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Optimizing human reliability in work shifts scheduling: A real case of clothing job shop

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2023年第8期39卷 3518-3536页

作者： Arekamari, Mohammad Akbari Namaghi, Mohammad Ghasemi Payame Noor Univ Fac Management & Econ Tehran Iran

In this research, we provide a mathematical model with an objective function of minimizing human errors for the problem of shift scheduling optimization. The unique aspects which are considered in our model are learning, forgetting, fatigue, and rest parameters (which affect the human reliability). Various problems with different human parameters, based on real cases of clothing job shops, are solved to study the efficiency and effectiveness of the optimization model. Two improved bacterial foraging algorithms are presented to handle the complexity of our problem. To examine the capability of the presented swarm intelligence algorithms in obtaining global optimum problem, we compare the results of the algorithms against the solution of basic bacterial foraging algorithm in terms of solutions quality. Results indicate that the solution approaches are more efficient in searching solution space and convergence. Analysis of different shift scheduling shows that in moderate fatigue condition, the scenario of scheduling with continuous work hours is better in terms of productivity. Also, experiments demonstrate that optimum reliable shift schedules are related to human factors. The findings of our research indicate that the presented scheduling model and improved algorithms can provide optimum shift schedules regarding human factors.

关键词： bacterial foraging algorithm clothing job shop human parameters human reliability scheduling scenarios

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Collision Avoidance Strategy Optimization of Ship's Speed Alteration with bacterial foraging algorithm

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International Conference on Mechatronics and Control Engineering (ICMCE 2012)

作者： Ma, Wenyao Yang, Jiaxuan Guangdong Ocean Univ Nav Dept Zhanjiang 524025 Peoples R China Dailian Maritime Univ Navigat Coll Dalian 116026 Peoples R China

ISBN: (纸本)9783037855959

When ship navigates at sea, collision avoidance of ship's speed alteration is frequently adopted by officer on watch in order to prevent from forming collision situation with target ship(s). bacterial foraging algorithm (BFA) that imitates the social foraging behavior of Escherichia coli is an optimal search method suitable for complex problems. This research adopts the bacterial foraging algorithm to find the speed alteration collision avoidance strategy from an economical viewpoint, combining the international regulations for preventing collisions at sea (COLREGS) and the safety domain of ship. An optimal time of changing speed, amplitude of speed alteration and navigation restoration time will also be provided. The effectiveness of the algorithm has been verified by simulation. The study offers new thinking and a practical method for collision avoidance decision.

关键词： collision avoidance bacterial foraging algorithm safety domain of a ship optimization

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bacterial foraging algorithm based parameter estimation of solar PV model

Bacterial Foraging Algorithm based parameter estimation of s...

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Annual International Conference on Emerging Research Areas

作者： Neeraja Krishnakumar Rini Venugopalan N. Rajasekar School of Electrical Engineering VIT University

ISBN: (纸本)9781467351508

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model of photovoltaic module before proceeding to the installation part. However, the modeling-of solar PV characteristics is difficult since the manufacturer's datasheet provides only four values such as V_(mp), I_(mp), V_(oc), and I_(sc). For the accurate modeling of photovoltaic panels, the precise estimation of model parameters at different environmental conditions is very essential. Optimization technique is a very powerful tool to obtain the solution of complex non-linear problems. In this paper, the application of the bacterial foraging algorithm (BFA) for the accurate extraction of model parameters has been discussed. A systematic evaluation and performance comparison of BFA with other optimization techniques such as Genetic algorithm, Artificial Immune System etc. has been done and the best computational technique is determined based on certain criteria such as accuracy, consistency, speed of convergence etc. The computed data is compared with experimental data and the results are validated using two photovoltaic modules of different nature (multicrystalline and thin film).

关键词： bacterial foraging algorithm Genetic algorithm Artificial Immune algorithm Photovoltaic module

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Improved dual-population cooperative bacterial colony algorithm for solving distributed flexible job shop scheduling problem 24

Improved dual-population cooperative bacterial colony algori...

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3rd International Symposium on Control Engineering and Robotics, ISCER 2024

作者： Xun, Xuebin Xu, Keke Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment Zhengzhou University of Light Industry Zhengzhou450002 China

ISBN: (纸本)9798400709951

An improved two-population collaborative bacterial colony algorithm is proposed for the distributed flexible job shop scheduling problem. Different initialization schemes are designed for the two populations to improve the quality and diversity of the initial solutions;different cross-mutation operators are designed for the two populations during the convergence operation to achieve a balance between the exploration and development of the algorithm;on the basis of the method of exchanging elite individuals to perform the replication operation, two migration methods are designed to complete the separation of the populations and the generation of the new populations, which in turn realizes the collaborative optimization between the two populations;Designing the corresponding local search for key plants to improve the local search capability of the algorithm. Finally, fifteen test algorithms are obtained by utilizing the generalized test set of the extended flexible job shop, and twenty-five sets of comparative tests are designed by using the orthogonal test method for parameter selection. Comparing with the existing algorithms, the experimental results confirm that the proposed algorithm is more effective in solving the distributed flexible job shop scheduling problem. © 2024 ACM.

