Generating dispatching rules for semiconductor manufacturing to minimize weighted tardiness

Pickardt,; Branke,; Hildebrandt,; Heger, Jens; Scholz‐Reiter, Bernd

doi:10.1109/wsc.2010.5678946

Cited by 43 publications

(17 citation statements)

References 25 publications

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“…For example, Tay and Ho [135] applied GP to evolve dispatching [73] rules for flexible job shop scheduling and use the least waiting time assignment [49] to find a suitable machine to process an operation. Similarly, Pickardt et al [117] evolved dispatching rules for semiconductor manufacturing and used two existing heuristics, i.e. minimum batch size (MBS) and larger batches first (LBF), to control batch formulation.…”

Section: Component(s) To Be Evolvedmentioning

confidence: 99%

“…Nie et al [100] Pickardt et al [117] Baykasoglu et al [11] Abednego and Hendratmo [1] Nguyen et al [85] Nie et al [101] Nie et al [102] Vazquez-Rodriguez and Ochoa [136] Attribute analysis Interpretability Generalisability Jakobovi and Marasovi [58] Nguyen et al [86] Nguyen et al [87] Nie et al [103] Han et al [41] Nguyen et al [88] Nguyen et al [89] Nguyen et al [90] Nguyen et al [91] Nie et al [104] Park et al [110] Park et al [111] Pickardt et al [118] Qin et al [122] Nie et al [105] Hildebrandt and Branke [45] Hildebrandt et al [47] Hunt et al [53] Hunt et al [54] Nguyen et al [93] Nguyen et al [92] Nguyen et al [94] Nguyen et al [95] Nguyen et al [96] Park et al [112] Alsina et al [3] Belisrio and Pierreval [15] Sim and Hart [130] Branke et al [18] …”

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confidence: 99%

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Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

214

101

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Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature. The flexibility of genetic programming also allows it to discover very sophisticated heuristics to deal with complex and dynamic production environments. However, as compared to other applications of genetic programming or scheduling applications of other evolutionary computation techniques, the configurations and requirements of genetic programming for production scheduling are more complicated. In this paper, a unified framework for automated design of production scheduling heuristics with genetic programming is developed. The goal of the framework is to provide the researchers with the overall picture of how genetic programming can be applied for this task and the key components. The framework is also used to facilitate our discussions and analyses of existing studies in the field. Finally, this paper shows how knowledge from machine learning and operations research can be employed and how the current challenges can be addressed.

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Section: Component(s) To Be Evolvedmentioning

confidence: 99%

mentioning

confidence: 99%

Genetic programming for production scheduling: a survey with a unified framework

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

214

101

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“…These are usually metaheuristics such as Evolutionary Algorithms (EAs) that search over a space of heuristics. Various papers have shown that in particular Genetic Programming (GP) can be successfully used to generate dispatching rules for scheduling scenarios that significantly out-perform manually developed benchmark rules , Pickardt et al 2010, Pickardt et al 2013, Geiger et al 2006.…”

Section: Introductionmentioning

confidence: 99%

Evolving control rules for a dual-constrained job scheduling scenario

Branke

Groves

Hildebrandt³

2016

2016 Winter Simulation Conference (WSC)

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Dispatching rules are often used for scheduling in semiconductor manufacturing due to the complexity and stochasticity of the problem. In the past, simulation-based Genetic Programming has been shown to be a powerful tool to automate the time-consuming and expensive process of designing such rules. However, the scheduling problems considered were usually only constrained by the capacity of the machines. In this paper, we extend this idea to dual-constrained flow shop scheduling, with machines and operators for loading and unloading to be scheduled simultaneously. We show empirically on a small test problem with parallel workstations, re-entrant flows and dynamic stochastic job arrival that the approach is able to generate dispatching rules that perform significantly better than benchmark rules from the literature.

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“…In recent years, Genetic Programming (GP) gained popularity as an effective optimization technique [1], and its capabilities of automatically uncovering hidden relationships in datasets and producing rules to control complex systems haves been proved in several real-world applications [2] [3].…”

Section: Introductionmentioning

confidence: 99%

Learning Dynamical Systems Using Standard Symbolic Regression

Gaucel¹,

Keijzer²,

Lutton³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Symbolic regression has many successful applications in learning free-form regular equations from data. Trying to apply the same approach to differential equations is the logical next step: so far, however, results have not matched the quality obtained with regular equations, mainly due to additional constraints and dependencies between variables that make the problem extremely hard to tackle. In this paper we propose a new approach to dynamic systems learning. Symbolic regression is used to obtain a set of first-order Eulerian approximations of differential equations, and mathematical properties of the approximation are then exploited to reconstruct the original differential equations. Advantages of this technique include the de-coupling of systems of differential equations, that can now be learned independently; the possibility of exploiting established techniques for standard symbolic regression, after trivial operations on the original dataset; and the substantial reduction of computational effort, when compared to existing ad-hoc solutions for the same purpose. Experimental results show the efficacy of the proposed approach on an instance of the Lotka-Volterra model.

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Generating dispatching rules for semiconductor manufacturing to minimize weighted tardiness

Cited by 43 publications

References 25 publications

Genetic programming for production scheduling: a survey with a unified framework

Genetic programming for production scheduling: a survey with a unified framework

Evolving control rules for a dual-constrained job scheduling scenario

Learning Dynamical Systems Using Standard Symbolic Regression

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