Browsing by Author "Kang, Jia-Rong"

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An integer programming approach and visual analysis for detecting hierarchical community structures in social networks
(2015-04-01)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Chen, Jyun-Yu
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工業工程與管理學系
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Department of Industrial Engineering and Management
Detecting community structures in social networks is a very important task in social network analysis as these community structures explain relationships among individuals and can be used to predict social behavior. The relationship among subcommunities in each community can further be identified as hierarchical community structures, in which each super node at each hierarchical level represents a nested structure of communities or nodes. Most previous studies attempting to detect hierarchical community structures focused on new metaheuristics that are computationally efficient but do not guarantee the optimal community partition. As a result, this work applies a novel integer programming (IP) approach to detect hierarchical community structures in social networks. This approach has flexible community capacity limits, does not limit the community numbers at different levels, and maximizes a quality measure for hierarchical community partition. The proposed IP approach can use existing software solvers to detect hierarchical community structures without implementing an algorithm. Visual analysis of experimental results shows that the proposed model with different settings for level numbers can analyze reasonable and sophisticated hierarchical community structures, such that the relationships between communities at different levels can be elucidated clearly. (C) 2014 Elsevier Inc. All rights reserved.
Detecting Hierarchical Community Structures in Social Networks Using Integer Linear Programming
(2013-01-01)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Chen, Jyun-Yu
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Chen, Chien-Liang
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工業工程與管理學系
Detection of hierarchical community structures is one of the most crucial tasks for analyzing complicated social networks. In a hierarchical community structure, the super node at a higher level represents a nested structure so that the relationship of subcommunities in a community can be observed. Most of the previous works focused on designing metaheuristics for detecting hierarchical community structures, which may be computationally efficient, but cannot always guarantee the community partition optimality. Hence, this paper proposes an integer linear programming model for detecting the hierarchical community structure in social networks, which takes into account the number of levels and the limit of community size of each level. Our experimental results show that our model can find a reasonable hierarchical community structure, where the interaction between communities at different levels can be comprehended more clearly.
Forecasting Rare Faults of Critical Components in LED Epitaxy Plants Using a Hybrid Grey Forecasting and Harmony Search Approach
(2016-12)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Kang, Jia-Rong
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Chang, Sen-Chia
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Chueh, Chuang-Hua
In the light emitting diode (LED) manufacturing industry, the most expensive and crucial facilities are manufacturing machines. Condition-based maintenance (CBM) for crucial components of a manufacturing machine aims to forecast in advance the precise time when some aging component will be broken and replace it in time, to avoid performing abnormally to manufacture defect products. This study focuses on the CBM for a crucial component called particle filter of a pneumatic conveyor machine in the LED epitaxy plant. Conventional forecasting methods were based on the theory of statistics, which requests a large number of data samples and assumes some probability distribution. With advance of machine technology, however, the data samples of broken particle filters to be collected are very few, such that those conventional methods cannot be applied. As a result, this study proposes a novel hybrid grey forecasting and harmony search approach, in which grey forecasting was shown to perform well for small data samples. In the proposed method, operating conditions of particle filters are monitored and collected by industrial sensors. Then, those data are preprocessed by data filtering and clustering. Finally, a hybrid grey forecasting and harmony search approach is used to fit the curve of the aging condition of a particle filter. Numerical analysis of a real example in an LED epitaxy plant shows that the proposed method performs better than conventional methods.