关键词： bacterial foraging algorithm Distributed flexible job shop scheduling dual-population local search

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Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based

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Energy Engineering 2022年第5期119卷 2081-2104页

作者： Yuchen Hao Dawei Su Zhen Lei Control Center of State Grid of Jiangsu Electric Power Co. Ltd.Nanjing210000China

Renewable energy sources(RES)such as wind turbines(WT)and solar cells have attracted the attention of power system operators and users alike,thanks to their lack of environmental pollution,independence of fossil fuels,and meager marginal *** the introduction of RES,challenges have faced the unit commitment(UC)problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’inputs and outputs,and specifying the optimal generation of each *** output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with *** a result,the UC problem in the presence of RES faces *** grid consumed load is not always equal to and is randomly different from the predicted values,which also contributes to uncertainty in solving the aforementioned *** current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this *** new model is adopted to solve the wind-generated power uncertainty,and energy storage systems(ESSs)are included in the problem for further *** problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage *** solve the UC programming model,a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.

关键词： Unit commitment security-constrained programming wind farms uncertainty honey bee mating algorithm bacterial foraging algorithm

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Tourism route optimization based on improved knowledge ant colony algorithm

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COMPLEX & INTELLIGENT SYSTEMS 2022年第5期8卷 3973-3988页

作者： Li, Sidi Luo, Tianyu Wang, Ling Xing, Lining Ren, Teng Cent South Univ Forestry & Technol Sch Foreign Languages Changsha 410004 Peoples R China Cent South Univ Forestry & Technol Coll Logist & Transportat Changsha 410004 Peoples R China

With the rapid development of tourism in the economy, popular demand for tourism also increases. Unreasonable distribution arises a series of problems such as reduction of tourist satisfaction and decrease of the income in tourist attractions. Based on consideration of tourism route planning, a mathematical model which takes the maximization of the overall satisfaction of all tourist groups as the objective function is established by taking the age and preferences of tourists, the upper limits of the tourist carrying capacity in various tourism routes, etc. as constraints. It aims to maximize income in tourist attractions while improving tourist satisfaction. Based on the tourist data of a travel agency, the statistical ideas of hierarchical clustering and random sampling are utilized to process the acquired data to obtain the simulation examples in the article. Aiming at this model, a knowledge-based hybrid ant colony algorithm is designed. On this basis, the mechanism of bacterial foraging algorithm is introduced. It improves the performance of the algorithm and avoids the generation of local optimal solution. At the same time, two knowledge models are in addition to improve the solution quality of the algorithm. Typical simulation indicates that the improved ant colony algorithm can find the optimal solution at a higher efficiency when solving the tourism route planning problem. The model can also satisfy the economic benefit of enterprises and achieves favorable path optimization effect under different optional routes, thus further verifying the effect liveness of the model.

关键词： Tourism route planning Tourist satisfaction Ant colony algorithm bacterial foraging algorithm Knowledge models

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Artificial neural network integrated with bio-inspired approach for optimal VAr management and voltage profile enhancement in grid system

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2021年第21期43卷 2838-2859页

作者： Chandrasekaran, Kumar Selvaraj, Jaisiva Xavier, Felix Joseph Kandasamy, Prabaakaran M Kumarasamy Coll Engn Dept EEE Karur Tamil Nadu India IFET Coll Engn Dept EEE Villupuram Tamil Nadu India Bule Hora Univ Elect & Comp Engn Dept Hagere Maryam Ethiopia Vel Tech High Tech Dr Rangarajan Dr Sakunthala En Dept EEE Chennai Tamil Nadu India

