Ming-Hung Shu

Fault-tree analysis for liquefied natural gas terminal emergency shutdown system

Natural gas, one of the cleanest, most efficient and useful of all energy sources, is a vital component of the worlds supply of energy. To make natural gas more convenient for storage and transportation, it is refined and condensed into a liquid called liquefied natural gas (LNG). In a LNG site, safety is a long-team and critical issue. The emergency shutdown (ESD) system in the LNG receiving terminal is used to automatically stop the pumps and isolate the leakage section. Fault-tree analysis (FTA) has been widely used for providing logical functional relationships among subsystems and components of a system and identifying the root causes of the undesired failures in a system. In the conventional FTA for the ESD system, we usually assume that exact failure probabilities of events are collected. However, in most real applications, first, the FTA for the ESD system needs to be made at a early design or manufacturing stage, certain new components normally used without failure data; secondly, sometimes the environmental change in the system during the operation periods. This makes more difficult to gather past exact failures data for the FTA. To complete the FTA of the ESD system under these uncertain situations, we apply the intuitionistic fuzzy sets (IFS) theory to the FTA. We generate the intuitionistic fuzzy fault-tree interval, and the intuitionistic fuzzy reliability interval for the ESD system. We also present an algorithm to find the critical components in the system based on IFS-FTA and determine weak paths in the ESD system, where the key improvement must be made.

Supplier selection using fuzzy quality data and their applications to touch screen

The purchasing function directly affects the competitive ability of a firm. Since the determination of suitable suppliers from a set of suppliers has become a key strategic consideration, managers need to periodically evaluate suppliers on the basis of their products quality to select suppliers whose quality characteristics of products meet the standards. The quantification of the process capability is effective to understand the quality of the units shipped from a supplier. While fuzzy data commonly exist in our real world, the quality-based supplier selection with fuzzy quality data is proposed in this paper. We apply the resolution identity result, a well-known method used in fuzzy sets theory, in terms of solving the nonlinear programming problems with bounded variables to construct the membership function of a fuzzy capability-index estimate for each supplier. The preferred suppliers are selected by using a ranking method of fuzzy preference relations of suppliers. Finally, a case study of touch screens is provided to describe the applicability that incorporates the fuzzy data into the problem of quality-based supplier selection and evaluation.

Fuzzy testing and selecting better processes performance

Purpose – The principal aim of this study was to provide more realistic output data based on imprecise measurements of product quality. The real‐world problems in fuzzy testing and selecting better processes performance are considered.Design/methodology/approach – The Taguchi index, which provides numerical measures on process performance, has been widely used in the industry. In practice, the Taghchi index is estimated by sample data, thus it is of interest to obtain the confidence limits of the estimate Cpm for assessing processes. In addition, it is much more realistic, because in general the output quality characteristics of continuous quantities are more or less imprecise. Using the approach taken by Buckley, with some extensions, a general method is used to combine the vector of fuzzy numbers to produce the membership function of a fuzzy estimator of Cpm for further fuzzy testing and selection of better process performances.Findings – As the rapid advancement of manufacturing technology occurs, curr...

Fuzzy MaxGWMA chart for identifying abnormal variations of on-line manufacturing processes with imprecise information

The exponentially-weighted-moving-average (EWMA) control chart was developed to detect small shifts in mean and variability of a key quality characteristic in a manufacturing process. To improve its performance, several modified statistical schemes on manipulation of random samples of real data (precise numbers) collected from the quality characteristic have been proposed. Among them, the recently recommended control chart named the maximum generally weighted moving average (MaxGWMA) is found superior in recognition of its outstanding diagnostic abilities at warning abnormal-manufacturing variations swiftly. In this paper, based on the well-known fuzzy set theory, we develop a Fuzzy-MaxGWMA (F-MaxGWMA) chart, an extension of the MaxGWMA chart, to well accommodate the fuzzy environment where both the randomness and fuzziness of imprecise sample data (fuzzy numbers) are taken into consideration. Moreover, for identifying assignable variations of the on-line manufacturing process with fuzzy data, an index-of-optimism criterion is implemented to instantaneously monitoring as well as classifying the process conditions into multi-intermittent states between in control and out of control. It can overcome the constraints of binary classifications of the process condition used by the MaxGWMA chart when fuzzy data inevitably appear in practical manufacturing processes. Finally, a realistic example to control the coating thickness of an industrial cutting-tool manufacturing process is illustrated to demonstrate the adaptability and effectiveness of this newly extended approach.

