Tabu Search Based Hybrid Meta-Heuristic Approaches for Schedule-Based Production Cost Minimization Problem for the Case of Cable Manufacturing Systems

Daneshdoost, Fereshteh; Hajiaghaei–Keshteli, Mostafa; Şahin, Ramazan; Niroomand, Sadegh

doi:10.15388/21-infor471

Cited by 21 publications

(9 citation statements)

References 71 publications

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“…It can provide more frequent and large coverage data information, including the amplitude of crustal movement, the reduction of forest coverage, and the change of carbon dioxide concentration in the air. Second, wearable sensors, including GPS and accelerometers embedded in mobile terminals and smart phones, as well as apps that can record temperature, light effect, pressure, and exercise conditions, are commonly used for fitness tracking, recording sleep and heartbeat data, and even built-in gyroscopes and accelerometers in the equipment to identify the movement direction and angle of the wearer's head [14]. The bone conduction sensor of the built-in audio system displays the distance direction and temperature.…”

Section: Construction Of Sensor Media Under the Background Of Big Datamentioning

confidence: 99%

[Retracted] Construction and Case Analysis of Sensor News Whole Chain Production Model Based on Artificial Intelligence Technology

Bolaji

2023

Journal of Sensors

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In the current era of data explosion, people’s media have access to a large amount of information through the Internet. How to generate effective and realistic news and let interested people accept it is a hot topic in the current news media industry. The continuous development of sensor technology and the application of artificial intelligence technology enable the directional push of intelligent sensor news to be realized. Intelligent sensor media uses sensor technology to monitor and collect information and then combines artificial intelligence and big data to process the information. After obtaining effective media information, it will be pushed in a targeted way and tracked for analysis. By analyzing the current situation and difficulties of sensor news, this paper seeks for an effective combination of artificial intelligence technology and sensor news and establishes a whole process directional push model for sensor news production and thermal tracking analysis. This model is pioneering and practical in the media field. In order to further verify the effectiveness of the model, the important application field of sensor news is selected as the simulation research object, and the specific selection and layout of sensors, the data analysis obtained by sensors, the push of environmental news, and the thermal tracking analysis are simulated. The results show that through data analysis, news editing, and targeted push of water pollution environmental news, the news has gained a certain degree of attention among the main design media. The popularity index is estimated based on the number of likes and comments forwarded by the current mainstream Internet media platforms (Toutiao, Tiktok, and Weibo). The environmental pollution news thermal index of the sensor reached the highest thermal index score of 12 on the 26th day after the activity. On the Weibo client, the message reached the highest heat index of 14 points on the 28th. The simulation analysis of actual cases further illustrates the advantages of this design framework.

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Section: Construction Of Sensor Media Under the Background Of Big Datamentioning

confidence: 99%

[Retracted] Construction and Case Analysis of Sensor News Whole Chain Production Model Based on Artificial Intelligence Technology

Bolaji

2023

Journal of Sensors

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“…The analysis evaluated the intelligence component and greenness of Iranian ports using data envelopment analysis (DEA) by Sadri et al [44]. In a study by Daneshdoost et al [45], they searched for a method based on hybrid meta-heuristic approaches for the minimization of cable production costs. Ghoushchi et al [46] studied the landfill site selection problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Designing Sustainable Recovery Network of End‐of‐Life Product during the COVID‐19 Pandemic: A Real and Applied Case Study

Abbasi

Daneshmand-Mehr

Kanafi

2022

Discrete Dynamics in Nature and Society

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One of the most important aspects of supply chain management (SCM) is the recovery network (RN), which covers all activities associated with return products (such as collection, recovery, repair, recycling, and waste disposal). Our goal in this paper is to provide a new mathematical model of sustainable end-of-life management (SEOLM) during the COVID-19 pandemic for readers. The suggested recovery network model (RNM) can explain the trade-offs between economic (minimizing total costs), environmental (minimizing bad environmental impacts), and social (minimizing bad social impacts) aspects during the pandemic and the great lockdown. A new RN can be designed with a sustainable and hygienic design when taking environmental, economic, and social considerations into account. It proposes guidelines for managers and scholars on how to address recovery network design (RND) challenges during the pandemic through a mathematical article with a sustainable approach. The scalarization approach of a multi-objective mixed-integer programming (MOMIP) problem in this paper is the weighted sum method. The validation of the presented model and the related Pareto frontier has been illustrated by a case study and numerical example. To perform the optimization process, Lingo software is used.

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“…Tabu search algorithm is used in many fields such as scheduling problems [33,35] and route planning [34].…”

Section: Tabu Search Algorithmmentioning

confidence: 99%

Machine Learning-Based Comparative Study For Heart Disease Prediction

Gullu

Akçayol

Barışçı

2022

Advances in Artificial Intelligence Research

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Heart disease is one of the most common causes of death globally. In this study, machine learning algorithms and models widely used in the literature to predict heart disease have been extensively compared, and a hybrid feature selection based on genetic algorithm and tabu search methods have been developed. The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module. The models have been tested using different datasets, and detailed comparisons and analysis were presented. The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

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Tabu Search Based Hybrid Meta-Heuristic Approaches for Schedule-Based Production Cost Minimization Problem for the Case of Cable Manufacturing Systems

Cited by 21 publications

References 71 publications

[Retracted] Construction and Case Analysis of Sensor News Whole Chain Production Model Based on Artificial Intelligence Technology

[Retracted] Construction and Case Analysis of Sensor News Whole Chain Production Model Based on Artificial Intelligence Technology

Designing Sustainable Recovery Network of End‐of‐Life Product during the COVID‐19 Pandemic: A Real and Applied Case Study

Machine Learning-Based Comparative Study For Heart Disease Prediction

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