DATA DRIVEN PERFORMANCE EVALUATION IN SHIPBUILDING

Rapid development in data science keeps paving the way for use of data for many purposes in shipbuilding, both for product development and production, such as Industry 4.0 have been developing many industries. Similar to other industries the evaluation of performance in shipbuilding is the key to success which is closely connected to productivity and lowered costs. Data mining and analysis techniques are used to create effective algorithms to evaluate the performance, also by means of cost estimation based on parametric methods. However, it is usually not very clear how data are collected, organised and prepared for analysing and deriving valuable knowledge as well as algorithms. In most of the cases, having this data requires either continuous investment in expensive software or expensive external expertise which are generally not available for small and medium size shipyards. In this study, considering the needs of the small and medium sized shipyards, a step-by-step methodology is proposed which could be easily applied with widely available low budget software. The application is demonstrated with a case to evaluate the performance of early phase structural design with a data driven cost estimation algorithm

Maintenance/repair and production-oriented life cycle cost/earning model for ship structural optimisation during conceptual design stage

The aim of this paper is to investigate the effect of the change in structural weight due to optimisation experiments on life cycle cost and earning elements using the life cycle cost/earning model, which was developed for structure optimisation. The relation between structural variables and relevant cost/earning elements are explored and discussed in detail. The developed model is restricted to the relevant life cycle cost and earning elements, namely production cost, periodic maintenance cost, fuel oil cost, operational earning and dismantling earning. Therefore it is important to emphasise here that the cost/earning figure calculated through the developed methodology will not be a full life cycle cost/earning value for a subject vessel, but will be the relevant life cycle cost/earning value. As one of the main focuses of this paper is the maintenance/repair issue, the data was collected from a number of ship operators and was solely used for the purpose of regression analysis. An illustrative example for a chemical tanker is provided to show the applicability of the proposed approac

조선소 소조립 주판 배치 시뮬레이션 기반 공간 계획 시스템 개발

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 조선해양공학과, 2021. 2. 우종훈.In the shipbuilding subassembly process, space is one of the main resource constraints limiting production capacity. To efficiently manage the space resource, how subassembly parts will occupy the workshop floor need to be analyzed before production. In this study, a methodology of controlling the subassembly space resource is proposed. In this methodology, first the impact of space on the production capacity for a given time period is analyzed. This analysis is performed through a framework of discrete event simulation modelling the subassembly process using subassembly part scheduling algorithm and spatial arrangement planning algorithm. The production schedules feasibility in terms of space resource utilization is examined through the simulation model. Second, a detailed subassembly part arrangement layout is generated using a genetic algorithm based spatial arrangement algorithm. The algorithm is used to efficiently utilize the work area and accurately predict the amount of area required for a subassembly production lot. After the methodology is presented, a case study of the simulation model is analyzed, and the performance of the genetic algorithm based spatial arrangement algorithm is evaluated.조선소의 소조립 공정에서 공간 자원은 생산 능력을 결정하는 주요 자원이다. 공간 자원을 효율적으로 관리하기 위해서는 생산 계획 검토 단계에서 소조립품 배치 위치 및 공간 활용에 대한 분석이 필요하다. 본 연구에서는 이산사건 시뮬레이션 모델링 및 공간 배치 알고리즘에 기반한 소조립 공간 자원 활용 계획을 분석하는 방법론을 제안한다. 시뮬레이션 모델과 모델 내 탑재되어 있는 소조립 계획 및 공간 배치 모듈을 활용하여 생산 계획 기간동안의 생산성 및 계획준수율에 공간 자원이 미치는 영향을 분석할 수 있다. 이를 통해 생산 계획의 타당성을 검증하고 개선 방안 도출에 도움이 될 수 있다. 다음으로 유전알고리즘에 기반한 소조립 주판 배치 레이아웃 생성 알고리즘 및 방법론을 제안한다. 소조립 주판 배치 레이아웃 생성 알고리즘을 통해 소조립 작업장 공간 활용률을 높일 수 있으며 작업에 필요한 공간을 정확하게 예측할 수 있다. 마지막으로 소조립 생산 사례를 시뮬레이션 모델로 분석하고 소조립 주판 배치 레이아웃 생성 알고리즘의 성능을 평가하였다.1. Introduction 1 1.1 Study background 1 1.2 Past research 4 1.3 Research scope and methodology 6 2. Defining the subassembly process 8 2.1 Defining the part object 8 2.2 Defining the workshop 11 2.3 Defining the scheduling methodology 13 3. Developing the simulation model 15 3.1 Representing the product object 15 3.2 Subassembly part scheduling algorithm 18 3.3 Spatial arrangement planning algorithm 22 3.3.1 Factors in evaluating algorithm result 29 3.4 Simulation case study and analysis 30 3.5 System based on simulation model 35 4. Detailed spatial arrangement 38 4.1 Motivation and relation to simulation model 38 4.2 Algorithm structure and details 41 4.3 Detailed arrangement layout system 47 4.4 Algorithm evaluation and analysis 48 5. Conclusion 51Maste

