An Improvement the Accuracy of Grey Forecasting Model for Cargo Throughput in International Commercial Ports of Kaohsiung

Abstract:-Based on the grey theory, grey prediction models, which are GM (1, 1), have been adopted to predict the cargo throughput and modified GM (1, 1) using Fourier series called "FRMGM (1, 1)" for improving the accuracy of forecast model. A forecasting the cargo throughput in the international commercial port of Kaohsiung from 2013-2015 has also been conducted based on the previous data to serve as a reference for port manager in making development plans and construction as well as orienting development in the future. All data source is collected from the Ministry of Transportation and Communication of Taiwan. Through simulation results, this study showed that both of two models are suitable but the FRMGM (1, 1) is the excellent model in forecast with average accuracy of predict is 100%. Hence, the FRMGM (1, 1) model is strongly suggested for forecast the cargo throughput in the port of Kaohsiung

A Hybrid Approach for Measuring Productivity in the Global Fitness Industry by Using Grey and DEA Model

Maintaining sustainable development is becoming an important issue for the fitness industry. To solve this problem, the decision-makers need to understand the performance of this industry. This research proposes a hybrid approach based on grey model (GM) and Malmquist productivity index (MPI), to measure operational performance of worldwide fitness manufactures over several periods. From that, decision making units (DMUs) and managers can improve business performance and build a sustainable development strategy. This research conducted on 15 fitness manufactures, by the use of several input and output variables. GM was used to predict the future value of these variables. Following the MPI was used to evaluate performance of all DMUs. The MPI results showed some manufactures become more efficient, while others become less efficient. The results provide past-future insights for decision-maker to sustain fitness requirement manufacturing. The study will be a useful reference for other industries as well

Microfluidic Mixing: A Review

The aim of microfluidic mixing is to achieve a thorough and rapid mixing of multiple samples in microscale devices. In such devices, sample mixing is essentially achieved by enhancing the diffusion effect between the different species flows. Broadly speaking, microfluidic mixing schemes can be categorized as either “active”, where an external energy force is applied to perturb the sample species, or “passive”, where the contact area and contact time of the species samples are increased through specially-designed microchannel configurations. Many mixers have been proposed to facilitate this task over the past 10 years. Accordingly, this paper commences by providing a high level overview of the field of microfluidic mixing devices before describing some of the more significant proposals for active and passive mixers

A Study of the Strategic Alliance for EMS Industry: The Application of a Hybrid DEA and GM (1, 1) Approach

Choosing a partner is a critical factor for success in international strategic alliances, although criteria for partner selection vary between developed and transitional markets. This study aims to develop effective methods to assist enterprise to measure the firms’ operation efficiency, find out the candidate priority under several different inputs and outputs, and forecast the values of those variables in the future. The methodologies are constructed by the concepts of Data Envelopment Analysis (DEA) and grey model (GM). Realistic data in four consecutive years (2009–2012) a total of 20 companies of the Electronic Manufacturing Service (EMS) industry that went public are completely collected. This paper tries to help target company—DMU1—to find the right alliance partners. By our proposed approach, the results show the priority in the recent years. The research study is hopefully of interest to managers who are in manufacturing industry in general and EMS enterprises in particular

COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation

Personalized text generation has broad industrial applications, such as explanation generation for recommendations, conversational systems, etc. Personalized text generators are usually trained on user written text, e.g., reviews collected on e-commerce platforms. However, due to historical, social, or behavioral reasons, there may exist bias that associates certain linguistic quality of user written text with the users' protected attributes such as gender, race, etc. The generators can identify and inherit these correlations and generate texts discriminately w.r.t. the users' protected attributes. Without proper intervention, such bias can adversarially influence the users' trust and reliance on the system. From a broader perspective, bias in auto-generated contents can reinforce the social stereotypes about how online users write through interactions with the users. In this work, we investigate the fairness of personalized text generation in the setting of explainable recommendation. We develop a general framework for achieving measure-specific counterfactual fairness on the linguistic quality of personalized explanations. We propose learning disentangled representations for counterfactual inference and develop a novel policy learning algorithm with carefully designed rewards for fairness optimization. The framework can be applied for achieving fairness on any given specifications of linguistic quality measures, and can be adapted to most of existing models and real-world settings. Extensive experiments demonstrate the superior ability of our method in achieving fairness while maintaining high generation performance

Service Innovation Model of the Automobile Service Industry

Abstract Service innovations in the service industry will improve its ability to compete in maintenance as well as increase the number of customers. Measuring service innovations according to the characteristics of the automobile industry is important. Thus, the aim of this paper is to develop a model to measure service innovation in the automobile service industry. A structural equation model is created based on theory and previous models. Data about the industry’s perception of service innovation are collected from questionnaires in Kaohsiung, Taiwan. Samples were collected from 61 authorized automobile service centers. The results indicate that customer demand, competition, and a knowledge-based network as enabler factors are positively correlated with service innovation. Furthermore, a new service concept, new client interface, new service delivery system, and new technology options are positively related to service innovation. Finally, service innovation has a significant positive e�ect on a firm’s performance. This study developed an e�ective service innovation model of the automobile service industry