scholarly journals Safe-economical route model of a ship to avoid tropical cyclones using dynamic forecast environment

2014 ◽  
Vol 2 (8) ◽  
pp. 4907-4945
Author(s):  
L. Wu ◽  
Y. Wen ◽  
D. Wu ◽  
J. Zhang ◽  
C. Xiao
Keyword(s):  
Tropical Cyclones ◽  
Numerical Models ◽  
Economic Cost ◽  
Assessment Model ◽  
Proposed Model ◽  
Study Results ◽  
Capsizing Probability ◽  
Dynamic Forecasting ◽  
Forecasting Errors

Abstract. In heavy sea conditions related to tropical cyclones (TCs), losses to shipping caused by capsizing are greater than other kinds of accidents. Therefore, it is important to consider capsizing risk in the algorithms used to generate safe-economic routes that avoid tropical cyclones (RATC). A safe-economic routing and assessment model for RATC, based on a dynamic forecasting environment, is presented in this paper. In the proposed model, a ship's risk is quantified using its capsizing probability caused by heavy wave conditions. Forecasting errors in the numerical models are considered in the ship risk assessment according to their distribution characteristics. A case study shows that: the economic cost of RATCs is associated not only to the ship's speed, but also to the acceptable capsizing probability which is related with the ship's characteristic and the cargo loading condition. Case study results demonstrate that the optimal routes obtained from the model proposed in this paper are superior to those produced by traditional methods.

scholarly journals Safe-economical route and its assessment model of a ship to avoid tropical cyclones using dynamic forecast environment

2013 ◽  
Vol 1 (3) ◽  
pp. 1857-1893
Author(s):  
L. C. Wu ◽  
Y. Q. Wen ◽  
D. Y. Wu
Keyword(s):  
Tropical Cyclones ◽  
Numerical Models ◽  
Economic Cost ◽  
Assessment Model ◽  
Risk Level ◽  
Proposed Model ◽  
Study Results ◽  
Capsizing Probability ◽  
Forecasting Errors

Abstract. In heavy sea conditions related to tropical cyclones (TCs), losses to shipping caused by capsizing are greater than other kinds of accidents. Therefore, it is important to consider capsizing risk in the algorithms used to generate safe-economic routes that avoid tropical cyclones (RATC). A safe-economic routing and assessment model for RATC, based on a dynamic forecasting environment, is presented in this paper. In the proposed model, a ship's risk is quantified using its capsizing probability caused by heavy wave conditions. Forecasting errors in the numerical models are considered according to their distribution characteristics. A case study shows that: the economic cost of RATCs is associated not only to the ship's speed and the acceptable risk level, but also to the ship's wind and wave resistance. Case study results demonstrate that the optimal routes obtained from the model proposed in this paper are significantly superior to those produced by traditional methods.

scholarly journals Optimizing the Environmental and Economic Sustainability of Remote Community Infrastructure

2020 ◽  
Vol 12 (6) ◽  
pp. 2208 ◽  
Author(s):  
Jamie E. Filer ◽  
Justin D. Delorit ◽  
Andrew J. Hoisington ◽  
Steven J. Schuldt
Keyword(s):  
Environmental Impacts ◽  
Sustainability Assessment ◽  
Model Performance ◽  
Assessment Model ◽  
Remote Communities ◽  
Remote Community ◽  
Rural Villages ◽  
Sustainable Technologies ◽  
Study Results

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, the alternatives often come at a higher procurement cost and mobilization requirement. To assist planners with this challenging task, this paper presents the development of a novel infrastructure sustainability assessment model capable of generating optimal tradeoffs between minimizing environmental impacts and minimizing life-cycle costs over the community’s anticipated lifespan. Model performance was evaluated using a case study of a hypothetical 500-person remote military base with 864 feasible infrastructure portfolios and 48 procedural portfolios. The case study results demonstrated the model’s novel capability to assist planners in identifying optimal combinations of infrastructure alternatives that minimize negative sustainability impacts, leading to remote communities that are more self-sufficient with reduced emissions and costs.

