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12th IEEE International Conference on Software Engineering and Service Science (IEEE ICSESS)

作者： Liu, Guowei Ren, Hongxiang Xu, Fuquan Zhou, Yi Dalian Maritime Univ Key Lab Marine Dynam Simulat Dalian Liaoning Peoples R China Oil Prod Serv Co CNOOC Energy Technol & Serv Tianjin 300452 Peoples R China

ISBN: (纸本)9781665415064

In order to reduce the detention time of ships in the port, make full use of port resources and optimize port services. This paper analyzes the factors that affect the efficiency of the dispatching of ships entering and leaving the port, and studies the coordinated dispatching of multiple ship types and multiple berth types. Based on the proposed hypothesis, with the minimum total scheduling time and the minimum waiting time as the optimization goals, a coordinated scheduling model for berth allocation and channel was established. Based on the nsga-ii algorithm, a multi-objective optimization genetic algorithm suitable for solving the model of berth allocation and channel coordination scheduling is designed. By testing the intensive ship entry and exit data in a short period of time, the solution efficiency and stability of the algorithm in this paper are better than those of the nsga-ii algorithm, and it is more suitable for solving the optimization problem of berth allocation and channel coordination scheduling.

关键词： Berth allocation ships entering and leaving the port improved nsga-ii algorithm

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Dangerous Goods Container Location Allocation Strategy based on improved nsga-ii algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 253-261页

作者： Zhang, Xinmei Liang, Nannan Chen, Chen China Univ Petr Coll Mech & Elect Engn Qingdao Shandong Peoples R China China Univ Petr Coll Econ & Management Qingdao Shandong Peoples R China

The characteristics of port dangerous goods are complicated and diverse in danger, which is very likely to cause chain effects once a fire and explosion accident occurs. Based on the distribution characteristics of dangerous goods container yards and the special national storage requirements for dangerous goods containers, the paper establishes a multi-objective optimization model with a double priority of safety and economy, starting from reducing the number of reversals. The improved non-dominated sorting genetic algorithm based on the elite strategy was used to solve the model and the algorithm was tested and improved. Based on the Pareto optimal solution set, the entropy weight-TOPSIS method was used to optimize the sorting of multiple solution sets, which improved the performance of the algorithm. The analysis further clarifies the important relationship between attributes, and the running time is shortened by 85.7% compared with the traditional nsga algorithm. The optimization model and algorithm can provide decision support for the actual operation and management of container storage, and provide a good reference for accident risk prevention and control.

关键词： Dangerous goods containers container allocation improved nsga-ii algorithm entropy weight-TOPSIS

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Research on intelligent workshop resource scheduling method based on improved nsga-ii algorithm

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2021年 71卷 102141-102141页

作者： Yuan, Minghai Li, Yadong Zhang, Lizhi Pei, Fengque Hohai Univ Coll Mech & Elect Engn Changzhou Jiangsu Peoples R China

With the intensification of globalization, the competition among various manufacturing enterprises has become increasingly fierce, enterprises are developing in the direction of the product diversification, zero inventory or low inventory, and scheduling in production management has become more complicated. In this paper, machine and workpiece were as objects to study the problem of workshop scheduling in intelligent manufacturing environment. The resource scheduling model of intelligent manufacturing workshop was established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. The Nondominated Sorting Genetic algorithm (nsga-ii) algorithm was improved, and the evaluation function was established based on ranking level and crowding degree, then the competition mechanism was introduced. Random mutation strategy and crossover method based on process and machine was adopted to generate a new generation of populations. The elitist retention strategy was improved, the variable proportion method was designed to determine the probability, and the optimal solution is determined by the Analytic Hierarchy Process (AHP). The benchmark cases and practical production and processing problems were tested to verify the superiority and effectiveness of the improved algorithm.

关键词： Intelligent manufacturing Workshop resource scheduling improved nsga-ii algorithm Variable proportion

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Research on flexible weaving planning based on nsga-ii algorithm

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JOURNAL OF INDUSTRIAL TEXTILES 2025年 55卷

作者： Dai, Ning Wu, Wenkang Xu, Kaixin Hu, Xudong Yuan, Yanhong Peng, Laihu Xu, Yushan Gan, Pengyuan Zhejiang Sci Tech Univ Key Lab Modern Text Machinery & Technol Zhejiang P 9282nd St Hangzhou 310018 Peoples R China Zhejiang Kangli Automat Technol Co Ltd Shaoxing Peoples R China

Facing the shift in the weaving industry from mass production to a more diversified, small-batch production model, traditional production scheduling systems are no longer capable of meeting the demand for rapid market response. To address this issue, this paper first analyzes the production process of a weaving workshop, identifying key scheduling challenges such as order allocation, equipment selection, and operation sequencing. Based on this analysis, a flexible job shop multi-objective scheduling model tailored for weaving workshops is developed. To handle the multiple constraints and optimization goals inherent in the model, an improved nsga-ii algorithm is proposed. This algorithm combines artificial bee colony (ABC) algorithm for population initialization with simulated annealing (SA) for population filtering. Simulation examples and case studies from actual workshops demonstrate that the improved nsga-ii algorithm outperforms other algorithms in solving the scheduling problem for weaving workshops. The proposed multi-objective scheduling model and its improved algorithm provide accurate and efficient optimization solutions for workshop scheduling.

