Browsing by Author "Chiou, Chie-Wun"

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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
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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 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 GA-Tabu algorithm for scheduling in-line steppers in low-yield scenarios
(2009-11-01)
Chiou, Chie-Wun
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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
This paper presents a scheduling algorithm for an in-line stepper in low-yield scenarios, which mostly appear in cases when new process/production is introduced. An in-line stepper is a bottleneck machine in a semiconductor fab. Its interior comprises a sequence of chambers, while its exterior is a dock equipped with several ports. The transportation unit for entry of each port is a job (a group of wafers), while that for each chamber is a piece of wafer. This transportation incompatibility may lead to a capacity-loss, in particular in low-yield scenarios. Such a capacity-loss could be alleviated by effective scheduling. The proposed scheduling algorithm, called GA-Tabu, is a combination of a genetic algorithm (GA) and a tabu search technique. Numerical experiments indicate that the GA-Tabu algorithm outperforms seven benchmark ones. In particular, the GA-Tabu algorithm outperforms a prior GA both in solution quality and computation efforts. (C) 2009 Elsevier Ltd. All rights reserved.
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
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Wu, Muh-Cherng
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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.
Product-mix decision in a mixed-yield wafer fabrication scenario
(2006-07-01)
Wu, Muh-Cherng
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Chang, Wen-Jung
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Chiou, Chie-Wun
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工業工程與管理學系
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Department of Industrial Engineering and Management
The product mix decision problem for semiconductor manufacturing has been extensively studied in literature. However, most of them are based on a high-yield scenario. Yet, in a low-yield manufacturing environment, some research claims that scrap low-yield lots in an early stage may produce more profit. Considering the early scrapping characteristics, this paper aims to solve the product mix decision problem for a mixed-yield scenario, which involves the simultaneous production of high-yield and low-yield products. A nonlinear mathematical program is developed to model the decision problem. Two methods for solving the nonlinear program are proposed. Method 1 converts the nonlinear program into a linear program by setting some variables as parameters. The method provides an optimal solution by exhaustively searching these parameterized variables and solving the LP models iteratively. Method 2 aims to reduce the computation complexity while providing a near optimal solution. Experiment results show that method 2 is better than method 1, when aggregately considering solution quality and computation efforts.
Scheduling of multiple in-line steppers for semiconductor wafer fabs
(2014-03-01)
Chiou, Chie-Wun
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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
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.
Scheduling semiconductor in-line steppers in new product/process introduction scenarios
(2010)
Wu, Muh-Cherng
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Chiou, Chie-Wun
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工業工程與管理學系
;
Department of Industrial Engineering and Management
This paper presents a scheduling method for an in-line stepper operating in a new process/production introduction (NPI) scenario. An in-line stepper is a bottleneck machine in a semiconductor fab. Its interior is comprised of a series of chambers, while its exterior is a dock equipped with a limited number of ports. The transportation unit for each chamber is a piece of wafer, while that for each port is a job that can contain up to 25 wafers. This transportation incompatibility may lead to an unexpected capacity loss for an in-line stepper - in particular in an NPI scenario that, by nature, includes a substantial number of small-sized jobs. Such a capacity loss can be alleviated by effective scheduling. A genetic algorithm (GA) scheduling method is proposed to enhance the productivity of in-line steppers. Four other sequencing methods are compared with the GA method. Numeric experiments indicate that the GA method outperforms the four benchmarks. The higher the percentage of small-sized jobs, the better the performance of the GA.