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Tainan, Taiwan

Kuo Y.,Hsing Kuo University
Computers and Industrial Engineering | Year: 2010

The vehicle routing problem (VRP) has been addressed in many research papers. Only a few of them take time-dependent travel speeds into consideration. Moreover, most research related to the VRP aims to minimize total travel time or travel distance. In recent years, reducing carbon emissions has become an important issue. Therefore, fuel consumption is also an important index in the VRP. In this research a model is proposed for calculating total fuel consumption for the time-dependent vehicle routing problem (TDVRP) where speed and travel times are assumed to depend on the time of travel when planning vehicle routing. In the model, the fuel consumption not only takes loading weight into consideration but also satisfies the "non-passing" property, which is ignored in most TDVRP-related research papers. Then a simulated annealing (SA) algorithm is proposed for finding the vehicle routing with the lowest total fuel consumption. An experimental evaluation of the proposed method is performed. The results show that the proposed method provides a 24.61% improvement in fuel consumption over the method based on minimizing transportation time and a 22.69% improvement over the method based on minimizing transportation distances. © 2010 Elsevier Ltd. All rights reserved. Source


Chiu K.-C.,Hsing Kuo University
2011 IEEE International Conference on Quality and Reliability, ICQR 2011 | Year: 2011

Over the last two decades, various software reliability growth models (SRGM) have been proposed, and there has been a gradual but marked shift in the balance between software reliability and software testing cost in recent years. Chiu and Huang (2008) provided a Software Reliability Growth Model from the Perspective of Learning Effects, which is able to reasonably describe the S-shaped and exponential-shaped types of behaviors simultaneously, and offers better performance when fitting different data with consideration of the learning effects. However, this earlier model assumes that the learning effects are constant. In contrast, this paper discusses a software reliability growth model with time-dependent learning effects. © 2011 IEEE. Source


Kuo Y.,Hsing Kuo University | Wang C.-C.,Feng Chia University
Expert Systems with Applications | Year: 2012

The purpose of this paper is to propose a variable neighbourhood search (VNS) for solving the multi-depot vehicle routing problem with loading cost (MDVRPLC). The MDVRPLC is the combination of multi-depot vehicle routing problem (MDVRP) and vehicle routing problem with loading cost (VRPLC) which are both variations of the vehicle routing problem (VRP) and occur only rarely in the literature. In fact, an extensive literature search failed to find any literature related specifically to the MDVRPLC. The proposed VNS comprises three phases. First, a stochastic method is used for initial solution generation. Second, four operators are randomly selected to search neighbourhood solutions. Third, a criterion similar to simulated annealing (SA) is used for neighbourhood solution acceptance. The proposed VNS has been test on 23 MDVRP benchmark problems. The experimental results show that the proposed method provides an average 23.77% improvement in total transportation cost over the best known results based on minimizing transportation distance. The results show that the proposed method is efficient and effective in solving problems. © 2011 Elsevier Ltd. All rights reserved. Source


Wu T.-T.,Chia Nan University of Pharmacy and Science | Sung T.-W.,Hsing Kuo University
CIN - Computers Informatics Nursing | Year: 2014

In recent years, mobile device-assisted clinical education has become popular among nursing school students. The introduction of mobile devices saves manpower and reduces errors while enhancing nursing students' professional knowledge and skills. To respond to the demands of various learning strategies and to maintain existing systems of education, the concept of Cloud Learning is gradually being introduced to instructional environments. Cloud computing facilitates learning that is personalized, diverse, and virtual. This study involved assessing the advantages of mobile devices and Cloud Learning in a public health practice course, in which Google+ was used as the learning platform, integrating various application tools. Users could save and access data by using any wireless Internet device. The platform was student centered and based on resource sharing and collaborative learning. With the assistance of highly flexible and convenient technology, certain obstacles in traditional practice training can be resolved. Our findings showed that the students who adopted Google+ were learned more effectively compared with those who were limited to traditional learning systems. Most students and the nurse educator expressed a positive attitude toward and were satisfied with the innovative learning method. Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. Source


Huang Y.-M.,National Cheng Kung University | Huang Y.-M.,Chia Nan University of Pharmacy and Science | Liu C.-H.,Hsing Kuo University | Tsai C.-C.,National Taiwan University of Science and Technology
Interactive Learning Environments | Year: 2013

Web-based self-learning (WBSL) has received a lot of attention in recent years due to the vast amount of varied materials available in the Web 2.0 environment. However, this large amount of material also has resulted in a serious problem of cognitive overload that degrades the efficacy of learning. In this study, an information graphics method is proposed to resolve this problem. This method is based on social tagging, which is used to visualize the relationships among materials and can thus assist learners in facilitating learning. To examine the feasibility of the proposed method for managing cognitive load, an experimental model was designed in which cognitive load theory was adopted as the theoretical framework. A total of 60 university students participated in the experiment, and the partial least squares method was used to verify the experimental model. The results show that the information graphics method has a positive impact on three types of cognitive load, namely intrinsic, extraneous, and germane. Furthermore, intrinsic and germane cognitive load have a positive influence on perceived learning effectiveness, while extraneous cognitive load does not have a significant influence. One possible reason for this outcome is that the problem of visual load was not considered in the design of this study. The overall summary of the findings is that the use of social tagging can effectively manage cognitive load and positively links to perceived learning effectiveness. © 2013 Copyright Taylor and Francis Group, LLC. Source

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