关键词： improved nsga-ii algorithm weaving flexible scheduling duplex codec multi-objective optimization

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Optimization Control of Front-End Speed Regulation (FESR) Wind Turbine Based on improved nsga-ii

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IEEE ACCESS 2019年 7卷 45583-45593页

作者： Li Xiaoqing Dong Haiying Li Hongwei Li Mingxue Sun Zhiqiang Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Sch New Energy & Power Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Univ Technol Sch Energy & Power Engn Lanzhou 730050 Gansu Peoples R China Lanzhou Jiaotong Univ Inst Railway Technol Lanzhou 730000 Gansu Peoples R China

Aiming at the power optimization control problem of front-end speed regulation (FESR) wind turbine, when the wind speed above rated wind speed, an optimal control strategy of variable pitch and variable torque based on improved nsga-ii algorithm is proposed. For this strategy, the reverse learning mechanism was used to initialize and evolve, and the crossover probability and mutation probability were adjusted dynamically based on the exponential distribution. A fuzzy comprehensive evaluation was used to get the best solution from the non-inferior solutions. The objective function was established upon the goals of the constant output speed for hydraulic speed converter and the smoothness of FESR's output power. And pitch angle and vane opening angle were optimized by the improved nsga-ii algorithm. Afterward, the optimization control algorithm has been validated through simulation experiment to a 2MWFESR. The simulation results show that the output power of FESR was smoothed, and the speed fluctuation and power fluctuation of FESR were also reduced, better control performance is thus achieved. Therefore, it is feasible and effective to optimize the control of FESR by the improved nsga-ii algorithm proposed in this paper.

关键词： Front-end speed regulation (FESR) wind turbine hybrid automata power control optimization control improved nsga-ii algorithm

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Inverse analysis of geotechnical parameters using an improved version of non-dominated sorting genetic algorithm ii

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COMPUTERS AND GEOTECHNICS 2024年 171卷

作者： He, Bo-Hu Du, Xiu-Li Bai, Ming-Zhou Yang, Jin-Wen Ma, Dong China Railway 16th Bur Grp Co Ltd Postdoctoral Res Workstat Beijing 100018 Peoples R China Beijing Univ Technol Fac Urban Construct Beijing 100124 Peoples R China Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China

The accuracy of slope mechanical parameter values determines the accuracy of slope stability assessments and the effectiveness of protective measures. Traditional methods for obtaining geotechnical parameters cannot entirely take into consideration the size, disturbance, variability, and uncertainty of mechanical parameters. Hence, we aimed to propose two algorithmic models for the geotechnical parameters of highway slope materials based on an improved non-dominated sorting genetic algorithm ii (nsga-ii) combined with a GA-optimised backpropagation (BP) neural network (NN),named BPGA- nsga-ii model and improved BPGA- nsga-ii *** results indicated that compared with the original nsga-ii model, the improved model showed a substantial shift in the Pareto front, and the errors are smaller than the original *** the two improved models, the improved BPGA-nsga-ii model has higher inversion ***, this study provides a scientifically effective method for improving the accuracy of the geotechnical parameter inversion for highway slope materials.

关键词： improved nsga-ii algorithm Highway slope Mechanical parameters Inversion Geotechnical parameters

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A novel optimal dispatch strategy for hybrid energy ship power system based on the nsga-ii

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 232卷

作者： Wang, Xinyu Zhu, Hongyu Luo, Xiaoyuan Chang, Shaoping Guan, Xinping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Jiangsu Collaborat Innovat Ctr Smart Distribut Net Nanjing 210000 Peoples R China Shanghai Jiao Tong Univ Sch Elect & Elect Engn Shanghai 200240 Peoples R China

As the most promising future generation green ship, hybrid energy ship power systems (HESPS) have gradually attracted attention. However, the integration of new energy including wind energy and solar energy, raises key challenges in designing a suit optimal dispatch for HESPS under different navigation conditions. Based on this, an optimal dispatch strategy using the improved Non -dominated Sorting Genetic (nsga-ii) algorithm is proposed. Firstly, an integrated HESPS model consisting of diesel power generation system, energy storage system (ESS), wind power generation system (WPGS) and photovoltaic power generation system is established. Based on this, a multi -objective optimization strategy is proposed to reduce the cost and greenhouse gas emissions. Through the design of crossover operator and mutation operator, an improved nsgaii is developed to find optimal solutions. Finally, three cases are presented to test the performance of proposed optimal dispatch strategy. Compared with traditional nsga-ii and multi -objective particle swarm optimization (MOPSO), the indicator of Hypervolume, Proportion of independent solutions, Generational Distance (GD) and Inverted Generational Distance can be improved at least 0.39%, 0.18%, 1.85% and 15.87%. At the same time, the corresponding cost and energy efficiency operational index (EEOI) of HESPS can be reduced by 13.17% and 17.53%.