Evaluation of supply chain performance using delivery-time performance analysis chart approach

Abstract Variability reduction and business synchronization are acknowledged as keys to achieving timely deliveries in supply chain networks. The evaluation of delivery performance is a crucial component in the overall management and time control of a supply chain. This study uses lead time, delivery window and delivery performance chart (DPC) to measure the delivery performance of every stage in a serial supply chain. Lead time is an important delivery performance metric for organizations. DPC, which is integrated by the process capability indices (PCIs) and the concept of Motorola Six-Sigma, is proposed to provide several simultaneously visualized important features of delivery performance in a supply chain. We also consider the uncertainty of the estimated PCIs caused by sampling data and plot their lower confidence bounds (LCBs) in the DPC to measure the minimum delivery capability of each stage in the supply chain. Finally, a case study related to a supply chain of notebook industry is presented to demonstrate how the proposed approach can be employed to evaluate delivery performance for further reduction of lead time variability.

New quantitative approach based on index of optimism for fuzzy judgement of online manufacturing process

Abstract Shewhart type fuzzy charts have been immensely proposed to deal with several practical problems of fuzzy data in monitoring and examining manufacturing processes. In mediating the shortcomings of indistinguishable classification conditions of a recently proposed fuzzy and s charts, this paper aims at developing a quantitative approach based on the index of optimism for fuzzy judgement of online manufacturing processes. By quantifying the linguistic notion ‘rather’ that is usually mentioned as a qualitative term in previous researches, we make it numerically measured in this study; as such, our thorough evaluation conditions of a manufacturing process, including in control, rather in-control, rather out of control and out of control, are more sufficient and justified. In addition, our proposed approach with the multi-intermittent states certainly overcome the constraints of binary classifications of traditional Shewhart type control charts when fuzzy data inevitably appear in practical manufacturing processes; hence, it fulfils the current literature of control charts.

A comparative study of the monitoring performance for weighted control charts

Abstract Statistical process control (SPC) is the method that monitors process quality characteristics. Through control charts, one can detect whether the present process malfunction. However, some annoyances may arise while the engineers need to choose appropriately control chart under different levels of process variation. The Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been widely used for monitoring semiconductor manufacturing processes. Generally weighted moving average (GWMA) control chart is a new method of SPC, which was proposed by Sheu and Lin (2003). The main objective of this research is using step-by-step procedures to present a comparative study of the monitoring performance for Shewhart, CUSUM, EWMA and GWMA control charts. According to a specified in-control average run length (ARL), we determine the parameters of each control chart based on the Monte Carlo numerical simulation. The setting parameters in each control chart sre displayed and tabulated. While the process means or the process standard deviations are changing in different levels, the performance of each weighted control chart can be then compared by using the ARL. A rule of thumb for selecting better control schemes is provided as a truthfully reference to help engineers in choosing the more appropriate control charts immediately when the assignable causes occurred.

Optimising steelmaking variables of sulphur free-cutting steels for process improvement and cost reduction

Abstract The key point to sulphur free-cutting steels production is the managing of critical variables among the process inputs and outputs. This research focuses on the adoption of the six-sigma methodology to acquire the optimal combination parameters of sulphur free-cutting steel processes, thus achieving a stable production and reducing costs. This success has achieved the average decline in consumption of ferromanganese up to 12% along with annual savings of more than NT

Accurate forecasting models in predicting the inbound tourism demand in Vietnam

15 million in production costs for the China Steel Corporation in Taiwan.

Fuzzy and Control Charts: A Data-Adaptability and Human-Acceptance Approach

Abstract Tourism has been considered a complexly integrated and self-contained economic activity; however, it is one of the biggest industries in many countries. In order to make the tourism industry grow stably, it has been always an important issue to predict the tourism demand accurately. Though there have been many different studies in the methodologies, methods and models to forecast the tourism demand, there is no standard forecasting model that can be applied in different situations of the industry. In this study, two conventional models named ARIMA and Grey forecasting GM(1,1) were investigated. Their residuals were modified with Fourier series in order to improve the model accuracy level. In the empirical study of inbound tourism demand in Vietnam, the Fourier modified models called FARIMA(2,1,1) (1,0,2)12 and FGM(1,1) have very low values of mean absolute percentage error (MAPE) of 0.0055 and 0.0105, respectively. Both of them are excellent to forecast the inbound tourism demand in Vietnam but FARIMA(2,1,1)(1,0,2)12 is better and it is therefore suggested.