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Author: search.filters.author.Wu, Muh-CherngSubject: search.filters.subject.Chromosome representation

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    Merits of using chromosome representations and shadow chromosomes in genetic algorithms for solving scheduling problems
    (2019-08-01)
    Lin, Chi-Shiuan
    ;
    Lee, I-Ling
    ;
    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.
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    A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems
    (2018-01-01)
    Lu, Po-Hsiang
    ;
    Wu, Muh-Cherng
    ;
    Tan, Hao
    ;
    Peng, Yong-Han
    ;
    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.
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    Effects of different chromosome representations in developing genetic algorithms to solve DFJS scheduling problems
    (2017-04)
    Wu, Muh-Cherng
    ;
    Lin, Chi-Shiuan
    ;
    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.
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    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
    ;
    Li, Yi-Hsun
    ;
    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.