A robust scheduling rule using a Neural Network in dynamically changing job-shop environments

Eguchi, Toru; Oba, Fumiyasu; Toyooka, Satoru

doi:10.1504/ijmtm.2008.017727

Cited by 18 publications

(24 citation statements)

References 13 publications

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“…Supervised learning techniques such as decision tree [107,127], logistic regression [57], support vector machines [129], and artificial neural networks [32,138] have also been investigated in the literature for automated design of production scheduling heuristics. For supervised learning, optimal decisions from solving small instances with exact optimisation methods or from the historical data are needed to build the training set.…”

Section: Machine Learningmentioning

confidence: 99%

“…As compared to other hyper-heuristics based on supervised learning such as decision tree [107,127], logistic regression [57], support vector machine [129], and artificial neural networks [32,138], genetic programming (GP) has shown a number of key advantages. First, GP has flexible representations which allow various heuristics to be represented as different computer programs.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

205

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: Machine Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Nguyen

Mei

Zhang

2017

Complex Intell. Syst.

205

101

View full text Add to dashboard Cite

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“…Shop configuration Single machine [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] Parallel machines [34] Job shop [26], [31], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56] Flexible job shop [57], [58], [59], [60], [61], [62], [63], [64], [65] Flow shop [25], [66], [67] Special processing Sequence-dependent setups …”

Section: Problem Class Referencesmentioning

confidence: 99%

“…Parametric representation [21], [38], [42], [46] [35], [36], [41], [45], [47], [52], [57], [58], [59], [67] Grammar-based representation [24], [30], [39] [22], [23], [25], [26], [27], [28], [29], [31], [32], [33], [34], [37], [40], [43], [44], [48], [49], [50], [51], [52], [53], [54], [55], [56], [60], [61], [62], [63], [64], [65], [66] The next section (III-A) discusses the two types of learning methods used within hyper-heuristics, followed by a discussion of the selection of attributes to be provided to the hyperheuristic in Section III-B. The different representations of priority functions are presented in Section III-C together with suitable optimisation algorithms as they are closely tied to the chosen representation.…”

Section: Supervised Learningmentioning

confidence: 99%

Automated Design of Production Scheduling Heuristics: A Review

Branke

Nguyen

Pickardt

et al. 2016

IEEE Trans. Evol. Computat.

320

116

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“…This cost is a function of critical ratio defined as ratio of due date and total processing time. Eguchi et al (2008) developed a robust and effective scheduling rule using a neural network (NN) which is considered as a priority rule for the complex and dynamic job-shops. Papakostas and Chryssolouris (2009) adopted a new scheduling policy called RTSLACK for improving tardiness and found to be superior as compared to EDD, SPT and SLACK.…”

Section: Priority Rule For Schedulingmentioning

confidence: 99%

Multi-objective scheduling in an agent based Holonic manufacturing system

Jana¹,

Bairagi²,

Paul³

et al. 2013

10.5267/j.dsl

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The present paper is aimed at multi-objective scheduling in an agent based holonic manufacturing system to satisfy the goal of several communities namely the product, the resource, and the organization simultaneously. In this attempt, first a multi criteria based priority rule is developed following Simple Additive Weight (SAW) method under Multi Criteria Decision Making (MCDM) environment to rank the products. Accordingly, the products are allowed to select a particular resource for execution by negotiation considering minimum time as criterion. The interests of different communities are accomplished by allocating the ordered rank of products to the ordered rank of resources. Conflict, if arises between products and resources, are resolved by introducing the concept of Early Finish Time (EFT) as criterion for task allocation. A scheduling algorithm is proposed for execution of the rule. In view of machine failure, a cooperation strategy is evolved that also optimizes reallocation of the incomplete task. It is concluded that the proposed scheduling algorithm together with the disturbance handling algorithm are poised to satisfy the agent's local objective as well as organization's global objective concurrently and are commensurable with multi agent paradigm.

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A robust scheduling rule using a Neural Network in dynamically changing job-shop environments

Cited by 18 publications

References 13 publications

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

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

Automated Design of Production Scheduling Heuristics: A Review

Multi-objective scheduling in an agent based Holonic manufacturing system

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