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Now showing 1 - 10 of 38

Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems
(2019-08-01)
Lin, Chi-Shiuan
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Lee, I-Ling
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Wu, Muh-Cherng
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工業工程與管理學系
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Department of Industrial Engineering and Management
Two conjectures, the use of incomplete chromosome representations and shadow chromosomes may improve the performance of genetic algorithms (GAs), are examined in this study. The examination entails testing distributed flexible job shop scheduling (DFJS) problems subject to preventive maintenance (PM) that involve four scheduling decisions. Genetic algorithms based on a complete chromosome representation that explicitly models the four decisions have been developed previously. By contrast, herein, two incomplete chromosome representations are proposed, whereby the conjectured advantages are two-fold. First, an incomplete chromosome representation models two scheduling decisions, and the remaining two are decoded by heuristic rules designed to ensure the load balance of manufacturing resources. Therefore, scheduling solutions with load imbalance will not be generated, which will help prevent the execution of ineffective searches. Second, a novel method of generating new chromosomes is developed and employed, instead of using traditional genetic operations. These chromosomes, called shadow chromosomes, are generated from good quality scheduling solutions and they may improve performance. Based on these two conjectures, four GAs are proposed. Numerical experiments reveal that each proposed GA outperforms the prior GAs substantially and the two conjectures are thus well justified. These findings shed light on the application of the two conjectures for developing metaheuristic algorithms to solve other high-dimensional space search problems.
A Modified Lotka-Volterra Model for Diffusion and Substitution of Multigeneration DRAM Processing Technologies
(2017-01-01)
Hung, Hui-Chih
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Chiu, Yu-Chih
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Wu, Muh-Cherng
;
工業工程與管理學系
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Department of Industrial Engineering and Management
We attempt to develop an effective forecasting model for the diffusion and substitution of multigeneration Dynamic Random Access Memory (DRAM) processing technologies. We consider market share data and propose a modified Lotka-Volterramodel, in which an additional constraint on the summation of market share is introduced. The mean absolute error is used to measure the accuracy of our market share predictions. Market share data in DRAM industries from quarter one (Q1) of 2005 to 2013 Q4 is collected to validate the prediction accuracy. Our model significantly outperforms other benchmark forecasting models of both revenue and market share data, including the Bass and Lotka-Volterra models. Compared to prior studies on forecasting the diffusion and substitution of multigeneration technologies, our model has two new perspectives: (1) allowing undetermined number of multigeneration technologies and inconsecutive adoption of new technologies and (2) requiring less data for forecasting newborn technologies.
A case-based reasoning approach to generating new product ideas
(2006-08-01)
Wu, Muh-Cherng
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Lo, Ying-Fu
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Hsu, Shang-Hwa
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工業工程與管理學系
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Department of Industrial Engineering and Management
This paper presents an approach to facilitate the generation of new product ideas. In this approach, a product is represented by a vector, which involves a number of product attributes. These attributes are defined based on the user-centered design (UCD) paradigm. That is, the context of use and interactions with users for the product is described by these attributes. By the product representation, a product database can be established. For a benchmarking product, we use the case-based reasoning (CBR) technique to retrieve those products, which are "sufficiently relevant" to the benchmarking one, from the database. The functions of the retrieved products are the candidate new ideas to be added on the benchmarking product. These retrieved products are screened manually to identify creative and valuable ideas. A prototype system of this approach has been implemented. Experiments show that the proposed approach outperforms the brainstorming technique, which has been widely used in industry.
Analysis of Competition Between IDM and Fabless-Foundry Business Models in the Semiconductor Industry
(2017-08-01)
Hung, Hui-Chih
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Chiu, Yu-Chih
;
Wu, Muh-Cherng
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Three major business models are found in the semiconductor industry, namely, the integrated device manufacturer (IDM), fabless, and foundry models. IDMs design and manufacture integrated circuits (ICs), fabless companies focus only on IC design, and foundry companies are concerned only with IC manufacturing. The business model resulting when fabless and foundry companies form a strategic alliance is called the fabless-foundry model. Recently, the revenue growth rate of the fabless-foundry model has been considerably higher than that of IDM. This raises the question of whether the fabless- foundry model will ultimately dominate the semiconductor industry or grow only to a certain extent. To investigate this, we adopted the Hodrick-Prescott filter to remove the cyclic signal from original revenue data and then applied the Lotka-Volterra model to analyze the competition and cooperation relationships between pairs of these business models. The results reveal that the fabless and foundry models have a mutualistic relationship, whereas IDM competes with both the fabless and foundry models. IDM and the fabless- foundry model compete, but their revenues should ultimately arrive at an equilibrium point. In the long term, IDM and the fabless- foundry model should coexist; the ultimate market share of IDM is predicted to be approximately 55% and that of the fabless- foundry model approximately 45%.
