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Optimisation of job shop scheduling problem using genetic algorithm and simulated annealing: a case study of manufacturing industry

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第3期16卷 883-892页

作者： Phanden, Rakesh Kumar Gupta, Shrajal Wolde, Biruk Kumar, Ravinder Chakraborty, Ayon Federat Univ Australia POB 663 Ballarat Vic 3353 Australia Sat Kabir Inst Technol & Management VPO Ladrawan Bahadurgarh 124507 Haryana India Inst Hlth & Management Level 2-187 Boundary Rd North Melbourne Vic 3051 Australia Jagan Inst Management Studies Rohini Sec 5 Delhi 110085 India Arba Minch Univ Arba Minch Ethiopia Amity Univ Uttar Pradesh Dept Mech Engn Noida India

Production scheduling is an important activity within the manufacturing system to improve its performance. It is a process of assigning resources to the task or vice versa, which depends upon the configuration of the shop floor and the type of products to be manufactured. In job shops, scheduling is a very complex task since it involves a variety of products to process on a limited number of machines to cut down on the amount of time it takes to do tasks. In the present work, a case study from the manufacturing industry has been taken to maximise the amount of time it takes to do tasks (i.e., makespan) having job shop configuration. Two distinguished nature-inspired algorithms, viz Simulated annealing (SA) and genetic algorithm (GA), have been pragmatic in optimising the existing schedule. The results show that GA outperform the SA by a 1.76% increment in the makespan value. Also, the GA and SA possessed better results than the company's existing production schedule by 32.23 and 31.02%, respectively.

关键词： Production scheduling Job shop scheduling Simulated annealing and genetic algorithm Case study

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Demand analysis of flood insurance by using logistic regression model and genetic algorithm

Demand analysis of flood insurance by using logistic regress...

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International Conference on Operations Research of the Indonesian-Operations-Research-Association (IORA)

作者： Sidi, P. Mamat, M. B. Sukono Supian, S. Putra, A. S. Univ Terbuka Fac Math & Nat Sci Dept Math South Tangerang Indonesia Univ Sultan Zainal Abidin Fac Informat & Comp Kuala Terengganu Malaysia Univ Padjadjaran Fac Math & Nat Sci Dept Math Sumedang Indonesia Univ Padjadjaran Fac Math & Nat Sci Dept Geophysiscs Sumedang Indonesia

Citarum River floods in the area of South Bandung Indonesia, often resulting damage to some buildings belonging to the people living in the vicinity. One effort to alleviate the risk of building damage is to have flood insurance. The main obstacle is not all people in the Citarum basin decide to buy flood insurance. In this paper, we intend to analyse the decision to buy flood insurance. It is assumed that there are eight variables that influence the decision of purchasing flood assurance, include: income level, education level, house distance with river, building election with road, flood frequency experience, flood prediction, perception on insurance company, and perception towards government effort in handling flood. The analysis was done by using logistic regression model, and to estimate model parameters, it is done with genetic algorithm. The results of the analysis shows that eight variables analysed significantly influence the demand of flood insurance. These results are expected to be considered for insurance companies, to influence the decision of the community to be willing to buy flood insurance.

关键词： Citarum River building damage floods insurance logistic regression and genetic algorithm

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Optimization of fermentation medium for nisin production from Lactococcus lactis *** using support vector regression(SVR) and genetic algorithm(GA)

Optimization of fermentation medium for nisin production fro...

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21st IUBMB and 12th FAOBMB International Congress of Biochemistry and Molecular Biology

作者： Guo Welllang Wang Hao Yang Shuang Meng Qingfan College of Life Science Jilin University

A sequential optimization strategy was applied for optimizing the fermentation medium to enhance nisin ***-Burman design(PBD) were applied for screening the significant components of the fermentation medium. Then,the path of Steepest ascent method(PSA) was employed for get the suitable range of the significant *** Box-Behnken design

关键词： and genetic algorithm Optimization of fermentation medium for nisin production from Lactococcus lactis subsp.lactis using support vector regression SVR

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Water Uptake of HDPE Reinforced with Washingtonia Fibre Biocomposites: Mathematical Modelling using Artificial Neural Network, Response Surface Methodology and genetic algorithm

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ADVANCES IN MATERIALS AND PROCESSING TECHNOLOGIES 2024年第3期10卷 1627-1650页