DATA MINING METHODOLOGY FOR DETERMINING THE OPTIMAL MODEL OF COST PREDICTION IN SHIP INTERIM PRODUCT ASSEMBLY

In order to accurately predict costs of the thousands of interim products that are assembled in shipyards, it is necessary to use skilled engineers to develop detailed Gantt charts for each interim product separately which takes many hours. It is helpful to develop a prediction tool to estimate the cost of interim products accurately and quickly without the need for skilled engineers. This will drive down shipyard costs and improve competitiveness. Data mining is used extensively for developing prediction models in other industries. Since ships consist of thousands of interim products, it is logical to develop a data mining methodology for a shipyard or any other manufacturing industry where interim products are produced. The methodology involves analysis of existing interim products and data collection. Pre-processing and principal component analysis is done to make the data “user-friendly” for later prediction processing and the development of both accurate and robust models. The support vector machine is demonstrated as the better model when there are a lower number of tuples. However as the number of tuples is increased to over 10000, then the artificial neural network model is recommended

Global pain local pain : the globalization of manufacturing has produced cheaper goods for everyone, but the trend has cost hundreds of thousands of jobs in the Fifth District

Manufactures ; Federal Reserve District, 5th ; Unemployment

메타 휴리스틱 최적화를 이용한 기계학습 기반 선박 건조 공정 리드 타임 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2021.8. 하오유주.In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is basic data that is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it mostly makes the plan by calculating the average lead times derived from historical data. Therefore, to understand the complex relationship between lead time and other influencing factors, this study proposes to use machine learning (ML) algorithms, support vector machine (SVM) and artificial neural network (ANN), which are frequently applied in prediction fields. Moreover, to improve prediction accuracy, this study proposes to apply meta-heuristic algorithms to optimize the parameters of the ML models. This thesis builds hybrid models, including meta-heuristic-ANN, meta-heuristic-SVM models. In addition, this study compares model’s performance with each other. In searching for the ML model’s parameters, the results point out that the new self-organizing hierarchical particle swarm optimization (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC) algorithm is superior in terms of performance. More importantly, the test results demonstrate that the integrated models, based on NHPSO-JTVAC, have the smallest mean absolute percentage error (MAPE) test error in the three shipyard block process data sets, 11.79%, 16.03% and 16.45%, respectively. The results also demonstrate that the built models based on NHPSO-JTVAC can achieve further meaningful enhancements in terms of prediction accuracy. Overall, the NHPSO–JTVAC-SVM, NHPSO–JTVAC-ANN models are feasible for predicting the lead time in shipbuilding.