Assessment model of lean effect (AMLE)

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samah Elrhanimi ◽  
Laila EL Abbadi
Keyword(s):  
Lean Manufacturing ◽  
Scoring Rules ◽  
Assessment Model ◽  
Global Reporting Initiative ◽  
Performance Measurements ◽  
Content Type ◽  
Iso 26000 ◽  
Study Results ◽  
Global Performance

PurposeThe purpose of this paper is to present the “Assessment Model of Lean Effect” (AMLE), a theoretical model that measures Lean manufacturing implementation effect over the global performance of a company.Design/methodology/approachAMLE model is divided in two criteria types: the “Facilitators criteria” and the “Results criteria”. “Results criteria” are inspired from the European Foundation for Quality Management (EFQM), Global Reporting Initiative (GRI) and ISO 26000. The “Facilitators criteria” are based on the main philosophy of the Lean manufacturing, which is the total elimination of all types of waste. The development of the scoring scale was based on the results, approach, deployment, assessment and review (RADAR) philosophy and the experience of nine consultants from the automotive field; the choice of the consultants was based on three conditions. Furthermore, each consultant has his\her own weight according to its expertise. Lastly, the AMLE was validated via a case study set in an automotive industry company called FEBA. The validation process is divided in two different steps: the first step is related to Facilitators assessment and scoring; via the evaluation of the different projects implemented by FEBA to eliminate the different types of waste. The second step concerns Results assessment and scoring, via the evaluation of the performance measurements used by FEBA to assess the effect of the Facilitators' implementation.FindingsThe developed model (AMLE) enabled the Lean manufacturing effect assessment on the global performance of a firm from the automotive field. The case study results reveal that the aforementioned firm does not give priority to social measurements. Consequently, the performance of the firm was negatively impacted.Research limitations/implicationsThe criteria of AMLE are inspired from the definition of the Lean manufacturing given by Taiichi Ohno, from ISO 26000 and from GRI; meaning that these criteria could be adjusted if other references existed or developed. In addition, the scoring rules are established according to the experience of a limited number of consultants from the automotive field. The scoring rules establishment would lead to meaningful outcomes, if the number of participants was increased. During the assessment of the global performance, the perception of the auditor plays an important role in terms of scoring because the scoring rules allow the possibility to the auditor to give from the minimum to the maximum of the score, according to his perception and experience. For the case study, the validation of the developed model requires starting with the “Facilitators” implementation process and then measure the generated global performance. However, due to time constraints and limited opportunities for new projects, the validation was based only on existing projects managed by the firm. To address the study limitations, it is envisaged to detail and explain the scoring rules while extending the number of consultants. Furthermore, the assessment of Lean manufacturing global performance through the AMLE model may be subjective and requires a mathematical modeling. In fact, the Lean manufacturing performance assessment via the developed model could have a degree of subjectivity; that is why the design of a mathematical model seems required.Practical implicationsThe research findings may direct practitioners and decision makers to the importance of assessing the global effect of the Lean manufacturing on the overall performance of the firm. The AMLE model is a tool allowing the assessment of Lean manufacturing effect over economic, environmental and social performances.Originality/valueThe developed model is the first one assessing the global performance generated by the elimination of waste via the application of the Lean manufacturing.

A fuzzy-logic model for benchmarking concrete roof maintenance

Facilities ◽  
2021 ◽  
Vol 39 (7/8) ◽  
pp. 568-583
Author(s):  
Sanduni Peiris ◽  
Nayanthara De Silva
Keyword(s):  
Fuzzy Logic ◽  
Design Methodology ◽  
Concrete Structures ◽  
Logic Model ◽  
Maintenance Strategies ◽  
Content Type ◽  
Proposed Model ◽  
Study Results ◽  
Decision Making Tool

Purpose Concrete structures undergo early and fast deterioration, which causes defects such as cracks, water leaks and delamination, resulting from a lack of or inefficient maintenance practices. To improve this behaviour, this paper aims to develop a maintenance strategy benchmarking model for concrete structures. Design/methodology/approach Fuzzy logic toolbox on MATLAB R2018a was used to develop the proposed model and it was applied to two cases. A comprehensive literature search was done to review common concrete defects, their impact on the performance and functionality of the structure, effectiveness of maintenance strategies and previous maintenance benchmarking models. The literature findings were further validated through expert interviews which have been incorporated in the model. Findings Case study results show that preventive maintenance (PM), predictive maintenance (PdM) and corrective maintenance (CM) strategies are required more or less in similar combinations for maintenance of concrete roof structures. The best combination for case 1 is 36.42% PM, 35.40% PdM and 28.18% CM, and for case 2 is 35.93% PM, 35.08% PdM and 28.99% CM. According to suitability, they can be ranked as PM > PdM > CM. Originality/value This model will contribute as a comprehensive decision-making tool for building/facility managers. The findings further carry a strong message to those who practice only CM in their buildings.

