As the shipbuilding industry is an engineering-to-order industry, different types of products are manufactured according to customer requests, and each product goes through different processes and workshops. During the shipbuilding process, if the product is not able to go directly to the subsequent process due to physical constraints of workshop, it temporarily waits in a stockyard. Since the waiting process involves unpredictable circumstances, plans regarding time and space cannot be established in advance. Therefore, unnecessary movement often occurs when ship blocks enter or depart from the stockyard. In this study, a reinforcement learning approach was proposed to minimise rearrangement in such circumstances. For this purpose, an environment in which blocks are arranged and rearranged was defined. Rewards based on the simplified rules were logically defined, and simulation was performed for quantitative evaluation using the proposed reinforcement learning algorithm. This algorithm was verified using an example model derived from actual data from a shipyard. The method proposed in this study can be used not only to the arrangement problem of ship block stockyards but also to the various arrangement and allocation problems or logistics problems in the manufacturing industry.
Given the stricter environmental regulations and rising oil prices, means of transportation based on eco-friendly fuel are drawing increased attention. Electric propulsion systems (EPSs) have been applied to various means of transportation, including cars, and many studies have examined ships equipped with EPSs. Generally, because of the low energy density of the battery, EPS-equipped vehicles experience the disadvantages of limited operating time and distance compared to fossil fuel-based vehicles. In this study, we developed an algorithm that determines the optimal electric motor and battery specifications for the basic requirements of a small craft equipped with an EPS. Moreover, to control the stability of the craft, we developed an algorithm that optimally arranges EPS components, wherein the center of gravity is used as an object function. The differential evolution algorithm was used for optimization, and the effectiveness was verified by applying this algorithm to the actual design of a small craft. The proposed algorithm represents the research results for determining the basic EPS specifications for a small craft and deriving an optimal arrangement for these specifications. This algorithm is expected to be effectively applied to design a new electric propulsion ship or to convert an existing ship to an electric propulsion ship.
Nowadays, digital twins exist everywhere in various fields. However, an analysis of existing applications in manufacturing and logistics revealed that many entirely apply the concept. To identify when a complete implementation of the concept is beneficial, we analyse the need and the implications within production logistics. This study also presents an architecture supporting integrating a digital twin into production logistics and a corresponding application scenario. Based on this, we have derived practical applications. Each application is applied to different situations, and actual benefits can overcome the limitations of the previous studies.
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