조선 산업에서 각 공정은 리드 타임을 가진다. 리드 타임이란 공정 시작과 종료 간에 시간으로, 고효율의 생산계획과 체계적 생산관리를 위해 매우 중요한 지표이다. 특히, 생산 계획 단계에서 정확한 리드타임 예측은 납기 준수를 위한 계획 수립을 위해 매우 중요하다. 그러나 기존의 예측 방법은 과거 데이터의 평균값을 사용했기 때문에 정확도가 매우 떨어졌다. 따라서 본 연구에서는 리드 타임과 다른 영향 요인 간의 복잡한 관계를 이해하기 위해 예측 분야에서 자주 적용되는 머신 러닝 (ML) 모델인 서포트 벡터 머신 (SVM) 및 인공 신경망 (ANN) 적용을 제안한다. 또한, 기계학습 모델 예측 정확도를 향상시키기 위해 메타 휴리스틱 알고리즘을 적용하여 모델의 파라미터를 최적화하고자 한다. 본 연구는 meta-heuristics-ANN, meta-heuristics-SVM 모델을 포함하는 하이브리드 모델을 구축한다. 더불어, 본 연구는 메타 휴리스틱 알고리즘 기반으로 최적화된 기계학습 모델의 성능을 서로 비교한다. 연구 결과를 통해, ML 모델의 파라미터를 탐색하는 과정에서 particle swam optimization (PSO)의 enhanced 버전인 NHPSO-JTVAC 알고리즘이 탐색 성능 면에서 다른 알고리즘보다 우수하다는 것을 알 수 있다. 뿐만 아니라 테스트 결과를 살펴보면 NHPSO-JTVAC에 기반한 하이브리드 모델이 조선소 세 개의 블록 공정 데이터에서 (각각 11.79%, 16.03% 및 16.45%) 가장 작은 MAPE 테스트 오차임을 알 수 있다. 이것은 NHPSO-JTVAC를 기반으로 구축된 모델이 예측 정확도 측면에서 의미 있는 향상을 더 달성할 수 있음을 보여준다. 전반적으로 NHPSO-JTVAC-SVM, NHPSO-JTVAC-ANN 모델은 조선소 블록 공정의 리드 타임을 예측하는 데 적합하다는 것을 확인할 수 있다.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Related Works 3 1.2.1 Related Works for Lead Time Prediction 3 1.2.2 Related Works for Hybrid Predictive Model 4 1.3 Thesis Organization 6 Chapter 2 Machine Learning 7 2.1 Support Vector Machine 7 2.1.1 Support Vector Machine Algorithm 7 2.1.2 Hyperparameter Optimization for SVM 10 2.2 Artificial Neural Network 11 2.2.1 Artificial Neural Network Algorithm 11 2.2.2 Hyperparameter Optimization for ANN 15 Chapter 3 Meta-heuristic Optimization Algorithms 17 3.1 Particle Swarm Optimization 17 3.2 NHPSO-JTVAC: An Advanced Version of PSO 18 3.3 Bat Algorithm 19 3.4 Firefly Algorithm 21 3.5 Grasshopper Optimization Algorithm 22 3.6 Moth Search Algorithm 24 Chapter 4 Hybrid Artificial Intelligence Models 27 4.1 Hybrid Meta-heuristic-SVM Models 27 4.1.1 Hybrid PSO-SVM Model 29 4.1.2 Hybrid NHPSO-JTVAC-SVM Model 30 4.1.3 Hybrid BA-SVM Model 31 4.1.4 Hybrid FA-SVM Model 33 4.1.5 Hybrid GOA-SVM Model 34 4.1.6 Hybrid MSA-SVM Model 35 4.2 Hybrid Meta-heuristic-ANN Models 36 4.2.1 Hybrid PSO-ANN Model 38 4.2.2 Hybrid NHPSO-JTVAC-ANN Model 39 4.2.3 Hybrid BA-ANN Model 40 4.2.4 Hybrid FA-ANN Model 41 4.2.5 Hybrid GOA-ANN Model 42 4.2.6 Hybrid MSA-ANN Model 43 Chapter 5 Lead Time Prediction Based on Hybrid AI Models 44 5.1 Data and Preparation 44 5.1.1 Data Normalization 45 5.1.2 Feature Selection 45 5.2 Lead Time Prediction 46 5.3 Performance Metrics 47 Chapter 6 Experimental Results 49 6.1 Results Based on Hybrid SVM-based Models 49 6.2 Results Based on Hybrid ANN-based Models 55 6.3 Overall Results 60 Chapter 7 Conclusions and Future Works 62 Bibliography 63 Appendix A 68 Abstract in Korean 69석

