surges. The simulation is used to examine the impact of such disruptions on overall supply chain profits and to analyze how setting upper and lower bounds on trading quantities influences supply chain disruption risks.

As efforts to utilize various types of data accumulated by companies continue to advance, customer complaints are increasingly being recognized as a valuable source of information for marketing activities. Although the volume of such data makes manual analysis difficult, the application of machine learning offers a promising solution. By analyzing complaint data, it is expected that companies will be able to identify current issues and customer needs, leading to improvements in products and services, as well as the development of more effective employee training programs. Customer complaint data is typically collected through various channels. It generally consists of both structured text data, which is recorded with predefined tags, and unstructured text, which is written freely by individuals. However, the data often varies in structure and expression depending on the author, and the level of detail is not consistent across all entries. In addition, complaint data tends to include a large amount of supplementary information unrelated to the actual complaint, which becomes noise in the analysis. While various machine learning-based review analysis methods have been proposed, applying them directly to such inconsistent and noisy text data often makes it difficult to extract meaningful insights. To address this, the present study targets large-scale customer complaint data that is inconsistent in format and includes significant noise, with the aim of developing an analysis method that enables the extraction of useful insights. The present approach first extracts meaningful information from the complaint data and then applies conventional analytical methods. Specifically, a method is proposed that combines pattern matching with context-based extraction techniques to isolate text segments suitable for analysis. By applying the proposed framework, document classification techniques such as labeling and clustering can be effectively utilized in accordance with complaint content. This enables not only the identification of overall trends in the data but also a deeper understanding of current issues and customer needs. Finally, the effectiveness of the proposed method is demonstrated by applying it to real-world complaint data.