Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems

Pickardt, Christoph W.; Hildebrandt, Torsten; Branke, Jürgen; Heger, Jens; Scholz‐Reiter, Bernd

doi:10.1016/j.ijpe.2012.10.016

Cited by 140 publications

(95 citation statements)

References 46 publications

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“…utilisation level). Although some special cases are considered in the literature such as batching [37,118], machine breakdowns [142], and unrelated parallel machines [31], these are very limited. In addition, most scheduling problems handled by GP are dynamic problems where jobs will arrive randomly over time and their information is only available upon their arrivals.…”

Section: Production Scheduling Problemsmentioning

confidence: 99%

“…Although this model is relatively simple, it can reflect important characteristics of job shops (which is suitable for studies on scheduling decisions) and its scale is reasonable for evaluation purpose. More complex simulation models such as simulation models of semi-conductor production systems [118] have also been used to evaluate scheduling heuristics.…”

Section: Evaluation Modelsmentioning

confidence: 99%

“…Pickardt et al [118] proposed a two-stage approach to evolve dispatching rule sets for semiconductor manufacturing. In the first stage, GP is used to evolve general dispatching rules.…”

Section: Other Search Mechanismsmentioning

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] …”

mentioning

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: Production Scheduling Problemsmentioning

confidence: 99%

Section: Evaluation Modelsmentioning

confidence: 99%

“…Pickardt et al [118] proposed a two-stage approach to evolve dispatching rule sets for semiconductor manufacturing. In the first stage, GP is used to evolve general dispatching rules.…”

Section: Other Search Mechanismsmentioning

confidence: 99%

mentioning

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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“…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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A New Representation in Genetic Programming for Evolving Dispatching Rules for Dynamic Flexible Job Shop Scheduling

Zhang¹,

Mei²,

Zhang³

2019

Lecture Notes in Computer Science

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Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems

Cited by 140 publications

References 46 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

A New Representation in Genetic Programming for Evolving Dispatching Rules for Dynamic Flexible Job Shop Scheduling

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