Chi Yo Huang

Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems

The concept of globalization has been prosperous in the past decades while manufacturing as well as logistics have already become one of the most significant issues in the globalization era. However, while modern globalized firms are leveraging both global manufacturing resources as well as logistics systems for pursuing higher quality, lower cost as well as product differentiation, how to evaluate, selecting an appropriate global manufacturing strategy by considering issues from both aspects of global manufacturing as well as logistics has become one of the most critical and difficult issues. Moreover, how the chosen intertwined global manufacturing as well as logistics system is to be optimized so that the aspired level of the global manufacturing system can be achieved have few been addressed. Thus, this research aims to resolve the above mentioned global manufacturing and logistics strategy selection as well as system reconfiguration issue. A Decision Making Trial and Evaluation Laboratory (DEMATEL) technique based novel multiple criteria decision making (MCDM) method with Analytic Network Process (ANP), Grey Relational Analysis (GRA) as well as VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) will be proposed for selecting and re-configuring the aspired global manufacturing and logistics system. An empirical study based on the global manufacturing and logistics system design of a semiconductor company will be provided for verifying the effectiveness of this proposed methodology.

A novel method for fuzzy measure identification

Fuzzy measure and Choquet integral are effective tools for handling complex multiple criteria decision making (MCDM) problems in which criteria are inter-dependent. The identification of a fuzzy measure requires the determination of 2(superscript n)-2 values when the number of criteria is n. The complexity of this problem increases exponentially, which makes it practically very difficult to solve. Many methods have been proposed to reduce the number of values to be determined including the introduction of new special fuzzy measures like the λ-fuzzy measures. However, manipulations of the proposed methods are difficult from the aspects of high data complexity as well as low computation efficiency. Thus, this paper proposed a novel fuzzy measure identification method by reducing the data complexity to n(n−1)/2 and enhancing the computation efficiency by leveraging a relatively small number of variables and constraints for linear programming. The proposed method was developed based on the evaluation of pair-wise additivity degrees or interdependence coefficients between the criteria. Depending on the information being provided by decision-makers on the individual density of each criterion, the fuzzy measure can be constructed by solving a simple system of linear inequalities or a linear programming problem. This novel method is validated through a supplier selection problem which occurs frequently in real-world decision-making problems. Validation results demonstrate that the newly-proposed method can model real-world MCDM problems successfully.

Short Term Wind Speed Predictions by Using the Grey Prediction Model Based Forecast Method

The wind has emerged as one of the fastest growing and most important alternative energy sources during the past decade. However, the most serious problem being faced by human beings in wind applications is the dependence on the volatility of the wind. Thus, wind forecasting techniques in general and short term wind prediction methods in special are very helpful for wind applications. Albeit various methods including the artificial neural networks, fuzzy logic, evolutionary computation approaches have been developed for short term predictions, very few scholars tried to introduce the Grey forecasting method, which was designed for the system analysis being characterized by inadequate information. Since the short term wind prediction problem is characterized by the nature of no regularity, difficult prediction, and huge influences by terrain and obstacle. It can be a suitable problem to be resolved by the Grey forecasting method. Thus, the authors developed a GM (1,1) Grey system based pilot study for future short term wind power forecasting. The accurate predictions of wind speed by using the GM(1,1) Grey forecasting method can serve as the basis for introducing the wind power as an efficient source of alternative energies. An empirical study based on the real data being measured from a wind farm being located in Penghu, Taiwan has demonstrated the efficiency of the GM(1,1) based forecast mechanism. In the future, the GM(1,1) based forecast mechanism can further be applied to the wind forecast problems based on various time spans.

Capacity allocation model for photolithography workstation with the constraints of process window and machine dedication

As semiconductor process technology progresses, a more rigid process requirement emerges. Process window, also known as process capability, means that a wafer needs to be processed on the machines that can satisfy its process specification. Machine dedication, means that after the first critical layer of a wafer lot is processed on a certain machine, the subsequent critical layers of this lot must be processed on the same machine, to ensure final products of a good quality. The interaction of the two characteristics makes the production plan, based on the overall capacity of workstations, very hard to implement in the real production environment. This paper, therefore, proposes an integer programming model to solve the capacity allocation problem. With the objective of load levelling, the proposed model will assign the load of each layer, of each order, to each machine, with considerations of process window and machine dedication constraints. The model can be used in practice, and the loading allocation results can be a reference for controlling load levelling and setting the release time, in a fab.

Evaluating and Enhancing Three-Dimensional Printing Service Providers for Rapid Prototyping Using the DEMATEL Based Network Process and VIKOR

During the past years, the three-dimensional printing (3DP) has become a dominant rapid prototyping (RP) technology due to its very viable process in terms of cost, speed, and sales of related equipment. Nowadays, numerous 3DP based RP services are available. Because of the capability, service quality, and pricing of the services varies, how to select a suitable 3DP based RP service provider is very critical to the companies being engaged in new product developments. However, the issue was seldom studied. To resolve this problem, a hybrid multiple-criteria decision making (HMCDM) framework for evaluating and enhancing an appropriate 3DP based RP service provider based on the Decision Making Trial and Evaluation Laboratory (DEMATEL) based Network Process (DNP) as well as VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) was proposed. The analytic framework was verified as feasible by an empirical study based on the opinions being provided by 3DP and RP experts. The well-verified framework can serve as the basis of future evaluation, selection, and enhancement of 3DP based RP service providers.

