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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Industrial Engineering and Management Systems
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Journal DOI :
Korean Institute of Industrial Engineers
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Volume & Issues
Volume 13, Issue 4 - Dec 2014
Volume 13, Issue 3 - Sep 2014
Volume 13, Issue 2 - Jun 2014
Volume 13, Issue 1 - Mar 2014
Selecting the target year
On A New Framework of Autoregressive Fuzzy Time Series Models
Song, Qiang ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 357~368
DOI : 10.7232/iems.2014.13.4.357
Since its birth in 1993, fuzzy time series have seen different classes of models designed and applied, such as fuzzy logic relation and rule-based models. These models have both advantages and disadvantages. The major drawbacks with these two classes of models are the difficulties encountered in identification and analysis of the model. Therefore, there is a strong need to explore new alternatives and this is the objective of this paper. By transforming a fuzzy number to a real number via integrating the inverse of the membership function, new autoregressive models can be developed to fit the observation values of a fuzzy time series. With the new models, the issues of model identification and parameter estimation can be addressed; and trends, seasonalities and multivariate fuzzy time series could also be modeled with ease. In addition, asymptotic behaviors of fuzzy time series can be inspected by means of characteristic equations.
Solving a New Multi-Period Multi-Objective Multi-Product Aggregate Production Planning Problem Using Fuzzy Goal Programming
Khalili-Damghani, Kaveh ; Shahrokh, Ayda ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 369~382
DOI : 10.7232/iems.2014.13.4.369
This paper introduces a new multi-product multi-period multi-objective aggregate production planning problem. The proposed problem is modeled using multi-objective mixed-integer mathematical programming. Three objective functions, including minimizing total cost, maximizing customer services level, and maximizing the quality of end-product, are considered, simultaneously. Several constraints such as quantity of production, available time, work force levels, inventory levels, backordering levels, machine capacity, warehouse space and available budget are also considered. Some parameters of the proposed model are assumed to be qualitative and modeled using fuzzy sets. Then, a fuzzy goal programming approach is proposed to solve the model. The proposed approach is applied on a real-world industrial case study of a color and resin production company called Teiph-Saipa. The approach is coded using LINGO software. The efficacy and applicability of the proposed approach are illustrated in the case study. The results of proposed approach are compared with those of the existing experimental methods used in the company. The relative dominance of the proposed approach is revealed in comparison with the experimental method. Finally, a data dictionary, including the way of gathering data for running the model, is proposed in order to facilitate the re-implementation of the model for future development and case studies.
Reverse Logistics Network Design with Incentive-Dependent Return
Asghari, Mohammad ; Abrishami, Salman J. ; Mahdavi, Faezeh ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 383~397
DOI : 10.7232/iems.2014.13.4.383
Reverse logistics network design issues have been popularly discussed in recent years. However, few papers in the past literature have been dedicated to incentive effect on return quantity of used products. The purpose of this study is to formulate a dynamic nonlinear programming model of reverse logistics network design with the aim of managing the used products allocation by coordinating the collection centers and recovery facilities to warrant economic efficiency. In the optimization model, a fuzzy approach is applied to interpret the relationship between the rate of return and the suggested incentives. Due to funding constraints in setting up the collection centers, this work considers these centers as multi-capacity levels, which can be opened or closed at different periods. In view of the fact that the problem is known as NP-hard, we propose a heuristic method based on tabu search procedure to solve the presented model. Finally, several dominance properties of optimal solutions are demonstrated in comparison with the results of a state-of-the-art commercial solver.
Humanitarian Relief Logistics with Time Restriction: Thai Flooding Case Study
Manopiniwes, Wapee ; Nagasawa, Keisuke ; Irohara, Takashi ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 398~407
DOI : 10.7232/iems.2014.13.4.398
Shortages and delays in a humanitarian logistics system can contribute to the pain and suffering of survivors or other affected people. Humanitarian logistics budgets should be sufficient to prevent such shortages or delays. Unlike commercial supply chain systems, the budgets for relief supply chain systems should be able to satisfy demand. This study describes a comprehensive model in an effort to satisfy the total relief demand by minimizing logistics operations costs. We herein propose a strategic model which determines the locations of distribution centers and the total inventory to be stocked for each distribution center where a flood or other catastrophe may occur. The proposed model is formulated and solved as a mixed-integer programming problem that integrates facility location and inventory decisions by considering capacity constraints and time restrictions in order to minimize the total cost of relief operations. The proposed model is then applied to a real flood case involving 47 disaster areas and 13 distribution centers in Thailand. Finally, we discuss the sensitivity analysis of the model and the managerial implications of this research.
Symbol Characters Allocation of a QWERTY Type Keyboard Design for Smartphones
Kim, Kuk ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 408~413
DOI : 10.7232/iems.2014.13.4.408
The QWERTY type keyboard is a classical device that has been used for computers for a long time. The keyboard design of mobile devices like smartphones is an important issue to consider because of the limited space on the touch screen. This paper presents a design for symbol allocation on the QWERTY type soft keyboard. A 27-cell model, including the full stop (.), is proposed in this paper. A QWERTY type keyboard for smartphones is mainly known for its spatial compatibility, whereas the characters of the ANSI keyboard are allocated to the shoulder positions for functional auxiliary input methods such as the long pressing method.