Forecasting Rare Faults of Critical Components in LED Epitaxy Plants Using a Hybrid Grey Forecasting and Harmony Search Approach
(2016-12)
Lin, Chun-Cheng
;
Deng, Der-Jiunn
;
Kang, Jia-Rong
;
Chang, Sen-Chia
;
Chueh, Chuang-Hua
In the light emitting diode (LED) manufacturing industry, the most expensive and crucial facilities are manufacturing machines. Condition-based maintenance (CBM) for crucial components of a manufacturing machine aims to forecast in advance the precise time when some aging component will be broken and replace it in time, to avoid performing abnormally to manufacture defect products. This study focuses on the CBM for a crucial component called particle filter of a pneumatic conveyor machine in the LED epitaxy plant. Conventional forecasting methods were based on the theory of statistics, which requests a large number of data samples and assumes some probability distribution. With advance of machine technology, however, the data samples of broken particle filters to be collected are very few, such that those conventional methods cannot be applied. As a result, this study proposes a novel hybrid grey forecasting and harmony search approach, in which grey forecasting was shown to perform well for small data samples. In the proposed method, operating conditions of particle filters are monitored and collected by industrial sensors. Then, those data are preprocessed by data filtering and clustering. Finally, a hybrid grey forecasting and harmony search approach is used to fit the curve of the aging condition of a particle filter. Numerical analysis of a real example in an LED epitaxy plant shows that the proposed method performs better than conventional methods.
Forest biomass-to-biofuel factory location problem with multiple objectives considering environmental uncertainties and social enterprises
(2020-07-20)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Huang, Guan-Lin
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Liu, Wan-Yu
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工業工程與管理學系
Forest biomass energy enjoys merit of large production, renewability, and clean combustion. However, most related works focused on only the objective of minimization of cost or pollution, but seldom on the social aspect. Social enterprise provides business models that cope with social or environmental problems, e.g., offering employment opportunities in disadvantaged areas. Therefore, this work considers social enterprises and environmental uncertainties in a forest biomass-to-biofuel factory location problem with multiple objectives, which determines whether to open forest biomass-to-biofuel factories at their potential locations to meet the energy demand and other practical constraints. Aside from minimization of cost and carbon emissions, the concerned problem additionally considers the objective of maximizing the job offers provided by opening factories from the social enterprise aspect. Additionally, based on the fuzzy theory, this problem includes the following environmental uncertainties: uncertain number of inventory days, uncertain job offers per unit of surplus factory scale, uncertain biomass production amount, and uncertain biofuel demand due to the price fluctuation of fossil fuels. This work employs the GIS to determine candidate locations of acquiring forest biomass and opening factories, and then solves the problem by fuzzy multi-objective linear programming. Through simulation, we observe the conflict among objectives, compare the differences between the proposed method and previous methods, and analyze the key factors that affect practical implementation. Simulation results show that when the total biofuel demand exceeds 200 million liters per year, the proposed method provides 16% more job offers than the previous method that focused on only cost minimization. (c) 2020 Elsevier Ltd. All rights reserved.
Joint order batching and picker Manhattan routing problem
(2016-05)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Hou, Chung-Chih
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Cheng, Chen -Yang
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工業工程與管理學系
In picking product items in a warehouse to fulfill customer orders, a practical way is to classify similar orders as the same batch and then to plan the optimal picker routing when picking each batch of items. Different from the previous problems, this work investigates the joint order batching and picker Manhattan routing problem, which simultaneously determines the optimal order batching allocation and the shortest picker Manhattan routing that cannot pass through storage shelves in the warehouse, under some practical constraints. This work further addresses this problem by particle swarm optimization with bad experience to avoid bad solutions, in which a novel solution representation is designed for simultaneously handling both order batching and picker routing. The idea of the design is to transform the warehouse floorplan into a grid, in which virtual order center and batch center are defined to represent symbolic positions of orders and batches of the solution, respectively. By calculating the distance between the two centers, similar orders are categorized as the same batch. Additionally, theoretical analysis of convergence and stability of the proposed approach is also derived. Performance of this approach is evaluated via comprehensive experimental analysis and a case study. (C) 2016 Elsevier Ltd. All rights reserved.
Modeling the Master Production Scheduling System with Downgraded Products for a TFT-LCD Module Factory
(2012-01-01)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Liu, Wan-Yu
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Chung, Shu-Hsing
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Chou, Kai-Shin
At the end processes of TFT-LCD supply chain, the LCM module factory takes charge of the process of assembling products, and its production planning needs to consider various products with different graded TFT-LCD panels while meeting customers\' specific qualifications, including lowest acceptance rates for quality and zero-bad-defect quantity. Since the machine setup time of the bottleneck workstation in the factory is very long, it is of practical importance to design an appropriate production scheduling with concerns about times of machine setups and different grades of TFT-LCD products. This paper proposes a production scheduling system that comprises the material planning with downgraded products as well as the bottleneck scheduling for a LCM module factory using make-to-stock production, and solves the system based on integer programming. Experimental results show that the system not only is capable of reducing the number of setup times, but also performs well in efficiency of preparing critical materials. From a practical aspect, such a system can support production managers to plan their production scheduling effectively.