UTAUT2 Based Predictions of Factors Influencing the Technology Acceptance of Phablets by DNP

The smart mobile devices have emerged during the past decade and have become one of the most dominant consumer electronic products. Therefore, exploring and understanding the factors which can influence the acceptance of novel mobile technology have become the essential task for the vendors and distributors of mobile devices. The Phablets, integrated smart devices combining the functionality and characteristics of both tablet PCs and smart phones, have gradually become possible alternatives for smart phones. Therefore, predicting factors which can influence the acceptance of Phablets have become indispensable for designing, manufacturing, and marketing of such mobile devices. However, such predictions are not easy. Meanwhile, very few researches tried to study related issues. Consequently, the authors aim to explore and predict the intentions to use and use behaviors of Phablets. The second generation of the Unified Theory of Acceptance and Use of Technology (UTAUT2) is introduced as a theoretic basis. The Decision Making Trial and Evaluation Laboratory (DEMATEL) based Network Process (DNP) will be used to construct the analytic framework. In light of the analytic results, the causal relationships being derived by the DEMATEL demonstrate the direct influence of the habit on other dimensions. Also, based on the influence weights being derived, the use intention, hedonic motivation, and performance expectancy are the most important dimensions. The analytic results can serve as a basis for concept developments, marketing strategy definitions, and new product designs of the future Phablets. The proposed analytic framework can also be used for predicting and analyzing consumers’ preferences toward future mobile devices.

Determinants and their causal relationships affecting the adoption of cloud computing in science and technology institutions

Cloud computing is a technology and service innovation that has attracted tremendous interest across almost all areas of computational use. With low construction costs and high system scalability, cloud computing provides an alternative to the traditional high-performance computing employed for decades in science and technology (S&T) institutions. Despite the tremendous benefits of cloud computing, achieving its successful diffusion is no easy task. The goal of this research was to construct a decision framework by identifying a series of key consideration factors and their causal relationships that affect the adoption of cloud computing in S&T institutions. To reach the research goal, we conducted an empirical study by surveying a group of people who are implementers and/or users of cloud computing in S&T institutions. Based on the classic innovation diffusion theories, this research extends the theoretical understanding of the determinants and their cause-and-effect relationships that influence the diffusion of technological innovation in the context of S&T. Practically, this research offers practitioners a strategic path to the successful adoption of cloud computing.

Evaluating Cloud Computing Based Telecommunications Service Quality Enhancement by Using a New Hybrid MCDM Model

The fast emergence of the cloud based computation fulfills modern telecommunication serve firms’ requirements for massive computation and storage space with very low costs. However, how the cloud-based service quality can be enhanced, how customers’ needs can be fulfilled, and how firms’ profits can be maximized were seldom discussed. Thus, this research aims to provide a new hybrid MCDM model combining DEMATEL and ANP based framework and Grey Relational Analysis (GRA) for improving to close the service quality gap, fulfill customers’ satisfactory and maximize profits in interdependence and feedback problems among dimensions/criteria. An empirical case study bases on enhancing the service quality of a Taiwanese mobile telecommunication firm’s application of cloud computation as the billing system. In results of this case study, the expertise in dynamic capacity management and new education and training should be the most important strategies for service quality enhancement to be introduced.

Predicting of the Short Term Wind Speed by Using a Real Valued Genetic Algorithm Based Least Squared Support Vector Machine

The possible future energy shortage has become a very serious problem in the world. An alternative energy which can replace the limited reservation of fossil fuels will be very helpful. The wind has emerged as one of the fastest growing and most important alternative energy sources during the past decade. However, the most serious problem being faced by human beings in wind applications is the dependence on the volatility of the wind. To apply the wind power efficiently, predictions of the wind speed are very important. Thus, this paper aims to precisely predict the short term regional wind speed by using a real valued genetic algorithm (RGA) based least squared support vector machine (LS-SVM). A dataset including the time, temperature, humidity, and the average regional wind speed being measured in a randomly selected date from a wind farm being located in Penghu, Taiwan was selected for verifying the forecast efficiency of the proposed RGA based LS-SVM. In this empirical study, prediction errors of the wind turbine speed are very limited. In the future, the proposed forecast mechanism can further be applied to the wind forecast problems based on various time spans.

Derivations of information technology strategies for enabling the cloud based banking service by a hybrid MADM framework

The cloud computing has emerged and become one of the most influential information technology (IT) development trends recently. The cloud computing can be used to enhance the operation efficiency and reduce the operating cost concurrently for firms in general and financial institutes in special. Albeit the cloud computing seems attractive to banking services from the aspects of cost minimization, service value maximization, and thus, competitive advantage achievement, barriers still exist for the introduction of cloud computing by banks. In order to enable the cloud based banking services, IT strategies should be introduced for overcoming the obstacles, including the security, system configuration, and other related issues. Based on the empirical study results by surveying an expert from one of the Taiwanese leading banks, the rapid deployment strategy, the multi-tenant technology, and huge data processing capabilities were regarded as the most important strategies.