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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
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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.
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
;
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 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.
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
;
工業工程與管理學系
;
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 GA methodology for the scheduling of yarn-dyed textile production
(2009-12-01)
Hsu, Hsi-Mei
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Hsiung, Yai
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Chen, Ying-Zhi
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Wu, Muh-Cherng
;
工業工程與管理學系
This paper presents a scheduling approach for yarn-dyed textile manufacturing. The scheduling problem is distinct in having four characteristics: multi-stage production, sequence-dependent setup times, hierarchical product structure, and group-delivery (a group of jobs pertaining to a particular customer order must be delivered together), which are seldom addressed as a whole in literature. The scheduling objective is to minimize the total tardiness of customer orders. The problem is formulated as a mixed integer programming (MIP) model, which is computationally extensive. To reduce the problem complexity, we decomposed the scheduling problem into a sequence of sub-problems. Each sub-problem is solved by a genetic algorithm (CA), and an iteration of solving the whole sequence of sub-problems is repeated until a satisfactory Solution has been obtained. Numerical experiment results indicated that the proposed approach significantly outperforms the EDD (earliest due date) scheduling method-currently used in the yarn-dyed textile industry. (C) 2009 Elsevier Ltd. All rights reserved.

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