Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool

Nakasuka, Shinichi; Yoshida, Taketoshi

doi:10.1080/00207549208942903

Cited by 88 publications

(27 citation statements)

References 24 publications

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“…From a recent survey of existing FMS scheduling procedures (Rachamadugu and Stecke 1994) and of existing expert systems in scheduling (Kusiak 1990), the concept ofapplying machine learning to obtain knowledge for scheduling is not new. However, research on this issue either involves too many human cognitions (Yih 1992) or is restricted on the pattern classification capability (e.g., Nakasuka andYoshida 1992 andShaw et al 1992).…”

Section: Introductionmentioning

confidence: 99%

Intelligent scheduling of FMSs with inductive learning capability using neural networks

Wang¹,

Chen²,

Liu³

1995

Int J Flex Manuf Syst

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Abstract. With the growing uncertainty and complexity in the manul~acturing environment, most scheduling problems have been proven to be NP-complete and this can degrade the performance of conventional operations research (OR) techniques~ This article presents a system-attribute-oriented knowledge-based scheduling system (SAOSS) with inductive learning capability. With the rich heritage from artificial intelligence (AI), SAOSS takes a multialgorithm paradigm which makes it more intelligent, flexible, and suitable than others for tackling complicated, dynamic scheduling problems.SAOSS employs an efficient and effective inductive learning method, a continuous iterative dichotomister 3 (CID3) algorithm, to induce decision rules for scheduling by converting corresponding decision frees into hidden layers of a self-ger~rated neural network. Connection weights between hidden units imply the schethling heuristics, which are then formulated into scheduling rules. An FMS scheduling problem is also given for illustration. The scheduling results show that the system-attribute-oriented knowledge-based approach is capable of addressing dynamic scheduling problems.

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Section: Introductionmentioning

confidence: 99%

Intelligent scheduling of FMSs with inductive learning capability using neural networks

Wang¹,

Chen²,

Liu³

1995

Int J Flex Manuf Syst

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“…Using the latter, which belongs to the field of Artificial Intelligence, a set of the system's earlier simulations (training examples) are used to determine the best rule for each of the system's possible states. This knowledge is then used to make intelligent decisions in real time (see for example, [12], [9]). …”

Section: Introductionmentioning

confidence: 99%

Learning-based scheduling in a job shop

Priore¹,

Fuente²

1999

Elektrotech. Inftech.

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A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the test in all the possible states that the system may be in. The system's state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules.

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“…0956-5515 © 1996 Chapman & Hall depends on knowledge engineers. Therefore knowledge acquisition techniques for scheduling expert systems have been studied (Kawashima and Komoda, 1991;Nakasuka and Yoshida, 1992;Ikkai et al, 1994;Pesch, 1994). In these techniques, knowledge is acquired from training data generated by domain experts, such as past logs of planning, interviews and so on.…”

Section: Introductionmentioning

confidence: 99%

Recursive learning method for knowledge-based planning system

1996

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In the status selection planning system, which is a kind of knowledge-based planning system, the quality of the solution depends on the status selection rules. However, it is usually difficult to acquire useful knowledge from human experts. The learning method of a status selection rule using inductive learning is proposed. The status selection rules are divided into several stages according to the planning process. Gathering a training set and learning a part of the knowledge inductively are repeated one by one from the previous stage rules. From the result of application to a job-shop problem, the effectiveness of the proposed method is shown.

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Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool

Cited by 88 publications

References 24 publications

Intelligent scheduling of FMSs with inductive learning capability using neural networks

Intelligent scheduling of FMSs with inductive learning capability using neural networks

Learning-based scheduling in a job shop

Recursive learning method for knowledge-based planning system

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