This article presents the design of an improved smart grid model with an improved optimal VAr strategy using a bio-inspired approach. The integration of an artificial neural network technique with bio-inspired algorithms is the novelty of this research work. The objective of the work is attained by integrating the artificial neural network with three different evolving techniques like the Big Bang-Big Crunch algorithm, the gravitational search algorithm, and bacterial foraging algorithm. The three different layers of artificial neural network reconcile with the power grid for enhancing the voltage profile generated by bio-inspired techniques. The effectiveness and reliability of the algorithms are examined on the standard IEEE 30 bus system. The sample test bus system comprises six Generators, four Transformer tap changers, and 2 Static Shunt VAr Compensator. Artificial intelligence technology is applied for enhancing the voltage profile by managing reactive power across the power system network. The results produced by artificial intelligence technique are more competent and outstanding than any other conservative optimization techniques. It is found that the real power loss across the network has been reduced from 5.744 MW to 4.234 MW, while the bus voltage deviation has been minimized from 1.475 p.u. to 0.0918 p.u. and the voltage profile has been improved from 0.968 to 1.0985 p.u. after integration of the artificial neural network. The optimal values of objective functions are attained by evaluating the training and testing performance of artificial neural network criteria such as coefficient of determination, mean absolute error, root mean square error, and mean absolute percentage error. The obtained results show the remarkable improvement in voltage profile after integrating artificial intelligence technology with a power grid system.

关键词： Artificial Neural Network Gravitational Search algorithm Big Bang-Big Crunch algorithm bacterial foraging algorithm Active Power Loss Voltage Deviation Voltage Profile

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bacterial foraging algorithm Application for Induction Motor Field Efficiency Estimation under Harmonics and Unbalanced Voltages

Bacterial Foraging Algorithm Application for Induction Motor...

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International Electric Machines & Drives Conference

作者： Vladimir Sousa Santos Enrique C. Quispe Julio R. Gómez Sarduy Percy R. Viego Norberto Lemozy Alejandro Jurado Mario Brugnoni Faculty of Engineering Universidad de Cienfuegos Cienfuegos Cuba Energy Research Group GIEN Energy and Mechanical Department Universidad Autónoma de Occidente Cali Colombia Faculty of Engineering University of Buenos Aires Argentina

The time harmonic flow in industrial electrical grids with voltage deviations and unbalance, significantly affects three phase induction motor operation and makes difficult energy characterization. There are different methods for in situ estimation of motors efficiency. However, none of them have been sufficiently precise for the analysis under non-sinusoidal, deviated and unbalanced voltage conditions. This paper presents a method based on the equivalent circuits, with losses segregation and using bacterial foraging algorithm (BFA). It allows determining under field conditions and low invasiveness, energy efficiency of induction motors operating under the before mentioned conditions. This method was tested successfully on a 0.55 kW motor, fed with significant levels of unbalance and voltage harmonics.

关键词： bacterial foraging algorithm efficiency harmonic with unbalance voltage induction motor

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Dynamic hyperparameter tuning-based path tracking control for robotic rollers working on earth-rock dam under complex construction conditions

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AUTOMATION IN CONSTRUCTION 2022年第0期143卷

作者： Guan, Shiwei Wang, Jiajun Wang, Xiaoling Shi, Mengnan Lin, Weiwei Chen, Wenlong State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

Robotic roller technology is important for ensuring the compaction quality and construction efficiency of earth -rock dams, and its key challenge is related to path tracking control. The current easy-to-use and effective pro-portion-integration-differentiation (PID) path tracking controller requires time-consuming and labor-intensive hyperparameter tuning, making its application in complex construction environments with a set of fixed hyperparameters, such as transition, misalignment, and position drift, difficult. This paper describes the TSABFA-PID path tracking controller based on hybrid tunicate swarm algorithm (TSA) and bacterial foraging algorithm (BFA), and modified kinematic model. The proposed controller can dynamically self-tune hyperparameters to adapt to complex construction condition by optimizing the objective function. The proposed controller achieved better results in both simulation environment and on-site tests than state-of-the-art methods. In the future, the deep reinforcement learning and human-computer interaction techniques will be integrated into the proposed controller to improve the compaction performance.

关键词： Path tracking control Robotic roller PID controller Tunicate swarm algorithm bacterial foraging algorithm Earth -rock dam

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Image Registration using bacterial foraging Optimization algorithm on Multi-core Processors

Image Registration using Bacterial Foraging Optimization Alg...

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4th International Symposium on Electrical and Electronics Engineering (ISEEE)

作者： Bejinariu, Silviu-Ioan Romanian Acad Inst Comp Sci Iasi Branch Iasi Romania

ISBN: (纸本)9781479924424

In this paper is proposed a parallel approach of the bacterial foraging Optimization algorithm (BFOA) used for image registration (IR) on multi-core processors based systems. IR is an optimization problem computing optimal parameters of the transform to align a source image to a given model by maximizing a similarity measure. IR is an important step in image fusion: the fusion result is affected by the registration result quality. A relatively new registration method is based on the bacterial foraging Optimization algorithm. Depending on the used optimization technique and similarity measure, the IR process may be time consuming. A comparison of the sequential and proposed parallel execution on multi-core systems, for pixel intensity and point feature based registration is presented.

关键词： bacterial foraging algorithm image registration optimization parallel computing

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