A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers

Ventura, José A.; Yoon, Suk-Hun

doi:10.1007/s10845-012-0650-9

Cited by 48 publications

(16 citation statements)

References 29 publications

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“…Certain orders are better, whereas others are worse. This issue should be studied relative to lot streaming in the hybrid flow shop scheduling problem, which considers splitting numbers as a decision variable for shortening flow time [14,21,49]. …”

Section: Scenario Analysismentioning

confidence: 99%

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Lin

Chen

2015

Simulation Modelling Practice and Theory

100

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Section: Scenario Analysismentioning

confidence: 99%

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Lin

Chen

2015

Simulation Modelling Practice and Theory

100

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“…And the related parameters of FSVM are also critical for assuring the recognition accuracy. The parameters needed to be optimized include the degree of polynomial kernel d, the width of Gaussian kernel γ , the combination coefficient of hybrid kernel β and the penalty factor of FSVM C. As a stochastic optimization algorithm simulating the natural selection and genetic mechanism in the process of biological evolution, GA has excellent global searching ability and has been widely used to solve optimization problems (Whitley 1994;Ventura and Yoon 2013;Ahmed et al 2014;Takeyasu and Kainosho 2014). Consequently, it is employed to achieve parameters optimization and input feature choice for FSVM-based classifiers.…”

Section: Optimizing the Input Feature Set And Parameters Of Fsvm Usinmentioning

confidence: 99%

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

2015

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Accurate control chart patterns recognition (CCPR) plays an essential role in the implementation of control charts. However, it is a challenging problem since nonrandom control chart patterns (CCPs) are normally distorted by "common process variations". In this paper, a novel method of CCPR by integrating fuzzy support vector machine (SVM) with hybrid kernel function and genetic algorithm (GA) is proposed. Firstly, two shape features and two statistical features that do not depend on the distribution parameters and number of samples are presented to explicitly describe the characteristics of CCPs. Then, a novel multiclass method based on fuzzy SVM with a hybrid kernel function is proposed. In this method, the influence of outliers on classification accuracy of SVM-based classifiers is weakened by assigning a degree of membership for every training sample. Meanwhile, a hybrid kernel function combining Gaussian kernel and polynomial kernel is adopted to further enhance the generalization ability of the classifiers. To solve the issue of features selection and parameters optimization, GA is used to simultaneously optimize the input features subsets and parameters of fuzzy SVM-based classifier. Finally, several simulation experiments and a real example are addressed to validate the feasibility and effectiveness of the proposed methodology. And the results of simulation experiments demonstrate that it can achieve excellent performance for CCPR and outperforms other approaches, such as learning B Pingyu Jiang vector quantization network, multi-layer perceptron network, probability neural network, fuzzy clustering and SVM, in term of recognition accuracy. The results of the practical cases manifest that the proposed method has application potential for solving the problem of control chart interpretation in real-world.

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“…Chang et al, 2007;Luo et al, 2009;Damodaran et al, 2009;Ventura and Yoon, 2013;Li et al, 2014;Chang and Liu, 2015 This scheme of GA has been implemented using both codi cations presented in the previous section: permutation and binary codi cation denoted GA(P) and GA(B) respectively . Both algorithms are identical, except with respect to the tness computation, since the way to calculate the owtime is simpler in the binary codi cation than in the permutation codi cation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Single machine interfering jobs problem with flowtime objective

González

2015

J Intell Manuf

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)>IJH=?J Interfering jobs problems (or multi agents scheduling problems) are an emergent topic in the scheduling literature. In these decision problems, two or more sets of jobs have to be scheduled, each one with its own criteria. More speci cally, we focus on a problem in which jobs belonging to two sets have to be scheduled in a single machine in order to minimize the total owtime of the jobs in one set, while the total owtime of the jobs in the other set should not exceed a given constant ϵ. This problem is known to be weakly NP-hard, and a Dynamic Programming (DP) algorithm has been proposed to nd optimal solutions.In this paper, we rst analyse the distribution of solutions of the problem in order to establish its empirical hardness. Next, a novel encoding scheme and a set of properties associated to the neighbourhood of this scheme are presented. These properties are used to develop both exact and approximate methods, i.e. a branch and bound (B&B) method, several constructive heuristics, and di erent versions of a genetic algorithm (GA). The computational experience carried out shows that the proposed B&B is more e cient than the existing DP algorithm. The results also show the advantages of the proposed encoding scheme, as the approximate methods yield close-to-optimum solutions for big-sized instances where exact methods are not feasible.Scheduling interfering jobs two-agent scheduling problem total owtime single machine

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A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers

Cited by 48 publications

References 29 publications

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

Single machine interfering jobs problem with flowtime objective

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