MIG gas shielding : Economic savings without detriment to quality

Over the years a number of claims have been made related to potential savings of the shielding gas used in the MIG process. A number of work streams have been set up to consider such areas from a technical and economic standpoint. The use of small helium additions has particular benefits and despite an increase in unit cost, the overriding benefits are achieved in reduced manhour cost. A similar situation has been established when using a high frequency process to switch shielding gases during welding. The outcome from this was very similar to that already described. Overlaid on these has been the increasing use of a technique that visualises actual gas flow during welding by the use of laser backlighting. Some preliminary work in this area is described particularly related to the effect of drafts on the gas distribution. A recent development on the market place is a piece of equipment, which regulates the gas flow automatically and synchronously with the welding current. Gas savings in the region of 50-60% have been obtained. Data has been produced to illustrate these benefits. The potential benefit of developing a computational fluid dynamic model of the gas flow is also described, and early development stages of the model shown. However, there will always exist the very basic management need to minimise leaks from the gas delivery systems

MULTIPURPOSE VESSEL FLEET FOR SHORT BLACK SEA SHIPPING THROUGH MULTIMODAL TRANSPORT CORRIDORS

A study about the requirements and cargo transportation demand in the Black Sea as part of a multimodal transportation frame is performed, estimating the potential need of a ship fleet of multipurpose ships. The study performs conceptual multipurpose vessel design and fleet sizing using the long-time experience and statistics in defining main dimensions of the ship and her hull form, resistance and propulsion, weights, stability, free-board, seakeeping and manoeuvrability, capital, operational and decommissioning expenditure, where the optimal design solution is obtained based on the energy efficiency, shipbuilding, operation, and resale costs at the end of the service life. A discussion about possible applications of a different fleet of ship sizes in improving the cargo transportation efficiency considers the vessel\u27s typical operational profile in such a way to maximise the economic impact conditional of the unsteady cargo flow and environmental impact

Energy efficiency parametric design tool in the framework of holistic ship design optimization

Recent International Maritime Organization (IMO) decisions with respect to measures to reduce the emissions from maritime greenhouse gases (GHGs) suggest that the collaboration of all major stakeholders of shipbuilding and ship operations is required to address this complex techno-economical and highly political problem efficiently. This calls eventually for the development of proper design, operational knowledge, and assessment tools for the energy-efficient design and operation of ships, as suggested by the Second IMO GHG Study (2009). This type of coordination of the efforts of many maritime stakeholders, with often conflicting professional interests but ultimately commonly aiming at optimal ship design and operation solutions, has been addressed within a methodology developed in the EU-funded Logistics-Based (LOGBASED) Design Project (2004–2007). Based on the knowledge base developed within this project, a new parametric design software tool (PDT) has been developed by the National Technical University of Athens, Ship Design Laboratory (NTUA-SDL), for implementing an energy efficiency design and management procedure. The PDT is an integral part of an earlier developed holistic ship design optimization approach by NTUA-SDL that addresses the multi-objective ship design optimization problem. It provides Pareto-optimum solutions and a complete mapping of the design space in a comprehensive way for the final assessment and decision by all the involved stakeholders. The application of the tool to the design of a large oil tanker and alternatively to container ships is elaborated in the presented paper