Algorithms for Supervised Machine Learning-Based Structural Performance Evaluation Framework

2020 ◽  
Author(s):  
Xiaowei Wang ◽  
YeongAe Heo
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Numerical Models ◽  
Evaluation Framework ◽  
Supervised Machine Learning ◽  
System Level ◽  
Structural Performance ◽  
Fe Model ◽  
Study Results

Abstract Machine learning (ML) approaches have gained increasing attention in the structural engineering field to evaluate structural performance using structural health monitoring (SHM) data. Supervised ML approaches can accelerate the learning process by using labeled training datasets to map an input to output dataset. But, SHM data are not informative to drive a mapping function to determine the real-world performance of large-scale complex structures in particular for future events. To leverage a framework for evaluating the system-level structural performance, this study couples supervised ML approaches with an advanced finite element (FE) model considering pre- and post-event model validation and updating. A well-instrumented system experiencing multiple seismic events is employed as a case study to demonstrate the proposed framework. An FE model of the instrumented system is created and validated using pre-event SHM datasets. Numerical data obtained from the FE model are used for datasets to develop ML prediction models, which are then validated by a post-event SHM dataset. Eight popular ML algorithms are examined and compared to shed light on the effectiveness of the ML algorithms for the proposed framework. The case study results indicate that the Random Forests and Neural Network algorithms provide better estimation for the structural system. The results also imply the need of post-event updating for numerical models used in the case study.

Metadata-based data quality assessment

2016 ◽  
Vol 46 (2) ◽  
pp. 232-250 ◽  
Author(s):  
Mustafa Aljumaili ◽  
Ramin Karim ◽  
Phillip Tretten
Keyword(s):  
Content Analysis ◽  
Data Quality ◽  
Design Methodology ◽  
Decision Makers ◽  
Assessment Model ◽  
Content Type ◽  
Data Quality Assessment ◽  
Proposed Model ◽  
Metadata Analysis

Purpose The purpose of this paper is to develop data quality (DQ) assessment model based on content analysis and metadata analysis. Design/methodology/approach A literature review of DQ assessment models has been conducted. A study of DQ key performances (KPIs) has been done. Finally, the proposed model has been developed and applied in a case study. Findings The results of this study shows that the metadata data have important information about DQ in a database and can be used to assess DQ to provide decision support for decision makers. Originality/value There is a lot of DQ assessment in the literature; however, metadata are not considered in these models. The model developed in this study is based on metadata in addition to the content analysis, to find a quantitative DQ assessment.

scholarly journals A conceptual model for measuring the competency level of Small and Medium-sized Enterprises (SMEs)

2021 ◽  
Vol 16 (1) ◽  
pp. 47-66
Author(s):  
E. Oztemel ◽  
S. Ozel
Keyword(s):  
Real Life ◽  
Assessment Model ◽  
Assessment Methodology ◽  
Governmental Organizations ◽  
Proposed Model ◽  
Different Types ◽  
Technological Competency ◽  
Effective Assessment ◽  
Competency Level

Small and Medium-Sized Enterprises (SMEs) are of major importance to developing countries. SMEs are the main drivers to strengthen society in sustaining economic growth and development. Governments provide various support programs to improve their industrial power and to increase the number of enterprises in the market. The enterprises must be assessed and suitable funds should be provided to those in need, to achieve an effective support program in the most efficient way.This requires implementing an assessment methodology based on a predefined set of scientific criteria. The current literature is comprehensive enough to assess the healthiness of the enterprises concerning strategic, technologic, financial as well as intellectual competencies but on the other hand, it lacks of an assessment model. This study aims to introduce a general framework for sustaining an effective assessment methodology for SMEs to eliminate this gap. The proposed model measures five different types of competencies such as Technological Competency, Strategic Competency, Financial Competency, Intellectual Competency, R&D and Innovation Competency. These competencies are to put forth the conditions in which the enterprises are running accurately. A real-life case study is conducted to ensure the baseline of the model to be implemented. The governmental organizations may utilize the model for sustaining their support role effectively to SMEs.

