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Chinese Automation Congress (CAC)

作者： Zeng, Qi He, Janjun Hu, Enze Wu, Renchao Cent South Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781728113128

In the pharmaceutical industry, the profit of the enterprise nowdays is more determined by warehouse cost, which can be significantly reduced by the reasonable distribution of goods. Considering the characteristics of goods (or drugs), a multi-objective optimization mathematical model for goods location is established in this paper. The optimization model is composed of a travel time model based on the input/output (I/O) frequency of goods and the speed change of stacker cranes when conveying goods. Meanwhile, taking into account strong goods correlation, finished products and raw materials are stored by class instead of by area, therefore, a within-class correlation model and a between-class correlation model are also comprised in the optimization model. The non-dominated sorting genetic algorithm ii (nsga-ii) is exploited to resolve the goods location optimization problem in the pharmaceutical automated warehouses. The simulation results show that the optimization model make the goods distribution more reasonable.

关键词： Automated warehouse Goods location optimization Mathematical model nsga-ii algorithm

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Multi-Objective Network Coding Optimization Based On nsga-ii algorithm

Multi-Objective Network Coding Optimization Based On NSGA-II...

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International Conference on Control Engineering and Communication Technology (ICCECT)

作者： Hao, Kun Wang, Beibei Luo, Yongmei Tianjin Inst Urban Construct Sch Comp & Informat Engn Tianjin Peoples R China Tianjin Inst Urban Construct Sch Control & Mech Engn Tianjin Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781467344999;9780769548814

Network coding could effectively improve transmission performance of multicast network, but encoding of node brings the additional calculation cost of node. In order to overcome the overhead brought by network coding, a network coding optimization model under the framework of algebraic network coding is designed in this paper, and the joint optimization for both the link cost and coding cost of network coding are carried out based on this model. In addition, the paper proposes MOONC (Multi-Objective Optimization Problem of Network Coding) based on improved nsga-ii algorithm. Adopting non-dominated sorting mechanism, virtual fitness and elitist strategy, this algorithm could not only improve algorithm efficiency and convergence speed, but also guarantee the population diversity. The simulation for typical network topology shows that this algorithm is effective and feasible.

关键词： Network Coding Link Cost Coding Cost nsga-ii algorithm

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Analysis and Optimization of the Electromagnetic Performance of a Novel Stator Modular Ring Drive Thruster Motor

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ENERGIES 2018年第6期11卷 1598-1598页

作者： Li, Yukai Song, Baowei Mao, Zhaoyong Tian, Wenlong Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Key Lab Unmanned Underwater Vehicle Xian 710072 Shaanxi Peoples R China

A rim driven thruster (RDT) is an integrated deep-sea motor thruster that has been widely studied. In order to improve the performance of RDT, a novel RDT motor with a modular stator is proposed in this paper. The electromagnetic performance of the new RDT motor is analyzed by the finite element method (FEM). The influence of structure parameters on the electromagnetic performance of the new RDT motor are analyzed in detail. It is shown that the effect of additional tooth width and pole arc coefficient on the electromagnetic performance of the stator modular RDT motor is significant. To obtain the optimal design with a maximum average electromagnetic torque and minimum torque fluctuation ratio, a multi-objective optimization design method combining the non-dominated sorting genetic algorithm ii (nsga-ii), Kriging method and FEM is presented in this paper. A set of Pareto optimal solutions is obtained, and the optimal design point is selected from the Pareto fronts. Compared with the initial design, the average electromagnetic torque of the optimized model is improved by 16.591% and the fluctuation ratio is reduced to 3.18%.

关键词： stator modular rim driven thruster (RDT) motor electromagnetic performance finite element method (FEM) multi-objective optimization design Kriging method nsga-ii algorithm

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Joint optimization of preventive maintenance and production scheduling for parallel machines system

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第1期32卷 913-923页

作者： Liao, Wenzhu Chen, Maocai Yang, Xiaoxia Chongqing Univ Dept Ind Engn Chongqing Peoples R China

This paper deals with the joint problem of production scheduling and maintenance planning as it becomes one important base for intelligent manufacturing. Due to the dual requirements both on cost and delivery date, a multi-objective optimization approach is proposed for parallel machines to allow decision makers to find compromise solution between production scheduling and maintenance planning: minimizing both maximum completion time and total maintenance cost. The new optimization approach is one multiple nondominated improved nsga-ii algorithm based on greedy idea and pre-distribution thinking, which can quickly and effectively seek Pareto optimal solution to solve the joint problem. As well as the introduced idea of averaging machine utilization, the job processing time model is constructed by considering machine degradation to meet real manufacturing situation. Moreover, a penalty function to maintenance (delay or advance) is proposed. Finally, the experimental analysis demonstrates the effectiveness and efficiency of this pre-distributed nsga-ii optimization approach, which could help solve the joint decision-making problem of production scheduling and preventive maintenance for parallel machines system.

