Most influential countries in the international medical device trade: Network-based analysis.

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, the international medical device trade has received extensive attention. To maintain the domestic supply of medical devices, some countries have sought multilateral trade cooperation or simply implemented export restrictions, which has exacerbated the instability and fragility of the global medical device market. It is crucial for government policymakers to identify the most influential countries in the international medical device trade and nip exports in the bud. However, few efforts have been made in previous studies to explore various countries' influence on the international medical device trade in light of their intricate trade relationships. To fill these research gaps, this study constructs a global medical device trade network (GMDTN) and explores the criticality of various countries from a network-based perspective. The evolution patterns and geographical distribution of influence among countries in the GMDTN are revealed. Details on the ways in which the influence of some crucial countries has formed are provided. The results show that the global medical device trade market is export oriented. The formation of some countries' strong influence may be due to their large number of trading partners or the deep dependence of some of those trading partners on that country (namely, breadth- or depth-based patterns). It is worth noting that the US has a dominant position in the international medical device trade in terms of both breadth and depth. In addition, some countries play a critical role as intermediate points in the influence formation process of other countries, although these countries are not critical direct trading partners. The findings of this study provide implications for policymakers seeking to understand the influence of countries on the international medical device trade and to proactively prepare responses to unexpected changes in this trade.

Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms.

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.