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Scheduling optimization for laminated door machining shop based on improved genetic algorithm

COMPUTERS & OPERATIONS RESEARCH 2025年 180卷

作者： Zhou, Xiaomin Li, Rongrong Wu, Zhihui Nanjing Forestry Univ Coinnovat Ctr Efficient Proc & Utilizat Forest Res Nanjing 210037 Peoples R China

In the digital transformation of the wooden-door manufacturing industry, material preparation planning and production scheduling directly influence the stability and effectiveness of the manufacturing system. Constructive problem-specific algorithms have been instrumental in solving real-world laminated door machining shop scheduling problem (LDMSSP). LDMSSP is a complex problem that combines a distributed permutation flowshop scheduling problem and distributed hybrid flow-shop scheduling problem. An improved genetic algorithm fused with the strategies of the improved heuristic algorithm, the local search, variable neighborhood search with multiple critical paths, and the iterated greedy search (IGGA) was proposed for application in the material preparation planning and scheduling optimization to minimize the makespan. Comprehensive design of experiments and statistical analyses were conducted to determine appropriate algorithm parameters and verify the substantial improvement of the IGGA. Experiments conducted on various benchmark instances indicated that IGGA outperformed other metaheuristics in both the best relative deviation index and the average relative deviation index. In the end, the minimal makespan for a real-world case involving the production of 74 laminated doors was 1.1 h with a 17.91% reduction, which further demonstrated the effectiveness of the proposed model and algorithm in solving LDMSSP. It also provided a valuable reference for the rational arrangement of material preparation planning and machining scheduling sequences.

关键词： Scheduling Laminated door Home furnishings intelligent manufacturing improved genetic algorithm Variable neighborhood search

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Online optimization of bidirectional amplifiers in long-haul fiber-optic time transfer systems based on improved genetic algorithm

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OPTICAL FIBER TECHNOLOGY 2025年 90卷

作者： Liu, Bo Guo, Xinxing Liu, Tao Dong, Ruifang Zhang, Shougang Chinese Acad Sci Natl Time Serv Ctr Xian 710600 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China Chinese Acad Sci Key Lab Time & Frequency Stand Xian 710600 Peoples R China

Bidirectional erbium-doped fiber amplifiers (Bi-EDFAs) are widely adopted in long-haul fiber-optic time transfer systems to compensate for the optical loss in the fiber links. The optical gains of the Bi-EDFAs should be elaborately optimized as they will induce unwanted noises at the same time, resulting in significant deterioration of the signal-to-noise ratio (SNR). In the practical long-distance fiber links with multiple Bi-EDFAs involved, the online optimization of the optical gains for the best SNR becomes a hard task. In this paper, we proposed a novel method of optimizing the optical gains of the working Bi-EDFAs in the fiber link by using an improved genetic algorithm, which can achieve an equivalent SNR result to that with the idealized solution based on the enumerative method meanwhile with the calculation time no long increases exponentially with the increased number of Bi-EDFAs. This method was further verified experimentally on 276 and 439 km-long coiled fiber links, in which three and five Bi-EDFAs were respectively incorporated. For both cases, the achieved SNRs showed more than 15 dB improvement compared with the original strategy of fixing the optical gains. Furthermore, compared with the results based on the idealized enumerative method, the SNRs were only 2 dB lower, while the iteration time was shortened by three orders of magnitude.

关键词： Time transfer Bi-EDFAs Signal-to-noise ratio improved genetic algorithm

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Flexible Scheduling Optimization of Four-Way Shuttle Systems Based on improved genetic algorithm

Flexible Scheduling Optimization of Four-Way Shuttle Systems...

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Lei, Kaiqian Zhang, Qin Xiang, Yang School of Logistics Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9798331506797

The four-way shuttle system, known for its high level of automation and strong handling capacity, is widely used in flexible automated warehousing systems. To enhance operational efficiency, this study establishes an optimization model for the scheduling of multiple four-way shuttles, targeting order completion time, minimal energy consumption, and shortest travel paths. An improved genetic algorithm is proposed to solve this model, providing optimal solutions for order allocation. By testing the algorithm under different system parameters, results show that the improved genetic algorithm achieves at least a 25% improvement in optimization efficiency compared to traditional genetic algorithms. © 2025 IEEE.