关键词： Production scheduling preventive maintenance nsga-ii algorithm parallel machines integer programming

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Neumann-Michell theory-based multi-objective optimization of hull form for a naval surface combatant

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APPLIED OCEAN RESEARCH 2017年 63卷 129-141页

作者： Wu, Jianwei Liu, Xiaoyi Zhao, Min Wan, Decheng Shanghai Jiao Tong Univ Collaborat Innovat Ctr Adv Ship & Deep Sea Explor Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China

A numerical multi-objective optimization procedure is proposed here to describe the development and application of a practical hydrodynamic optimization tool, OPTShip-SJTU. Three components including hull form modification module, hydrodynamic performance evaluation module and optimization module consist of this tool. The free-form deformation (FFD) method and shifting method are utilized as parametric hull surface modification techniques to generate a series of realistic hull forms subjected to geometric constraints, and the Neumann-Michell (NM) theory is implemented to predict the wave drag. Moreover, nsga-ii, a muti-objective genetic algorithm, is adopted to produce pareto-optimal front, and kriging model is used for predicting the total resistance during the optimization process to reduce the computational cost. Additionally, the analysis of variance (ANOVA) method is introduced to represent the influence of each design variable on the objective functions. In present work, a surface combatant DTMB Model 5415 is used as the initial design, and optimal solutions with obvious drag reductions at specific speeds are obtained. Eventually, three of optimal hulls are analyzed by NM theory and a RANS-based CFD solver naoe-FOAM-SJTU respectively. Numerical results confirm the availability and reliability of this multi-objective optimization tool. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： OPTShip-SJTU Multi-objective optimization nsga-ii algorithm Neumann-Michell theory Wave drag

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Multi-Objective Optimization of Molten Carbonate Fuel Cell and Absorption Refrigerator Hybrid System

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Energy Procedia 2018年 152卷 904-909页

作者： Chengzhuang Miao Yuewu Huang College of Environmental Science and Engineering Donghua University Shanghai 201620 China

The multi-objective optimization of the hybrid system consisting of molten carbonate fuel cell and absorption refrigerator is carried out based on the nondominated sorting genetic algorithm ii (nsga-ii). The operating current density, temperature, pressure of the molten carbonate fuel cell and the irreversibility parameter of the absorption refrigerator are selected as the decision variables, and the equivalent power, efficiency and ecological performance of the hybrid system are treated as the comprehensive optimization objective functions. The Pareto optimal solution set under the constraint conditions of decision variables is get. Two kinds of effective decision-making tools are used: TOPSIS and LINMAP. They are used to obtain relative optimum solution as well as the optimized operation condition. At last, the results of the multi-objective optimization are compared with those of the single objective optimization.

关键词： molten carbonate fuel cell multi-objective optimization nsga-ii algorithm decision-making method

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A method for the optimum selection of datacenters in geographically distributed clouds

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JOURNAL OF SUPERCOMPUTING 2017年第9期73卷 4042-4081页

