A random forest-based job shop rescheduling decision model with machine failures

Zhao, Meng; Gao, Liang; Li, Xinyu

doi:10.1007/s12652-019-01574-x

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…Simultaneously, the method avoids errors of bias and variance by random choosing of input-predictor variables with the application of various subsets of the related training dataset (Ren et al 2019;Lee and Moon 2018). Classification problem involves the forest, ascertaining the classification with the highest votes above total trees in the forest (Zhao et al 2019). Furthermore, the algorithm has also been widely used for a wide variety of wetland classification, and mapping natural coastal salt marsh vegetation environments (Tian et al 2016;Van Beijma and Comber 2014;Corcoran et al 2013).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

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Technology play a substantial function in exemplifying technical potentials that are unconsidered in the past to include intelligent behaviour. Computers in machine learning involve the modelling of intelligent behaviour with the least human mediation. Computational intelligence signifies a set of vital information processing methods for information managing and decision accomplishing. Major critical trials in technical exploration is the supervision of data obtained from various resources. Presently, Scanning Electron Microscopy (SEM) images are categorized as per user's measures, which greatly restrict misuse and reliability, owing for the need in definite classification algorithm. The foremost objective is to evaluate the exactness with each algorithm in terms of performance measures such as accuracy, precision, sensitivity, F-measure, kappa, computational time and confusion matrix. In this work, a recital assessment among three classification algorithms on SEM image dataset was conducted. Experimental results revealed that the Random forest algorithm gives the highest accuracy comparing to other two approach. Hence, the researchers would be able to perform automatic SEM image classification evading the necessity to categorize image and for providing a searchable database to identify a definite group of images.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Muthu

Kirubha

2020

J Ambient Intell Human Comput

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“…Their findings suggested that ensemble approaches may be more useful for credit risk analysis as random forests and gradient-boosting machines have stronger predictive power than decision trees. In a separate study, [6] assessed the effectiveness of various machine learning classifiers for analyzing German credit risk, including Support Vector Machines, Logistic regression, Decision trees, Random forests, and Neural networks. The maximum accuracy as well as recall was obtained in random forests, suggesting that it would be the most effective classifier for this task.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Classifiers for Credit Risk Analysis

Sudiksha,

Nanjundan,

George

2024

EAI Endorsed Trans IoT

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The modern world is a place of global commerce. Since globalization became popular, entrepreneurs of small and medium enterprises to large ones have looked up to banks, which have existed in various forms since antiquity, as their pillars of support. The risk of granting loans in various forms has significantly increased as a consequence of this, the businesses face financing difficulties. Credit Risk Analysis is a major aspect of approving the loan application that is done by analyzing different types of data. The goal is to minimize the risk of approving the loan for the Individuals or businesses who might not pay back on time. This research paper addresses this challenge by applying various machine learning classifiers to the German credit risk dataset. By evaluating and comparing the accuracy of these models to identify the most effective classifier for credit risk analysis. Furthermore, it proposes a contributory approach that combines the strengths of multiple classifiers to enhance the decision-making process for loan approvals. By leveraging ensemble learning techniques, such as the Voting Ensemble model, the aim is to improve the accuracy and reliability of credit risk analysis. Additionally, it explores tailored feature engineering techniques that focus on selecting and engineering informative features specific to credit risk analysis.

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“…Another machine learning technique that combines several decision trees is random forest (RF). The authors in [20] started by generating and processing data samples of machine failures, then designed the RF-based rescheduling model that would decide which rescheduling strategy has to be made (no rescheduling, right-shift rescheduling or total rescheduling). In [21], a comparison between several machine learning techniques was made.…”

Section: Job Shop Scheduling Using Artificial Intelligencementioning

confidence: 99%

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021

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The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

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A random forest-based job shop rescheduling decision model with machine failures

Cited by 6 publications

References 34 publications

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

RETRACTED ARTICLE: Computational intelligence on image classification methods for microscopic image data

Machine Learning Classifiers for Credit Risk Analysis

A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

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