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The purpose of this paper is to introduce an innovative transversal tubular braid texture and to study the elastic behavior of its 3 D printed structure comparatively to 3 D printed longitudinal tubular braid texture (maypole) to be used as reinforcement.

Regarding the lack of proper machines for the production of the proposed texture, the structure of samples was produced as a tubular lattice braid texture using a 3 D printer with the fused deposition modeling method subsequent to simulation by Rhinoceros software. The produced specimens were composited by polyurethane resin. The composite samples were evaluated by the split disk mechanical test to obtain their hoop stress. The structures of the reinforced composites were theoretically analyzed by ANSYS software.

The results of the mechanical test and theoretical analysis showed that the composites reinforced with transversal tubular lattice braid have higher strength compared to the composites reinforced with longitudinal ones. This assured that the composite reinforced by transversal tubular lattice braid is reliable to be used as high-performance tube for different applications.

Further work is carried out to produce the innovated complex structure continuously by a specially designed machine and fibrous materials to reinforce tubular composites in an industrial continual process to be applied for high-pressure fluids flows.

Supplier segmentation provides companies with suitable policies to control each segment, thereby saving time and resources. Sustainability has become a mandatory requirement in competitive business environments. This study aims to develop a clustering-based approach to sustainable supplier segmentation.

The characteristics of the suppliers and the aspects of the purchased items were considered simultaneously. The weights of the sub-criteria were determined using the best-worst method. Then, the K-means clustering algorithm was applied to all company suppliers based on four criteria. The proposed model is applied to a real case study to test the performance of the proposed approach.

The results prove that supplier segmentation is more efficient when using clustering algorithms, and the best criteria are selected for sustainable supplier segmentation and managing supplier relationships.

This study integrates sustainability considerations into the supplier segmentation problem using a hybrid approach. The proposed sustainable supplier segmentation is a practical tool that eliminates complexity and presents the possibility of convenient execution. The proposed method helps business owners to elevate their sustainable insights.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.