959 research outputs found
Development of production planning system for shipbuilding using component-based development framework
No full textProduction planning is a key part of production management of manufacturing enterprises. Since computerization began, modern production planning has been developed starting with Material Requirement Planning (MRP), and today Enterprise Resource Planning (ERP), Advanced Planning and Scheduling (APS), Supply Chain Management (SCM) has been spreading and advanced. However, in the shipbuilding field, rather than applying these general-purpose production planning methodologies, in most cases, each shipyard has developed its own production planning system. This is because the applications of general-purpose production planning methods are limited due to the order-taking industry such as shipbuilding with highly complicated construction process consisting of millions of parts per ship. This study introduces the design and development of the production planning system reflecting the production environment of heavy shipyards in Korea. Since Korean shipyards such as Hyundai, Daewoo and Samsung build more than 10 ships per year (50–70 ships in the case of large shipyards), a planning system for the mixed production with complex construction processes is required. This study draws requirements using PI/BPR (process innovation and business process reengineering) methodology to develop a production planning system for shipyards that simultaneously build several ships. Then, CBD software development methodology was applied for the design and implementation of planning system with drawn requirements. It is expected that the systematic development procedure as well as the requirements and functional elements for the development of the shipyard production planning system introduced in this study will be able to present important guidelines in the related research field of shipbuilding management
The Effect of High Glass Fiber Content and Reinforcement Combination on Pulse-Echo Ultrasonic Measurement of Composite Ship Structures
No full textShip structures made of glass fiber-reinforced polymer (GFRP) composite laminates are considerably thicker than aircraft and automobile structures and more likely to contain voids. The production characteristics of such composite laminates were investigated in this study by ultrasonic nondestructive evaluation (NDE). The laminate samples were produced from E-glass chopped strand mat (CSM) and woven roving (WR) fabrics with different glass fiber contents of 30–70%. Approximately 300 pulse-echo ultrasonic A-scans were performed on each sample. The laminate samples produced from only CSM tended to contain more voids compared with those produced from a combination of CSM and WR, resulting in the relative density of the former being lower than the design value, particularly for high glass fiber contents of ≥50%. The velocity of the ultrasonic waves through the CSM-only laminates was also lower for higher glass fiber contents, whereas it steadily increased for combined CSM–WR laminates. Burn-off tests of the laminates further revealed that the fabric configuration of the combined CSM–WR laminates was of higher quality, prevented the formation of voids, and improved inter-layer bonding. These findings indicate that combined CSM–WR laminates should be used to achieve more accurate ultrasonic NDE of GFRP composite structures
Simulation-based deep reinforcement learning for multi-objective identical parallel machine scheduling problem
No full textIn the shipbuilding industry, traditional optimization studies based on linear programming and constraint programming have been conducted to solve mid-term or long-term scheduling problems. However, due to the extensive computational time, these methods face limitations in addressing short-term scheduling problems for the unit production systems of shipbuilding processes, where various environmental uncertainties must be considered. This study employs a deep reinforcement learning approach to develop a dynamic scheduling algorithm for the welding process in profile shops, considering the random arrival of materials and variability in processing time. The scheduling problems of the welding process are formulated as multi-objective identical parallel machine scheduling problems, aimed at minimizing both setup time and tardiness. This study proposes a novel Markov decision process model for the multi-objective scheduling problems for the welding process, incorporating setup requirements and due date-related constraints into the state representation, action modelling, and reward design. Additionally, based on the proposed Markov decision process model, this study develops a learning environment in which a discrete-event simulation model of the welding process is integrated for state transition considering the uncertainties in the welding process. In the training phase of the scheduling agent, the Proximal Policy Optimization algorithm is applied to learn the scheduling policy, which is approximated by deep neural networks. The performance of the proposed algorithm is validated in comparison to four priority rules (SSPT, ATCS, MDD, and COVERT) for various test scenarios with different workloads and levels of variability in processing time
Simulation of greenhouse gas emissions of small ships considering operating conditions for environmental performance evaluation
No full textThis study developed a method for simulating greenhouse gas (GHG) emissions considering changes in conditions that may occur during the actual operation of small ships. Additionally, we analyzed and compared the results of the proposed method with that of existing emission simulations according to life-cycle assessment (LCA), thus verifying the proposed method's effectiveness. Through the results of the study, we confirmed that the proposed method improves the simulation by considering emissions due to ship operation, whereas existing methods focus on emissions caused by raw material production. Additionally, the proposed method could identify and quantify the relationship between changes in operating conditions and GHG emissions. We expect this GHG emissions simulation technique to help improve the environmental performance of ships in the future. (C) 2020 Society of Naval Architects of Korea. Production and hosting by Elsevier B.V.Y
Analysis on Hull Block Erection Process Considering Variability