作者： Belaadi, Ahmed Lekrine, Abdelaziz Boumaaza, Messaouda Alshahrani, Hassan Bourchak, Mostefa Juhany, Khalid Damiri, Fouad Suyambulingam, Indran Siengchin, Suchart Univ 20 Aout 1955 Skikda Fac Technol Dept Genie Mecan Skikda Algeria Univ 8 Mai 1945 Guelma Lab LGCH Guelma BP Algeria Najran Univ Coll Engn Dept Mech Engn Najran Saudi Arabia Najran Univ Sci & Engn Res Ctr Deanship Sci Res Najran Saudi Arabia King Abdulaziz Univ Aerosp Engn Dept Jeddah Saudi Arabia Univ Hassan II Casablanca Fac Sci Ben MSick Dept Chem Lab Biomol & Organ Synth BIOSYNTHO Casablanca Morocco King Mongkuts Univ Technol North Bangkok KMUTNB Sirindhorn Int Thai German Sch Engn TGGS Dept Mat & Prod Engn Nat Composites Res Grp Lab Bangkok Thailand

To research the mechanisms of diffusion and water uptake kinetics biocomposites, various HDPE materials reinforced by varying quantities of Washingtonia filifera (WF) fibres (10 to 30 wt%) were submerged at room temperature in distilled water. To optimise the immersion time and WF fibre content in HDPE/WF biocomposite water uptake, an artificial neural network, the response surface methodology and the genetic algorithm were employed. In this research, the CCD model of RSM was utilised to carry out test design, modelling, and optimisation. It was found that the way water absorbs liquids follows the Fickian diffusion mode. The outcomes demonstrate that the incorporation of WF fibres into the HDPE matrix decreased the diffusivity. The ANOVA determined the relative significance of each variable and demonstrated the model's validity by demonstrating a high correlation between the observed data. Moreover, the results showed that the ANN models have training, test and validation correlation coefficients for water absorption prediction of 0.9955, 0.9915 and 0.9999, respectively. RSM-GA revealed that a 10% fibre content and a one-hour immersion duration produced the lowest levels of absorption. Additionally, a model that is very suitable for predicting biocomposites water uptake and is applicable to a variety of industrial applications is developed.

关键词： Water Uptake natural fibre HDPE binder ANN RSM and genetic algorithm

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Improving decision effectiveness of artificial neural networks: A modified genetic algorithm approach

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DECISION SCIENCES 2003年第3期34卷 421-442页

作者： Sexton, RS Sriram, RS Etheridge, H Georgia State Univ Sch Accountancy Atlanta GA 30303 USA SW Missouri State Univ Comp Informat Syst Springfield MO 65804 USA Univ SW Louisiana Dept Accounting Lafayette LA 70504 USA

This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a training method to improve generalizability and to identify relevant inputs in a neural network (NN) model. Generalizability refers to the NN model's ability to perform well on exemplars (observations) that were not used during training (out-of-sample);improved generalizability enhances NN's acceptability as a valid decision-support tool. The MGA improves generalizability by setting unnecessary weights (or connections) to zero and by eliminating these weights. Because the eliminated weights have no further impact on the training (in-sample or out-of-sample data), the relevant variables can be identified from the model. By eliminating unnecessary weights, the MGA is able to search and find a parsimonious model that generalizes well. Unlike the traditional NN, the MGA identifies the model variables that contribute to an outcome, helping decision makers to rationalize output and accept results with greater confidence. The study uses real-life data to demonstrate the use of MGA.

关键词： artificial intelligence decision support financial distress and genetic algorithm

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PHGA: Proposed Hybrid genetic algorithm for Feature Selection in Binary Classification 9

PHGA: Proposed Hybrid Genetic Algorithm for Feature Selectio...

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9th International Conference on Information and Knowledge Technology (IKT)

作者： Khiabani, Aida Sabbaghi, Arash Islamic Azad Univ Dept Comp & Informat Technol Qazvin Branch Qazvin Iran Islamic Azad Univ Dept Comp Engn Semnan Branch Semnan Iran

ISBN: (纸本)9781538625477

In data pre-processing, feature selection has particular importance. Selection of appropriate features in classification leads to accuracy enhancement, reduction of execution time and increment of model interpretability. In this paper, an innovative algorithm for feature selection is proposed which combines filter and wrapper techniques and uses benefits of evolutionary computation and assembly of independent measures. Experimental results show the superiority of the proposed hybrid genetic algorithm for feature selection in binary classification (PHGA). In comparison with other existing methods PHGA can select the smaller set of features in a shorter execution time with higher classification accuracy.

关键词： Data mining data pre-processing feature selection feature ranking filtering wrapping assembly of independent measures evolutionary computation and genetic algorithm

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Optimization of barbell trajectory during the snatch lift technique by using genetic algorithm

Optimization of barbell trajectory during the snatch lift te...