关键词： A algorithm flexible warehousing system four-way shuttle scheduling improved genetic algorithm

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Multiobjective Reactive Power Optimization Planning for Medium Voltage Distribution Networks Based on improved genetic algorithm

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2025年第1期2025卷

作者： Li, Min Zhang, Juncheng Tan, Jing Tan, Xiaohong Tang, Lingjie Guangxi Power Grid Corp Ltd Nanning 530023 Peoples R China Guangxi Power Grid Corp Ltd Guilin Power Bur Guilin 541002 Peoples R China

The medium voltage distribution network is a key bridge between the power sector and electricity users. In the process of increasing user demand for electricity, the medium voltage distribution network system has encountered problems such as insufficient reactive power, unreasonable distribution, and insufficient voltage at the end nodes of the line, which have affected the power supply quality and stability of the power system. Therefore, a multiobjective reactive power optimization planning method for medium voltage distribution networks based on an improved genetic algorithm is studied. Establish a mathematical model for medium voltage distribution network planning based on the multiobjective functions of active power loss, total voltage deviation of system nodes, and minimum total compensation amount of system compensation devices. The balance equation between active and reactive power of power nodes and power absorption losses is taken as the equality constraint, and the maximum and minimum constraints of variables such as voltage at the generator end and tap position of the on-load tap changer are taken as the constraints of the model. By combining the advantages of the standard genetic algorithm and simulated annealing algorithm, an improved genetic algorithm is formed to effectively solve the constructed mathematical model. After countless iterations, the effective solution of the model is obtained to achieve multiobjective reactive power optimization planning for medium voltage distribution networks. The experimental results show that this method can achieve multiobjective reactive power optimization in medium voltage distribution networks and improve the stability of the power system.

关键词： improved genetic algorithm medium voltage distribution grid multiobjective penalty coefficient reactive power optimal planning voltage stability

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Construction and Simulation of a Data-driven Blended Teaching 'Video Recording Technology' Course Evaluation System Based on improved genetic algorithm 3

Construction and Simulation of a Data-driven Blended Teachin...

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3rd International Conference on Artificial Intelligence and Education, ICAIE 2024

作者： Lin, Yan Hu, Guofeng College of Art and Design Zhanjiang University of Science and Technology Guangdong China

ISBN: (纸本)9798400712692

With the development of modern educational technology, blended teaching has become an important teaching mode in higher education. In this paper, a data-driven hybrid teaching "video technology"course evaluation system based on improved genetic algorithm is proposed and simulated. Firstly, the current status of the application of improved genetic algorithm in higher education and its application in optimization problems are introduced through literature review. Second, a mathematical model of the data-driven hybrid teaching "video technology"course evaluation system based on improved genetic algorithm is constructed. By introducing the improved genetic algorithm mathematical model, the evaluation model is optimized to improve the accuracy and efficiency of evaluation. At the same time, combined with video technology teaching, real-time monitoring and recording of the classroom teaching process is realized, which provides rich data support for course evaluation. Finally, the constructed course evaluation system was simulated and studied. The performance of the system was verified by simulating the actual teaching process and collecting relevant data. The simulation results show that the data-driven hybrid teaching "video technology"course evaluation system based on improved genetic algorithm can accurately reflect the teaching process. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blended Learning Course Evaluation Data-driven Assessment Educational Technology improved genetic algorithm

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An improved genetic algorithm for generation expansion planning

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IEEE TRANSACTIONS ON POWER SYSTEMS 2000年第3期15卷 916-922页

作者： Park, JB Park, YM Won, JR Lee, KY Anyang Univ Dept Elect Engn Anyang City 708113 South Korea Seoul Natl Univ Sch Elect Engn Seoul 151742 South Korea Korea Elect Power Corp Korea Elect Power Res Inst Taejon 305380 South Korea Penn State Univ Dept Elect Engn University Pk PA 16802 USA

This paper presents a development of an improved genetic algorithm (TGA) and its application to a least-cost generation expansion planning (GEP) problem. Least-cost GEP problem is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, a process which is computationally impossible in a real-world GEP problem. in this paper, an improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. The main advantage of the IGA approach is that the "curse of dimensionality" and a local optimal trap inherent in mathematical programming methods can be simultaneously overcome. The IGA approach is applied to two test systems, one with 15 existing power plants, 5 types of candidate plants and a 14-year planning period, and the other, a practical Long-term system with a 24-year planning period.

关键词： generation expansion planning genetic algorithm global optimization improved genetic algorithm

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improved genetic algorithm with Local Search for Satellite Range Scheduling System and its Application in Environmental monitoring

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2019年 21卷 19-27页

作者： Song, Yan-Jie Zhang, Zhong-Shan Song, Bing-Yu Chen, Ying-Wu Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China

Satellites play an important role in such areas as environmental monitoring and disaster prediction that are related to human survival and development. Satellite monitoring and control is the key to the satellite's management and control of the ground to ensure its smooth implementation. Because of the imbalance of demand and available resources, satellite range scheduling has become particularly important. This paper analyzes the satellite range scheduling problem and sets up mathematical models and constraints. Afterwards, this paper proposes an efficient algorithm that combines improved genetic algorithm and local search method. The improved genetic algorithm is used to rapidly improve the quality of the planning scheme, and the neighborhood search is used for the subsequent small-scale optimization. In order to improve the speed of search, our algorithm uses a reorganization operation and a mutation operation adjusted with the number of iterations. In order to test the effectiveness of the algorithm, we conducted experimental verifications of the calculations of various types of satellites at different mission scales and compared them with other algorithms. (C) 2018 Elsevier Inc. All rights reserved.