关键词： Hybrid ship power system Greenhouse gas emission improved nsga-ii algorithm Energy management system

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Aircraft fleet route optimization based on cost and low carbon emission in aviation line alliance network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期39卷 1163-1182页

作者： Yan, Yan Zhang, Jin Tang, Qiuyu Southwest Jiaotong Univ Sch Transportat & Logist Chengdu Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Integrated & Intelligent Tra Chengdu Peoples R China

This paper studies the flight path optimization problem of air cargo companies in aviation line alliance. There are two limitations in this paper. One is to limit of the number and location of airbases and capacity in the air network. The other is to limit of flight time and airspace capacity of full cargo aircraft in actual operation. Considering the influence of alliance on operation, the selection probability of air alliance is introduced. It is assuming that all cargo aircraft is one type, the unit transportation cost of every aviation line is the same as each other, the queuing problem of aircraft landing is not considered, and the network transportation demand of itself must be completed by an airline. It proposes a directed aircraft fleet routing problem optimization model (SMDDDAAAFRPTW) with multi-airbase stochastic and time constraints to minimize total operating cost and flight distance. Using the multi-objective optimization algorithm nsga-ii by most scholars, and improving the initial solution generation step, introducing Genetic engineering into cross-mutation to solve the optimal number and location of air bases and fleet routing of multiple aircraft. Comparing with the weighted method and ant colony algorithm, it shows that the improved nsga-ii algorithm is effective and has better computational efficiency. The results show that the more segments are selected for outsourcing, the lowest cost of network and the lowest carbon emission. This kind of decision-making behavior is only suitable for the initial operation phase of the enterprise.

关键词： Aircraft fleet route optimization multi-airbases stochastic time and capacity constraints improved nsga-ii algorithm

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Multi-objective Optimization-Based algorithm for Selecting the Optimal Path of Rural Multi-temperature Zone Cold Chain Dynamic Logistics Intermodal Transportation

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-14页

作者： Qi, Chunwei Jilin Univ Architecture & Technol Sch Management Engn Changchun 130114 Peoples R China

The road network in rural areas is complex and the infrastructure is relatively backward. The multi-temperature zone cold chain logistics involves agricultural products with different temperature requirements, which requires considering the transportation cost, carbon emission cost, refrigeration cost, and time cost of different temperature zones during path planning, thereby increasing the difficulty of path planning. Therefore, a multi-objective optimization-based algorithm for selecting the optimal path of rural multi-temperature zone cold chain dynamic logistics intermodal transportation is proposed. Based on the analysis of the multi-temperature cold chain collection and distribution model based on multimodal transportation, a multi-objective optimization model is constructed. This model aims to minimize transportation costs, carbon emission costs, refrigeration costs, time costs, and maximize logistics quality, while satisfying constraints such as transfer schedule times, the number of transport mode conversions, transport mode selection, and time continuity. To solve this model, an improved nsga-ii algorithm is adopted, which combines an improved mutation operator, congestion distance calculation, and the C-W saving algorithm to achieve the optimal transport path solution. Additionally, ArcGIS software is used to implement the shortest path planning based on real road networks. The experimental results show that by selecting the road-rail combined transport mode and adopting the D1-D6-D10 transport path, it is possible to transport fresh agricultural products from location A to the distribution center at location B, with the lowest Pareto fitness value. Furthermore, the algorithm's effectiveness is further verified by completing the end-of-life fresh agricultural product distribution task with four multi-temperature refrigerated vehicles. The study also finds that extending or shortening the latest service time window for customers, although it leads to a dec

关键词： Multi-temperature zone Cold chain logistics Path selection Multimodal transportation improved nsga-ii algorithm C-W saving algorithm

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Optimization of the Energy Efficiency of a Piston Compressed Air Engine

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2014年第6期60卷 395-406页

作者： Yu, Qihui Cai, Maolin Shi, Yan Fan, Zichuan Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China

To improve the energy efficiency and output power of the piston compressed air engine (CAE), a mathematical model of its working process was set up. With the use of the MATLAB/Simulink software for simulation, the influences of the bore-to-stroke ratio, intake pressure and valve lift on the performance of the engine were obtained for the analysis of the energy efficiency and the output power. Moreover, to optimize the energy efficiency of the engine with the given output power, an improved nsga-ii was introduced, and a series of optimization intake pressures and valve lifts was obtained. When the output power value is about 2 kW, the intake pressure and the intake valve lift can be set to 1.99 MPa and 9.99 mm, the energy efficiency is highest: 31.17%. Finally, that the improved nsga-ii is superior to nsga-ii in proximity and diversity has been proved. This research can be referred in the optimization of the piston CAE and provides a method for the energy efficiency optimization study.

关键词： compressed air engine optimization energy efficiency improved nsga-ii algorithm

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