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5th IASTED International Conference on Biomechanics

作者： Nejadian, Shahram Lenjan Rostami, Mostafa Amirkarbir Univ Technol Fac Biomed Engn Tehran Iran

ISBN: (纸本)9780889866836

Mathematical modeling and optimization of snatch technique based on dynamic synthesis is the main purpose of this study. The barbell trajectory is proposed as the main index which was evaluated experimentally by several researchers who introduced optimum trajectory according to percentage of their owners' success. We believe that in optimizing the barbell trajectory we should consider the mechanical principles. Therefore we use a five-link biomechanical model to evaluate its behaviour and to predict the optimum barbell trajectory which minimizes the specific mechanical criterion by using genetic algorithm (GA) which is theoretically and empirically proven to provide a robust search in complex spaces, thereby offering a valid approach to problems requiring efficient and effective searches. Comparing the results of this model and experimental observations of other researchers, we show an improvement to introduce a good predictive model. Using this model can help the coaches to improve the performance of weightlifters.

关键词： optimization sport biomechanics weightlifting and genetic algorithm

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A genetic algorithm on multi-sensor networks lifetime optimization

A genetic algorithm on multi-sensor networks lifetime optimi...

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1st International Conference on Wireless algorithms, Systems and Applications

作者： Pan, Yantao Peng, Wei Lu, Xicheng Natl Univ Def Technol Sch Comp Changsha Peoples R China

ISBN: (纸本)3540371893

Since data communications consume the most energy of sensor networks, it is reasonable to take efficient traffic balancing to prolong the lifetime. In addition, the traffic aggregation is a main characteristic that distinguish sensor networks from others e.g. Internet and MANET. Therefore, an optimal traffic distribution will maximize the network lifetime. Furthermore, sensor networks are generally developed for special applications. If different sensor networks deployed in the same region can cooperate with each other in data transmission, their lifetimes can be improved remarkably. In this paper, we propose a genetic algorithm to achieve optimal traffic distribution on multi-sensor networks and show its efficiency by experiments.

关键词： sensor networks lifetime optimization and genetic algorithm

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Optimal design of system reliability using interval programming and genetic algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 1996年第1-2期31卷 237-240页

作者： Gen, M Cheng, RW Department of Industrial & Systems Engineering Ashikaga Institute of Technology Ashikaga 326 Japan

In this paper, we examine the optimal design problem of system reliability with uncertain coefficients and formulate it as an interval programming model. genetic algorithms are applied to the problem. The basic idea of the proposed method is first to transform the interval programming model into an equivalent bicriteria programming model and then to find the Pareto solutions of the bicriteria programming problem using genetic algorithms. Numerical examples are given to demonstrate the efficiency of the proposed approach.

关键词： reliability optimization interval programming bicriteria programming and genetic algorithm

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Multi-objective preventive maintenance strategy and optimization considering unavailability and cost: A case study on VOBC

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ALEXANDRIA ENGINEERING JOURNAL 2024年 105卷 567-577页

作者： Peng, Cong Wei, Shangguan Peng, Jiali Wang, Zongyao Chai, Linguo Cai, Baigen Xing, Yulong Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China China Acad Railway Sci Corp Ltd Stand & Metrol Res Inst Beijing 100081 Peoples R China China Railway Test & Certificat Ctr Beijing 100081 Peoples R China

The Vehicle On-Board Controller (VOBC) is a core component of urban rail transit trains, with high maintenance costs and serious consequences in case of failure. Ensuring long-term VOBC operation while minimizing maintenance costs is of paramount importance. This paper proposes a multi-objective preventive maintenance (PM) strategy and optimization considering system unavailability and maintenance costs. Firstly, we provide a statement of the problem and analyze the impact of different maintenance actions on VOBC. Next, the decreasing process parameter is introduced to adjust the PM interval dynamically. The mathematical models of system unavailability and maintenance costs are derived to construct a multi-objective PM strategy. Then, we develop a hybrid heuristic algorithm including multi-objective particle swarm optimization and genetic algorithm (MOPSO-GA), which is used to obtain the optimal Pareto frontier for maintenance strategies. In the study, we select maintenance strategies by introducing an aversion factor to fulfill the preferences of different decision-makers. A comprehensive sensitivity analysis is performed to illustrate the impact of several critical parameters on the multi-objective PM strategy. The case study on Beijing subway's VOBC is also executed. The outcomes indicate that the proposed method can dynamically adjust to match the preferences of diverse decision-makers and the MOPSO-GA algorithm has excellent robustness on most parameters. This approach allows for creating field maintenance strategies catering to the varied maintenance requirements of urban rail transit VOBC.

关键词： Preventive maintenance Multi-objective maintenance strategy System unavailability Maintenance costs Multi-objective particle swarm optimization and genetic algorithm Vehicle on-board controller

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