关键词： improved genetic algorithm Local Search Satellite Range Scheduling Problem Heuristic Optimization Environmental monitoring

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improved genetic algorithm for multi-threshold optimization in digital pathology image segmentation

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SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Huang, Tangsen Yin, Haibing Huang, Xingru Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Hunan Univ Sci & Engn Sch Informat Engn Yongzhou 425199 Peoples R China Hangzhou Dianzi Univ Lishui Inst Lishui 323000 Peoples R China

This paper presents an improved genetic algorithm focused on multi-threshold optimization for image segmentation in digital pathology. By innovatively enhancing the selection mechanism and crossover operation, the limitations of traditional genetic algorithms are effectively addressed, significantly improving both segmentation accuracy and computational efficiency. Experimental results demonstrate that the improved genetic algorithm achieves the best balance between precision and recall within the threshold range of 0.02 to 0.05, and it significantly outperforms traditional methods in terms of segmentation performance. Segmentation quality is quantified using metrics such as precision, recall, and F1 score, and statistical tests confirm the superior performance of the algorithm, especially in its global search capabilities for complex optimization problems. Although the algorithm's computation time is relatively long, its notable advantages in segmentation quality, particularly in handling high-precision segmentation tasks for complex images, are highly pronounced. The experiments also show that the algorithm exhibits strong robustness and stability, maintaining reliable performance under different initial conditions. Compared to general segmentation models, this algorithm demonstrates significant advantages in specialized tasks, such as pathology image segmentation, especially in resource-constrained environments. Therefore, this improved genetic algorithm offers an efficient and precise multi-threshold optimization solution for image segmentation, providing valuable reference for practical applications.

关键词： Image segmentation improved genetic algorithm Multi-threshold optimization Global search capability Robustness and stability

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improved genetic algorithm freely searching for dangerous slip surface of slope

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Journal of Central South University of Technology 2005年第6期12卷 749-752页

作者：万文曹平冯涛袁海平 School of Resources and Safety Engineering Central South University Changsha 410083 China School of Civil Engineering Hunan University of Science and Technology Xiangtan 411201 China School of Resources and Safety Engineering Central South University Changsha 410083 China School of Civil Engineering Hunan University of Science and Technology Xiangtan 411201 China

Based on the slice method of the non-circular slip surface for the calculation of integral stability of slope, an improved genetic algorithm was proposed, which can freely search for the most dangerous slip surface of slope and the corresponding minimum safety factor without supposing the geometric shape of the most dangerous slip surface. This improved genetic algorithm can simulate the genetic evolution process of organisms and avoid the local minimum value compared with the classical methods. The results of engineering cases show that it is a global optimal algorithm and has many advantages, such as higher efficiency and shorter time than the simple genetic algorithm.

关键词： slice method dangerous non-circular slip surface minimum safety factor improved genetic algorithm

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improved genetic algorithm for Solving Flexible Job Shop Scheduling Problem with Machine Deterioration Effect 7

Improved Genetic Algorithm for Solving Flexible Job Shop Sch...

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7th IEEE International Conference on Computer Science and Network Technology (ICCSNT)

作者： Lin, Yali Zhang, Peng Dalian Jiaotong Univ Coll Software Dalian Peoples R China Dalian Jiaotong Univ Innovat Entrepreneurship Inst Educ Dalian Peoples R China

ISBN: (纸本)9781728132990

An improved cloud adaptive genetic annealing algorithm is proposed for the multi-objective FJSP with machine deterioration effect [16]. In terms of shortcomings for poor local search ability and premature convergence in GA, we improve fitness calculations, cross-variation, etc. Fitness calculation is combined with local search ability and probability jump property of simulated annealing algorithm to make it jump out of the local optimal solution. The Hamming similarity is inserted in the crossover operation, and the similarity is used to detect whether crossover operation is required, which can accelerate the running efficiency and convergence speed of the algorithm. Then, the cross-operation combines the adaptive crossover probability of the cloud model to enhance the global search capability of the algorithm. At last, we set standard position and cross position to improve cross-operation, which can enhance the global search ability of the algorithm. Through simulation experiments, the effectiveness of the algorithm for the integrated multi-objective shop scheduling algorithm is verified.

关键词： Flexible job shop scheduling improved genetic algorithm Could model Machine deterioration algorithm Cloud model Machine deterioration effect Hamming similarity

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