One of the controversial issues in manufacturing systems is bottleneck. Managers and engineers are trying to find methods to eliminate the bottlenecks and waiting times in the production line. More over the manufacturing companies are striving to sustain their competiveness by decreasing the bottlenecks, total cost and increasing the productivity. The objective of this study is applying the computer simulation to analysis the production line bottlenecks. To achieve this goal a color manufacturing line was selected as a case study and the basic application of arena 13.9 software. Finally the some modifications in the simulation model are proposed to improve the production line as well as to decrease the bottleneck.

No abstract

Patent classification is an expensive and time-consuming task that has conventionally been performed by domain experts. However, the increase in the number of filed patents and the complexity of the documents make the classification task challenging. The text used in patent documents is not always written in a way to efficiently convey knowledge. Moreover, patent classification is a multi-label classification task with a large number of labels, which makes the problem even more complicated. Hence, automating this expensive and laborious task is essential for assisting domain experts in managing patent documents, facilitating reliable search, retrieval, and further patent analysis tasks. Transfer learning and pre-trained language models have recently achieved state-of-the-art results in many Natural Language Processing tasks. In this work, we focus on investigating the effect of fine-tuning the pre-trained language models, namely, BERT, XLNet, RoBERTa, and ELECTRA, for the essential task of multi-label patent classification. We compare these models with the baseline deep-learning approaches used for patent classification. We use various word embeddings to enhance the performance of the baseline models. The publicly available USPTO-2M patent classification benchmark and M-patent datasets are used for conducting experiments. We conclude that fine-tuning the pre-trained language models on the patent text improves the multi-label patent classification performance. Our findings indicate that XLNet performs the best and achieves a new state-of-the-art classification performance with respect to precision, recall, F1 measure, as well as coverage error, and LRAP.

No abstract

Supply chain is a network of different business units which focuses on integration among all units in order to produce and distribute end products to the customer. Nowadays, due to increased uncertainty in customer demand, it is necessary to be sure that the supply chain networks operate as efficient as possible in order to satisfy the customer demand at the lowest cost. Enhancing the efficiency of the supply chain necessitates the interaction between different members of the supply chain which can be achieved through supply chain management. Hence, the development of modeling approach in order to understand and analyze the dynamic behavior of supply chains is brought into consideration. This paper proposes a system dynamic simulation model for manufacturing supply chain. System dynamic is used as suitable method to understand and analyze the interactions of various components via feedback structure. The objective of this paper is to simulate the manufacturing supply chain of an electronic manufacturing company in Malaysia. The simulation model is used to study the system’s behavior (in terms of production rate, inventory levels, and backlog orders) under two different operational conditions (named as fixed and varied capacity policies) and compare their efficiency in terms of total cost. The analysis shows that the proposed operational condition, which is varied capacity policy, improves the system efficiency in terms of cost.