Modeling the Material Allocation System for TFT-LCD Module Factories Based on Make-to-Stock Production
(2014-12-01)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Chung, Shu-Hsing
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工業工程與管理學系
;
Department of Industrial Engineering and Management
The thin flim transistor liquid crystal display (TFT-LCD) module factories are in charge of the last process of the TFT-LCD supply chain, i.e., assembling LCD panels (produced from the former process in the supply chain) as well as electronic components into TFT-LCD panels or the finished products using TFT-LCD panels. Since the variation of machine processes and production techniques at the former process of the supply chain results in a variety of qualities of LCD panels, the finished products assembled with those LCD panels of different qualities are also of different qualities, according to which they are classified into different grades. This paper models the material allocation system for a TFT-LCD module factory based on the make-to-stock (MTS) production, in which one of the main designs is to allow the flexibility that the finished products of higher grades are able to be downgraded to respond to the drastic variation of customer requirements for each grade of finished products. The system is modelled in detail by an integer programming approach that takes into account not only downgraded products but also the customer requirements for the quality qualification rate and the lower bound of non-dot-defect rate of products, to maximize the total profit of the product portfolio. The model is evaluated by conducting an experimental study for a numerical example, which turns out that the proposed system looks promising as it determines a feasible product portfolio that decreases both the inventory of LCD panels and the volume of unqualified products while maintaining a high profit.
Modelling a Nurse Shift Schedule with Multiple Preference Ranks for Shifts and Days-Off
(2014)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Liu, Wan-Yu
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Deng, Der-Jiunn
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工業工程與管理學系
When it comes to nurse shift schedules, it is found that the nursing staff have diverse preferences about shift rotations and days-off. The previous studies only focused on the most preferred work shift and the number of satisfactory days-off of the schedule at the current schedule period but had few discussions on the previous schedule periods and other preference levels for shifts and days-off, which may affect fairness of shift schedules. As a result, this paper proposes a nurse scheduling model based upon integer programming that takes into account constraints of the schedule, different preference ranks towards each shift, and the historical data of previous schedule periods to maximize the satisfaction of all the nursing staff's preferences about the shift schedule. The main contribution of the proposed model is that we consider that the nursing staff's satisfaction level is affected by multiple preference ranks and their priority ordering to be scheduled, so that the quality of the generated shift schedule is more reasonable. Numerical results show that the planned shifts and days-off are fair and successfully meet the preferences of all the nursing staff.
Nurse Scheduling with Joint Normalized Shift and Day-Off Preference Satisfaction Using a Genetic Algorithm with Immigrant Scheme
(2015-01-01)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Chiang, Ding-Jung
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Chen, Chien-Liang
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工業工程與管理學系
To make a fair and satisfactory nurse shift schedule, this paper proposes a novel preference satisfaction function, in which numbers of the preferred work shifts and days-off of the nursing staff are balanced, and ranks for preferences and number of the preference ranks satisfied so far are also considered. Such a preference function is capable of equivalently and fairly planning the nurse preference schedule to improve the total satisfaction. Additionally, distributed sensors can be applied to collect the information on hospital beds to provide the schedule planner to determine the lowest required amount of manpower for each work shift, to avoid the working overload of the nursing staff. To solve the nursing schedule problem, we propose a genetic algorithm (GA) with an immigrant scheme, in which utilization of the immigrant scheme is helpful in efficiently reducing amount of infeasible solutions due to practical scheduling constraints, so that the GA can efficiently find better solutions for larger-scale problems. Performance of the proposed GA with and without solution recovery scheme is evaluated by conducting a comprehensive experimental analysis on three different-size instances.