Minimizing the resource consumption of heterogeneous batch-processing machines using a copula-based estimation of distribution algorithm

Liu, Chuang; Xu, Rui; Wang, Yu

doi:10.1016/j.asoc.2018.08.036

Cited by 7 publications

(2 citation statements)

References 55 publications

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“…EDAs have also been applied to solve problems in practical settings. For example, an EDA was proposed in [40] to solve problems involving various types of constraints, while EDAs were applied in [41,42] to solve flow-shop scheduling problems.…”

Section: B Copulasmentioning

confidence: 99%

An Estimation of Distribution Algorithm With Filtering and Learning

Tang

Song

Liu

et al. 2021

IEEE Trans. Automat. Sci. Eng.

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Estimation of distribution algorithm (EDA) is an efficient population-based stochastic search technique. Since it was proposed, many attempts have been made to improve its performance in the context of non-linear continuous optimization. However, the success of EDA depends on the accuracy of modeling, the effectiveness of sampling and the ability of exploration. An effective EDA often needs to take some measures to adjust the model and to guide sampling. In this paper, we propose a novel estimation of distribution algorithm which applies the idea of Kalman filtering to revise the modeling data and a learning strategy to improve sampling. The filtering scheme modifies the modeling data set using an estimation error matrix based on historic solution data. During the sampling process, the learning strategy determines the region to sample next based on the sampling outcomes so far, instead of completely random sampling. The proposed EDA also employs a multivariate probabilistic model based on copula function and can quickly reach the promising area in which the optimal solution is likely to be located. A collection of general benchmark functions are used to test the performance of the proposed algorithm. Computational experiments show that the EDA is effective.

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Section: B Copulasmentioning

confidence: 99%

An Estimation of Distribution Algorithm With Filtering and Learning

Tang

Song

Liu

et al. 2021

IEEE Trans. Automat. Sci. Eng.

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“…Potts and Kovalyov (2000) and Sun et al (2011) reviewed this subject. The batch scheduling problem in a flow shop environment with two stages was discussed by Tang and Liu (2009a, b), Behnamian et al (2012), Wang et al (2012), andLiu et al (2018). Batching problems in systems with more than two stages were also studied by Salmasi et al (2011), Damodaran et al (2013), Li et al (2015), and Matin et al (2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Production scheduling problem in a factory of automobile component primer painting

Singgih

Kim

et al. 2020

J Intell Manuf

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In a factory of automobile component primer painting, various automobile parts are attached to overhead hangers in a conveyor line and undergo a series of coating processes. Thereafter, the components are wrapped at a packaging station. The packaging process should be fully balanced by an appropriate sequence of components to prevent the bottleneck effect because each component requires different packaging times and materials. An overhead hanger has a capacity limit and can hold varying numbers of components depending on the component type. Capacity loss can occur if the hanger capacity is not fully utilized. To increase hanger utilization, companies sometimes mix two or more component types on the same hangers, and these hangers are called mixed hangers. However, mixed hangers generally cause heavy workload because different items require additional setup times during hanging and packing processes. Hence, having many mixed hangers is not recommended. A good production schedule requires a small number of mixed hangers and maximizes hanger utilization and packaging workload balance. We show that the scheduling problem is NP-hard and develop a mathematical programming model and efficient solution approaches for the problem. When applying the methods to solve real problems, we also use an initial solution-generating method that minimizes the mixing cost, set a rule for hanging the items on hangers considering eligibility constraint, and decrease the size of tabu list in proportion to the remaining computational time for assuring intensification in the final iterations of the search. Experimental results demonstrate the effectiveness of the proposed approaches.

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