An enhanced application of Lotka-Volterra model to forecast the sales of two competing retail formats
(2017-07-01)
Hung, Hui-Chih
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Chiu, Yu-Chih
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Huang, Huang-Chen
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Wu, Muh-Cherng
;
工業工程與管理學系
This research develops a sales forecasting model that can analyze the interaction effects of two retail competing formats (convenience-oriented vs. budget-oriented formats). A traditional approach to making such a forecast is based on the Lotka-Volterra equations (also called the LV-model). The LV-model assumes that the population of each species is affected by its self-growth, internal interaction within the species, and external interaction with other species. Most prior studies in business applications directly use sales data as input to the LV-model. The prior approach may result in misleading conclusions when sales data are embedded with seasonal variation, because this variation is not addressed in the original development of the LV-model. Therefore, this study proposes a forecasting framework (an enhanced application of the LV-model). The sales data of each retail format is considered as a compound data, which is decomposed into three individual components: (1) aggregate, (2) competition, and (3) seasonal components. The LV-model is used to forecast the competition component; the other two components are forecasted by typical time series methods; and the data of three components are finally combined into one. Empirical study indicates that the proposed method substantially outperforms the prior approach in terms of forecasting errors (4.4% vs. 16.7% for convenience-oriented and 5.8% vs. 16.2% for budget oriented). In addition, the proposed method reveals a more convincing predator-prey relationship between the two retail formats, which concludes that the convenience-oriented is the predator. To the opposite, the prior approach, concluding that the budget-oriented is the predator, is quite doubtful because the convenience-oriented shall be preferred while the GDP grows over time. This research makes a contribution in how to appropriately apply the LV-model in forecasting revenue and analyzing the interaction effects of two competing business species. (C) 2017 Elsevier Ltd. All rights reserved.
A membership pricing policy to facilitate service scale-expansion
(2017-01-01)
Hung, Hui-Chih
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Chung, Chung-Yu
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Wu, Muh-Cherng
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Shen, Wan-Ling
;
工業工程與管理學系
This paper proposes a multi-year membership pricing policy for a service business like a gym chain. In the pricing policy, the equivalent membership price per year is relatively low to attract customers, and the multi-year membership fees must be prepaid in full. The prepaid cash provides resources for scale-expansion by opening new stores. We develop a nonlinear mixed integer programming model to formulate the pricing decisions. Numerical experiments reveal that the multi-year membership pricing policy, from a long-term perspective, is substantially better than the business-as-usual pricing policy (1-year membership) in cash balance, profit, and market share. Yet, the performances of the two pricing policies do not differ much from a short-term perspective. These findings indicate that the multi-year pricing policy may be a good strategy, because it initially attracts less attention of competitors; suddenly it emerges and substantially outperforms its competitors in cash balance, profit, and market share.
A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems
(2018-01-01)
Lu, Po-Hsiang
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Wu, Muh-Cherng
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Tan, Hao
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Peng, Yong-Han
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Chen, Chen-Fu
This paper proposes a genetic algorithm for solving distributed and flexible job-shop scheduling (DFJS) problems. A DFJS problem involves three scheduling decisions: (1) job-to-cell assignment, (2) operation-sequencing, and (3) operation-to-machine assignment. Therefore, solving a DFJS problem is essentially a 3-dimensional solution space search problem; each dimension represents a type of decision. The algorithm is developed by proposing a new and concise chromosome representation , which models a 3-dimensional scheduling solution by a 1-dimensional scheme (i.e., a sequence of all jobs to be scheduled). That is, the chromosome space is 1-dimensional (1D) and the solution space is 3-dimensional (3D). In , we develop a 1D-to-3D decoding method to convert a 1D chromosome into a 3D solution. In addition, given a 3D solution, we use a refinement method to improve the scheduling performance and subsequently use a 3D-to-1D encoding method to convert the refined 3D solution into a 1D chromosome. The 1D-to-3D decoding method is designed to obtain a "good" 3D solution which tends to be load-balanced. In contrast, the refinement and 3D-to-1D encoding methods of a 3D solution provides a novel way (rather than by genetic operators) to generate new chromosomes, which are herein called shadow chromosomes. Numerical experiments indicate that outperforms the IGA developed by De Giovanni and Pezzella (Eur J Oper Res 200:395-408, 2010), which is the up-to-date best-performing genetic algorithm in solving DFJS problems.
Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems
(2017-04)
Wu, Muh-Cherng
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Lin, Chi-Shiuan
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Lin, Chia-Hui
;
Chen, Chen-Fu
;
工業工程與管理學系
This paper attempts to compare the effect of using different chromosome representations while developing a genetic algorithm to solve a scheduling problem called DFJS (distributed flexible job shop scheduling) problem. The DFJS problem is strongly NP-hard; most recent prior studies develop various genetic algorithms (GAs) to solve the problems. These prior GAs are similar in the algorithmic flows, but are different in proposing different chromosome representations. Extending from this line, this research proposes a new chromosome representation (called Sop) and develops a genetic algorithm (called GA_OP) to solve the DFJS problem. Experiment results indicate that GA_OP outperforms all prior genetic algorithms. This research advocates the importance of developing appropriate chromosome representations while applying genetic algorithms (or other meta-heuristic algorithms) to solve a space search problem, in particular when the solution space is high-dimensional. (C) 2016 Elsevier Ltd. All rights reserved.
A modified Lotka-Volterra model for competition forecasting in Taiwan\'s retail industry
(2014-11-01)
Hung, Hui-Chih
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Tsai, Yun-San
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Wu, Muh-Cherng
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工業工程與管理學系
;
Department of Industrial Engineering and Management
The retail industry is an important component of the supply chain of the goods and services that are consumed daily and competition has been increasing among retailers worldwide. Thus, forecasting the degree of retail competition has become an important issue. However, seasonal patterns and cycles in the level of retail activity dramatically reduce forecasting accuracy. This paper attempts to develop an improved forecasting methodology for retail industry competition subject to seasonal patterns and cycles. Using market share data and the moving average method, a modified Lotka-Volterra model with an additional constraint on the summation of market share is proposed. Furthermore, the mean absolute error is used to measure the forecasting accuracy of the market share. Real Taiwanese retail data from 1999 is used to validate the forecasting accuracy of our modified Lotka-Volterra model. Our methodology successfully mitigates errors from seasonal patterns and cycles and outperforms other benchmark models. These benchmarks include the Bass and Lotka-Volterra models for revenue or market share data, with or without using the moving average method. Our methodology assists the retail industry in the development of management strategies and the determination of investment timing. We also demonstrate how the Lotka-Volterra model can be used to forecast the degree of industry competition. (C) 2014 Elsevier Ltd. All rights reserved.
The influence of organizational factors on safety in Taiwanese high-risk industries
(2010-09-01)
Hsu, Shang Hwa
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Lee, Chun-Chia
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Wu, Muh-Cherng
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Takano, Kenichi
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工業工程與管理學系
Organizational theorists have recently begun to pay research attention to the workforces of emerging countries in Asia, especially the greater China regions. However, little research has focused on the influence of organizational factors on safety in these countries. This study hypothesized that organizational factors, characterized as safety leadership perspective (management commitment, blame culture) and safety climate perspective (harmonious relationships), would influence group-level safety management, which would in turn influence individual level safety awareness and practices. To test this hypothesis, a safety climate questionnaire was distributed to ten Taiwanese plants in high-risk industries. Structural equation modeling (SEM) was used to analyze the linkages among organizational level factors, work-group-level factors, and workers' safety awareness and practices. We found that safety leadership style and organizational harmony in Taiwanese high-risk industries can exert significant influences on work-group processes, which in turn have greater effects on individual safety awareness and practices. These findings and implications can serve as a basis for safety improvement in areas of the greater China region. (C) 2010 Elsevier Ltd. All rights reserved.

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