No full textThe hull block erection network process, which is performed during the master production planning stage of the shipyard, is frequently delayed because of limited resources, workspace, and block preparation ratio. In this study, a study to predict the delay with respect to the block erection schedule is conducted by considering the variability of the block preparation ratio based on the discrete event simulation algorithm. It is confirmed that the variation of the key event observance ratio is confirmed according to the variability caused by the block erection process, which has the minimum lead time in a limited resource environment, and the block preparation ratio. Furthermore, the optimal pitch value for the key event concordance is calculated based on simulation results.N
Development of Entering Order and Work-Volume Assignment Algorithms for the Management of Piping Components in Offshore Structure Construction
No full textIn the early 2010s, with rising oil prices and increasing purchase orders for offshore structures for deep-sea resource development, the shipyards that took these orders suffered unexpected losses. Unlike the construction of commercial carrier vessels, the construction of offshore structures necessary to develop deep-sea resources is difficult to manage due to the complexity of the outfitting process of the topside structure, which is a plant for gas and oil production and treatment. Piping components in particular, which comprise most of the design items, are difficult to manage because they involve 2 to 3 times the man-hours and up to 10 times the quantity of items compared to commercial carrier vessels. Due to not only high man-hours and quantity but also large fluctuations caused by design changes and long procurement lead times, process delays that result in delayed compensation frequently occurred. In response, Samsung Heavy Industries developed an integrated management system for piping components. This study describes the entering order optimization algorithm and work-volume assignment optimization algorithm, which are the core algorithms of this system. The entering order optimization algorithm determines the optimal installation order considering the procurement status of the piping components and the installation readiness status of the installation work site, through which it determines the entering order of the piping components. The algorithm seeks to accelerate the completion rate of installation of the piping components. Next, to minimize delivery delays of sub-contractors to the shipyard, this study developed a work-volume assignment optimization algorithm that can equalize the load on multiple sub-contractors considering the raw material readiness status and the production capacity of the sub-contractors, in terms of materials that must be ordered from external sub-contractors among the piping components whose entering order was determined. Finally, applying the algorithm developed using actual shipyard data resulted in an accelerated completion rate of installation and improved balance of load in terms of volume assigned to the sub-contractors
Machine Learning Methodology for Management of Shipbuilding Master Data
No full textThe continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE). (C) 2020 Society of Naval Architects of Korea. Production and hosting by Elsevier B.V.Y
Distributional reinforcement learning with the independent learners for flexible job shop scheduling problem with high variability
No full textMulti-agent scheduling algorithm is a useful method for the flexible job shop scheduling problem (FJSP). Also, the variability of the target system has to be considered in the scheduling problem that includes the machine failure, the setup change, etc. This study proposes the scheduling method that combines the independent learners with the implicit quantile network by modeling of the FJSP with high variability to the form of the multi-agent. The proposed method demonstrates superior performance compared to the several known heuristic dispatching rules. In addition, the trained model exhibits superior performance compared to the reinforcement learning algorithms such as proximal policy optimization and deep Q-network.Y
Simulation-based planning system for shipbuilding
No full textTo maintain the competitiveness of shipyards in the current, difficult situation, further improvements to technology are necessary. Recently, various production technologies have been developed to advance the shipyard production environment under the influence of the Industry 4.0 toward automation, smart factories, and intelligent planning systems. To contribute to such efforts, we introduce a research case aimed at a simulation-based shipbuilding planning system. Shipbuilding planning processes are reorganized using an integrated planning and scheduling system, and a process-centric discrete event system simulation is used to enhance planning quality. Moreover, the proposed simulation-based planning system is applied to an actual shipbuilding process, proving that it could enhance the quality of production planning through several productivity evaluation indices.Y
Productivity Improvement Strategies Using Simulation in Offshore Plant Construction
No full textSince the global financial crisis of 2008, the global shipbuilding industry has changed considerably and placed major Korean shipbuilding companies (mostly common carrier builders) in a precarious position. Current competitiveness in the global shipbuilding industry has been attracted by low labor costs in China. The Korean shipbuilding industry, with heavy industry as the central figure, is attempting to increase its share of offshore plant construction to develop deep-sea resources. This highlights the most outdated part of offshore plant in Korea, the development technology, which includes front-end engineering development and deep-water floaters, unlike in more advanced companies in the United States, where development technology has advanced rapidly. This has prompted the Korean government to invest most of its R&D funds in the areas of product and equipment development. However, mega shipbuilders such as Samsung, Hyundai, and Daewoo have incurred considerable losses at construction stages because of major delays in production. By contrast, international engineering companies have supported development engineering. The considerable financial losses incurred by mega shipbuilders are believed to be caused by a lack of quality management with respect to the massive production quantities and complexities involved in outfitting topside structures. This study investigates a strategy to advance production management specialized for the offshore plant business and describes a robust and sustainable technical roadmap based on current information technologies (IT) and simulation-based management methods.N
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