Browsing by Author "Chang, Tsung-Sheng"

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A Decision Support System Based on a Genetic Algorithm for the Utilization of Leftovers
(2016-05-03)
Fu, Hsin-Pin
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Ku, Cheng-Yuan
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Chang, Tsung-Sheng
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
There are usually some leftovers (usable pieces of raw material) and scraps (unusable pieces of raw material).generated after the completion of a manufacturing process. These leftovers consist of many different types, materials, styles and sizes so the use of such materials is difficult to manage, resulting in a significant material management problem for manufacturers. This study examines ways to use these leftovers and proposes the application of a genetic algorithm to handle the problem of matching usable leftovers to incoming orders. In order to verify the feasibility of the solution, a decision support system was implemented, based on a genetic algorithm. With lots of experimental results, we conclude this method is thus of considerable value to the managers of manufacturing factories.
A novel model to implement ERP based on dynamic capabilities A case study of an IC design company
(2015-01-01)
Chang, Tsung-Sheng
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Fu, Hsin-Pin
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Ku, Cheng-Yuan
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Purpose - The purpose of this paper is to propose an implementation model for enterprise resource planning (ERP) based on resource-based view, and using the dynamic capability theory as its theoretical foundation. This model includes: the establishment of the objectives of the implementation, an assessment of the available resources and the scope of the implementation, the redesign/integration and organizational learning during the process, the implementation of the system, and the measurement and evaluation of its performance. Design/methodology/approach - An integrated circuits design company in Taiwan was used in a case study to examine the validity of the proposed model. Findings - When the proposed ERP implementation model was applied in this study, the results show that organizational coordination, system-process redesign and integration, and organizational learning are the critical strategies for enterprises, in order to reduce the risks during the implementation of ERP projects. Practical implications - This model can help enterprises recognize the resources needed when implementing an ERP. In addition, they need to consider the reliability of these resources, as this will increase the efficiency of the implementation, and thus the probability of success. Originality/value - Studies of past models in the implementation of ERP have been conducted in various industries. There is a need for further studies that evaluate the different concepts in terms of the effectiveness of specific methods, in order to enhance the probability of successfully implementing a dynamic system. This paper is one of the first to explain how an enterprise can implement an ERP that is based on the theory of dynamic capabilities. The case study illustrates the important, critical success factors.
DEA models incorporating uncertain future performance
(2016-10-16)
Chang, Tsung-Sheng
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Tone, Kaoru
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Wu, Chen-Hui
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Conventional data envelopment analysis (DEA) models are designed for measuring the productive efficiency of decision making units (DMUs) based merely on historical data. However, in many practical applications, such past results are not sufficient for evaluating a DMU\'s performance in highly volatile operating environments, such as those with highly volatile crude oil prices and currency exchange rates. That is, in such environments, a DMU\'s whole performance may be seriously distorted if its future performance, which is sensitive to crude oil price volatility and/or currency fluctuations, is ignored in the evaluation process. However, despite its importance, to our knowledge, there are no DEA models proposed in the literature that explicitly take future performance volatility into account. Hence, this research aims at developing a new system of DEA models that incorporate a DMU\'s uncertain future performance, and thus can be applied to fully measure their efficiency. (C) 2016 Elsevier B.V. All rights reserved.
Handling negative data in slacks-based measure data envelopment analysis models
(2020-05-01)
Tone, Kaoru
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Chang, Tsung-Sheng
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Wu, Chen-Hui
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
This paper proposes slacks-based measure (SBM) data envelopment analysis (DEA) models that handle negative data. Unlike existing negative data allowable DEA models, the proposed SBM DEA models are consistent with ordinary SBM models and units invariant, they handle various types of returns to scale, and they avoid division by zero. These new SBM DEA models transform original negative inputs and outputs into positive counterparts based on a newly defined "base point". Hence, these models are referred to as the BP-SBM DEA models. In addition to the basic BP-SBM DEA models, this research further develops data-oriented and application-oriented BP-SBM DEA-type models for different application problems involving negative data. Numerical examples are provided to illustrate various aspects and implementation details of these models. (C) 2019 Elsevier B.V. All rights reserved.
Past-present-future Intertemporal DEA models
(2015-01-01)
Chang, Tsung-Sheng
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Tone, Kaoru
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Wu, Chen-Hui
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交大名義發表
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運輸與物流管理系註：原交通所+運管所
It has been well recognized that to thoroughly evaluate a firm\'s performance, the evaluator must assess not only its past and present records but also future potential. However, to the best of our knowledge, there are no data envelopment analysis (DEA)-type models proposed in the literature that simultaneously take past, present and, especially, future performance indicators into account. Hence, this research aims at developing a new type of DEA model referred to as Intertemporal DEA models that can be used to fully measure a firm\'s efficiency by explicitly considering its key inputs and outputs involving the past-present-future time span. In this research, the proposed Intertemporal DEA models are applied to the performance evaluation of high-tech Integrated Circuit design companies in Taiwan to demonstrate their advantages over other DEA models that ignore intertemporal efficiency.
Performance Evaluation of Alternative Jet Fuels using a hybrid MCDA method
(2019-01-01)
Xu, Bing
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Kolosz, Ben W.
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Andresen, John M.
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Ouenniche, Jamal
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Greening, Phil
We design a hybrid multi-criteria decision-making analysis (MCDA)-based framework to evaluate competing alternative jet fuels (AJFs). Our analysis on AJFs is based on a wide range of criteria with different forms of data and relations and reflect stakeholders' view and opinions. This contrast with conventional cost-benefit analysis which typically focuses on the economic benefits at the expense of environmental, technological and social aspects. Our work adds value to understand developing technologies to date, technical barriers to commercialization, other development drivers such as the availability and willingness of financial institutions to finance, businesses to invest in AJFs productions and its supply chains in the UK, policy support and regulation, and likely commercialization and scale-up potentials. By focusing on motives, attitudes and stakeholders rather than merely the technical and economic aspects of AJFs, our paper makes an important contribution to the field. (C) 2019 The Authors. Published by Elsevier Ltd.
Quantile-DEA classifiers with interval data
(2014-06-01)
Wei, Quanling
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Chang, Tsung-Sheng
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Han, Song
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
This research intends to develop the classifiers for dealing with binary classification problems with interval data whose difficulty to be tackled has been well recognized, regardless of the field. The proposed classifiers involve using the ideas and techniques of both quantiles and data envelopment analysis (DEA), and are thus referred to as quantile-DEA classifiers. That is, the classifiers first use the concept of quantiles to generate a desired number of exact-data sets from a training-data set comprising interval data. Then, the classifiers adopt the concept and technique of an intersection-form production possibility set in the DEA framework to construct acceptance domains with each corresponding to an exact-data set and thus a quantile. Here, an intersection-form acceptance domain is actually represented by a linear inequality system, which enables the quantile-DEA classifiers to efficiently discover the groups to which large volumes of data belong. In addition, the quantile feature enables the proposed classifiers not only to help reveal patterns, but also to tell the user the value or significance of these patterns.