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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
;
Wu, Muh-Cherng
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工業工程與管理學系
;
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 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
;
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
;
Takano, Kenichi
;
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.
Scheduling a hybrid MTO/MTS semiconductor fab with machine-dedication features
(2008-03-01)
Wu, Muh-Cherng
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Jiang, Jr-Hsiung
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Chang, Wen-Jen
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
A semiconductor foundry is essentially a make-to-order (MTO) factory. Yet, in a low-demand season, it may enter into a hybrid business model-producing make-to-stock (MTS) as well as MTO products to maintain high utilization of machines. This research proposes a scheduling method for such a hybrid MTO/MTS system with machine-dedication characteristics, a constraint imposed on the process route caused by the advance of manufacturing technology. The scheduling method aims to achieve a high on-time delivery rate for MTO products as well as a,high throughput for MTS products. Simulation experiments show that the proposed scheduling method outperforms representative methods in the literature. (C) 2007 Elsevier B.V. All rights reserved.
Scheduling of multiple in-line steppers for semiconductor wafer fabs
(2014-03-01)
Chiou, Chie-Wun
;
Wu, Muh-Cherng
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
A few prior studies noticed that an in-line stepper (a bottleneck machine in a semiconductor fab) may have a capacity loss while operated in a low-yield scenario. To alleviate such a capacity loss, some meta-heuristic algorithms for scheduling a single in-line stepper were proposed. Yet, in practice, there are multiple in-line steppers to be scheduled in a fab. This article aims to enhance prior algorithms so as to deal with the scheduling for multiple in-line steppers. Compared to prior studies, this research has to additionally consider how to appropriately allocate jobs to various machines. We enhance prior algorithms by developing a chromosome-decoding scheme which can yield a job-allocation decision for any given chromosome (or job sequence). Seven enhanced versions of meta-heuristic algorithms (genetic algorithm, Tabu, GA-Tabu, simulated annealing, M-MMAX, PACO and particle swarm optimisation) were then proposed and tested. Numerical experiments indicate that the GA-Tabu method outperforms the others. In addition, the lower the process yield, the better is the performance of the GA-Tabu algorithm.
Effect of solution representations on Tabu search in scheduling applications
(2013-12-01)
Chen, Chen-Fu
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Wu, Muh-Cherng
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Lin, Keng-Han
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工業工程與管理學系
;
Department of Industrial Engineering and Management
This research investigates the application of meta-heuristic algorithms to a scheduling problem called permutation manufacturing-cell flow shop (PMFS) from two perspectives. First, we examine the effect of using different solution representations (S-new and S-old) while applying Tabu-search algorithm. Experimental results reveal that Tabu_S-new outperforms Tabu_S-old. The rationale why Tabu_S-new is superior is further examined by characterizing the intermediate outcomes of the evolutionary processes in these two algorithms. We find that the superiority of Snew is due to its relatively higher degree of freedom in modeling Tabu neighborhood. Second, we propose a new algorithm GA_Tabu_S-new, which empirically outperforms the state-of-the-art meta-heuristic algorithms in solving the PMFS problem. This research highlights the importance of solution representation in the application of meta-heuristic algorithm, and establishes a significant milestone in solving the PMFS problem. (C) 2013 Elsevier Ltd. All rights reserved.
A comparison of two chromosome representation schemes used in solving a family-based scheduling problem
(2013-06-01)
Chen, Chen-Fu
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Wu, Muh-Cherng
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Li, Yi-Hsun
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Tai, Pang-Hao
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Chiou, Chie-Wun
Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective developing new chromosome (solution) representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two chromosome representation schemes (S-old and S-new) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X_Y, where X represents an algorithmic mechanism and Y represents a chromosome representation. Experiment results indicate that GA_S-new outperforms GA_S-old, and ACO_S-new also outperforms ACO_S-old. These findings reveal the importance of developing new chromosome representations in the application of meta-heuristic algorithms. (C) 2012 Elsevier Ltd. All rights reserved.
A combined dispatching criteria approach to scheduling dual flow-shops
(2013-02-01)
Chiou, Chie-Wun
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Chen, Wen-Min
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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
This study investigates a dual flow-shops scheduling problem. In the scheduling context, there are two flow shops and each shop involves three processing stages. The two shops are functionally identical but their stage processing times for a job are different. While sequentially going through the three processing stages, each job is allowed to travel between the two shops. That is, for a job, each of its three stages could be processed in any of the two shops. Such a context is called dual flow-shops in the sense that the two flow shops capacities are completely shared. The scheduling problem involves two decisions: (1) route assignment (i.e. assigning the processing stages of a job to a shop), and (2) job sequencing (i.e. sequencing the jobs waiting before each stage). The scheduling objective is to minimise the coefficient of variation of slack time (CVS ), in which the slack time (also called lateness) denotes the difference between the due date and total completion time of a job. We propose five genetic-algorithm-based (GA-based) solution methods to solve the scheduling problem, which are called GA-EDD, GA-FIFO, GA-SPT, GA-LFO, and GA-COMBO respectively. Numerical experiments indicate that GA-COMBO outperforms the other four methods.
A genetic algorithm for scheduling dual flow shops
(2012-01-01)
Chiou, Chie-Wun
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Chen, Wen-Min
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Liu, Chin-Min
;
Wu, Muh-Cherng
;
工業工程與管理學系
This study examines a dual-flow shop-scheduling problem that allows cross-shop processing. The scheduling objective is to minimize the coefficient of variation of slack time (lateness), where the slack time (ST) of a job denotes the difference between its due date and total completion time. This scheduling problem involves two decisions: job route assignment (assigning jobs to shops) and job sequencing. This study develops a genetic algorithm (GA) embedded with the earliest due date (EDD) dispatching rule for making these decisions. Numerical experiments with the GA algorithm indicate that the performance of adopting a cross-shop production policy may significantly outperform that of adopting a single-shop production policy. This is particularly true when the two flow shops are asymmetrically designed. This study develops a grouping heuristic algorithm to reduce setup time and due-date-based demand simultaneously. This study uses the proposed genetic algorithm (GA) to prove that the grouping heuristic algorithm performs well. Obtaining an approximate optimal solution makes it possible to decide the route assignment of jobs and the job sequencing of machines. (C) 2011 Elsevier Ltd. All rights reserved.
Exploring design features for enhancing players' challenge in strategy games
(2007-06-01)
Hsu, Shang Hwa
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Wen, Ming-Hui
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Wu, Muh-Cherng
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
This paper examines how to make a player feel more challenged in a strategic computer game. It is hypothesized that information availability and resource advantage affect play difficulty, which in turn affects the challenge experienced. The difficulty of play can be defined in terms of the mental workload that players experience and the physical effort that players exert. Forty-five male college and graduate students participated in a 3 x 3 ( information availability x resource advantage) between-subjects factorial design experiment. This experiment measured player mental workload, physical effort, and challenge. The results indicate that information availability affects player mental workload, and resource advantage affects levels of player physical effort, respectively. Moreover, the relationship between mental workload and challenge was found to be an inverted U-shaped curve; in other words, too much or too little mental workload may decrease player challenge. The relationship between physical effort and challenge exhibited similar characteristics.

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