A Workplace to Support Creativity
Samani, Sanaz Ahmadpoor ; Rasid, Siti Zaleha Binti Abdul ; bt Sofian, Saudah ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 414~420
DOI : 10.7232/iems.2014.13.4.414
The purpose of this paper is to provide a review of the background information regarding to the role of workplace on affecting people's performance. In today's industry creativity has a very special and important place because of the dynamic organizational changes and rapid growth of technology. To support these new working styles and specifically, to support creativity within an organization, flexible workplaces are often suggested. Since open-plan office offers more flexibility when compared to completely closed and private ones, they are seen to have more capabilities and are highly valued in today's industry. So the result of this study will contribute towards enhancing the understanding of the effect office design to enhance employees' performance, especially in creative tasks.
Supervised Learning-Based Collaborative Filtering Using Market Basket Data for the Cold-Start Problem
Hwang, Wook-Yeon ; Jun, Chi-Hyuck ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 421~431
DOI : 10.7232/iems.2014.13.4.421
The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.
Business Model Mining: Analyzing a Firm's Business Model with Text Mining of Annual Report
Lee, Jihwan ; Hong, Yoo S. ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 432~441
DOI : 10.7232/iems.2014.13.4.432
As the business model is receiving considerable attention these days, the ability to collect business model related information has become essential requirement for a company. The annual report is one of the most important external documents which contain crucial information about the company's business model. By investigating business descriptions and their future strategies within the annual report, we can easily analyze a company's business model. However, given the sheer volume of the data, which is usually over a hundred pages, it is not practical to depend only on manual extraction. The purpose of this study is to complement the manual extraction process by using text mining techniques. In this study, the text mining technique is applied in business model concept extraction and business model evolution analysis. By concept, we mean the overview of a company's business model within a specific year, and, by evolution, we mean temporal changes in the business model concept over time. The efficiency and effectiveness of our methodology is illustrated by a case example of three companies in the US video rental industry.
Portfolio Optimization with Groupwise Selection
Kim, Namhyoung ; Sra, Suvrit ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 442~448
DOI : 10.7232/iems.2014.13.4.442
Portfolio optimization in the presence of estimation error can be stabilized by incorporating norm-constraints; this result was shown by DeMiguel et al. (A generalized approach to portfolio optimization: improving performance by constraining portfolio norms, Management Science, 5, 798-812, 2009), who reported empirical performance better than numerous competing approaches. We extend the idea of norm-constraints by introducing a powerful enhancement, grouped selection for portfolio optimization. Here, instead of merely penalizing norms of the assets being selected, we penalize groups, where within a group assets are treated alike, but across groups, the penalization may differ. The idea of groupwise selection is grounded in statistics, but to our knowledge, it is novel in the context of portfolio optimization. Novelty aside, the real benefits of groupwise selection are substantiated by experiments; our results show that groupwise asset selection leads to strategies with lower variance, higher Sharpe ratios, and even higher expected returns than the ordinary norm-constrained formulations.
Prediction of Hypertension Complications Risk Using Classification Techniques
Lee, Wonji ; Lee, Junghye ; Lee, Hyeseon ; Jun, Chi-Hyuck ; Park, Il-Su ; Kang, Sung-Hong ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 449~453
DOI : 10.7232/iems.2014.13.4.449
Chronic diseases including hypertension and its complications are major sources causing the national medical expenditures to increase. We aim to predict the risk of hypertension complications for hypertension patients, using the sample national healthcare database established by Korean National Health Insurance Corporation. We apply classification techniques, such as logistic regression, linear discriminant analysis, and classification and regression tree to predict the hypertension complication onset event for each patient. The performance of these three methods is compared in terms of accuracy, sensitivity and specificity. The result shows that these methods seem to perform similarly although the logistic regression performs marginally better than the others.
Comparative Study of Dimension Reduction Methods for Highly Imbalanced Overlapping Churn Data
Lee, Sujee ; Koo, Bonhyo ; Jung, Kyu-Hwan ;
Industrial Engineering and Management Systems, volume 13, issue 4, 2014, Pages 454~462
DOI : 10.7232/iems.2014.13.4.454
Retention of possible churning customer is one of the most important issues in customer relationship management, so companies try to predict churn customers using their large-scale high-dimensional data. This study focuses on dealing with large data sets by reducing the dimensionality. By using six different dimension reduction methods-Principal Component Analysis (PCA), factor analysis (FA), locally linear embedding (LLE), local tangent space alignment (LTSA), locally preserving projections (LPP), and deep auto-encoder-our experiments apply each dimension reduction method to the training data, build a classification model using the mapped data and then measure the performance using hit rate to compare the dimension reduction methods. In the result, PCA shows good performance despite its simplicity, and the deep auto-encoder gives the best overall performance. These results can be explained by the characteristics of the churn prediction data that is highly correlated and overlapped over the classes. We also proposed a simple out-of-sample extension method for the nonlinear dimension reduction methods, LLE and LTSA, utilizing the characteristic of the data.