A deep learning-based quality assessment model of collaboratively edited documents: A case study of Wikipedia

2019 ◽  
pp. 016555151987764
Author(s):  
Ping Wang ◽  
Xiaodan Li ◽  
Renli Wu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Complete Information ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Learning Models ◽  
Proposed Model

Wikipedia is becoming increasingly critical in helping people obtain information and knowledge. Its leading advantage is that users can not only access information but also modify it. However, this presents a challenging issue: how can we measure the quality of a Wikipedia article? The existing approaches assess Wikipedia quality by statistical models or traditional machine learning algorithms. However, their performance is not satisfactory. Moreover, most existing models fail to extract complete information from articles, which degrades the model’s performance. In this article, we first survey related works and summarise a comprehensive feature framework. Then, state-of-the-art deep learning models are introduced and applied to assess Wikipedia quality. Finally, a comparison among deep learning models and traditional machine learning models is conducted to validate the effectiveness of the proposed model. The models are compared extensively in terms of their training and classification performance. Moreover, the importance of each feature and the importance of different feature sets are analysed separately.

Research on Scheme Evaluation of the Automobile Fault Diagnosis System

2012 ◽  
Vol 159 ◽  
pp. 235-239
Author(s):  
Min Yong Tong
Keyword(s):  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Diagnosis System ◽  
Proposed Model ◽  
Study Results ◽  
Comprehensive Evaluation Model ◽  
Fault Diagnosis System ◽  
Scheme Evaluation

Based on the characteristics of the automobile diagnosis system scheme, this paper has established the parameter system of diagnosis system scheme evaluation. Aiming at automobile diagnosis system scheme is difficult to evaluate its condition because of its structure is complex and its monitoring parameters are complicated and various, a second order fuzzy comprehensive evaluation model is proposed. The case study results show that the proposed model is feasible and effective. Hence, the evaluated results can aid to automobile diagnosis system design.

Development of a three-tier assessment model: a case study

2018 ◽  
Vol 25 (7) ◽  
pp. 2216-2229 ◽  
Author(s):  
Henry C. Lau ◽  
Andrew Ip ◽  
CKM Lee ◽  
GTS Ho
Keyword(s):  
Operations Management ◽  
Management Practices ◽  
Assessment Model ◽  
Application Process ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Proposed Model ◽  
Order Preference ◽  
Industrial Settings

PurposeThe purpose of this paper is to propose a three-tier assessment model (TAM), aiming to identify and evaluate the competitiveness level of companies. The existing problem is that companies find it difficult to choose a proper model which can be deployed to benchmark with competitors in terms of their competiveness level in the marketplace. Most of the available models are not appropriate or easy to use. The proposed assessment model is able to provide an insight for better planning and preparation so as to gain a better chance of success comparing with their competitors. Most importantly, the proposal model adopts a pragmatic approach and can be implemented without going through tedious mathematical calculations and analysis.Design/methodology/approachTAM embraces three different approaches deployed in various stages of the application process. The first stage is to identify the relevant criteria using hierarchical holographic modeling and the second stage is to assess the associated weightings of these criteria used to rate the potential competitiveness of related companies. The technique used in stage two is known as fuzzy analytic hierarchy process (FAHP) which is a combination of two well-established methods including fuzzy logic and analytical hierarchical programming. In stage three, a technique known as technique for order preference by similarity to the ideal solution (TOPSIS) is adopted to benchmark the level of competitiveness covering several companies in the same industry.FindingsIn this paper, a case study is conducted in order to validate the feasibility and practicality of the proposed model. Results indicate that TAM can be easily applied in various industrial settings by practitioners in the field for supporting operations management practices.Research limitations/implicationsSignificant amount of work is needed to ensure that the proposed model can be practically deployed in real industrial settings.Practical implicationsThis proposed model is able to capitalize on the benefits of the HMM, FAHP and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification.Originality/valueThis paper suggests a hybrid model to assess competitiveness level embracing three different techniques with the unique feature which is able to provide an insight for better planning and preparation in order to excel competitors. Companies may be able to follow the procedures and steps suggested in the paper to implement the model which is proven to be pragmatic and can be applied in real situations.

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