作者： Ziafat, Hassan Babamir, Seyed Morteza Univ Kashan Dept Comp Kashan Iran

The optimal selection of a datacenter is one of the most important challenges in the structure of a network for the wide distribution of resources in the environment of a geographically distributed cloud. This is due to the variety of datacenters with different quality-of-service (QoS) attributes. The user's requests and the conditions of the service-level agreements (SLAs) should be considered in the selection of datacenters. In terms of the frequency of datacenters and the range of QoS attributes, the selection of the optimal datacenter is an NP-hard problem. A method is therefore required that can suggest the best datacenter, based on the user's request and SLAs. Various attributes are considered in the SLA;in the current research, the focus is on the four important attributes of cost, response time, availability, and reliability. In a geo-distributed cloud environment, the nearest datacenter should be suggested after receiving the user's request, and according to its conditions, SLA violations can be minimized. In the approach proposed here, datacenters are clustered according to these four important attributes, so that the user can access these quickly based on specific need. In addition, in this method, cost and response time are taken as negative criteria, while accessibility and reliability are taken as positive, and the multi-objective nsga-ii algorithm is used for the selection of the optimal datacenter according to these positive and negative attributes. In this paper, the proposed method, known as nsgaii_Cluster, is implemented with the Random, Greedy and MOPSO algorithms;the extent of SLA violation of each of the above-mentioned attributes are compared using four methods. The simulation results indicate that compared to the Random, Greedy and MOPSO methods, the proposed approach has fewer SLA violations in terms of the cost, response time, availability, and reliability of the selected datacenters.

关键词： Geo-distributed datacenters Clustering K-means algorithm Multi-objective optimization algorithm nsga-ii algorithm Service-level agreement (SLA)

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Multiobjective formulation of the integration of storage systems within distribution networks for improving reliability

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ELECTRIC POWER SYSTEMS RESEARCH 2017年 148卷 87-96页

作者： Vieira Pombo, A. Murta-Pina, J. Fernao Pires, V. ESTSetubal Inst Politecn Setubal DEE Setubal Portugal Univ Nova Lisboa Ctr Technol & Syst Lisbon Portugal Univ Nova Lisboa Fac Ciencias & Tecnol Lisbon Portugal INESC ID Lisboa Lisbon Portugal

A new approach to improve reliability in distribution networks using energy storage systems is presented in this paper. Electric utilities have been using switching devices in distribution networks mainly to improve network reliability considering sustained interruptions. Temporary interruptions have been relegated to second place. However in modern societies appliances with electronic components are becoming more sensitive to temporary interruptions. Because of this, reliability indexes like the Momentary Average Interruption Frequency Index (MAIFI) that considers temporary interruptions are receiving more attention by electric utilities. The integration of storage systems into the multiobjective planning of distribution networks is proposed in this paper, to improve the reliability index MAIFI. This approach considers the optimization of storage systems integrated in a problem with multiple objective functions. The reliability index MAIFI is the first objective function, minimized by optimally choosing the size and location of battery banks in the distribution network. The second objective function, System Average Interruption Frequency Index (SAIDI) is optimized by finding the optimal number and location of switching devices. The third objective function consists on the minimization of the equipment cost. The optimization of these conflicting functions is achieved by the use of the Non-Dominated Sorting Genetic algorithm ii (nsga-ii). (C) 2017 Elsevier B.V. All rights reserved.

关键词： Distribution network planning Multiobjective optimization Switching devices Storage systems nsga-ii algorithm Reliability indexes SAIDI and MAIFI

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Optimal LNG importation portfolio considering multiple risk factors

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JOURNAL OF CLEANER PRODUCTION 2017年 151卷 452-464页

作者： Geng, Jiang-Bo Ji, Qiang Fan, Ying Shaikh, Faheemullah Zhongman Univ Econ & Law Sch Finance Wuhan 430073 Peoples R China Chinese Acad Sci Inst Policy & Management Ctr Energy & Environm Policy Res Beijing 100190 Peoples R China Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Mehran Univ Engn & Technol Mehran Univ Ctr Energy & Dev Jamshoro Sindh Pakistan Mehran Univ Engn & Technol Dept Elect Engn Jamshoro Sindh Pakistan

Using a supply chain perspective, this paper determines optimal portfolio strategies for liquefied natural gas (LNG) importation based on multi-objective optimisation models. Unlike previous studies, this paper first constructs a quantitative maritime transportation risk indicator by considering maritime transportation distances and the risk of pirate attacks. Next, it comprehensively considers the economic risks of importation, the exporting countries' political risks and the maritime transportation risks associated with developing optimal LNG importation strategies. Finally, from an emergency management perspective, different emergency scenarios are proposed to analyse how LNG importation strategies can be adjusted for extreme events occurring in LNG-exporting countries or along maritime transportation routes. A case study of China's LNG importation decisions is analysed using the proposed novel analytical framework. The non-dominated sorting genetic algorithm-ii (nsga-ii) is employed to obtain the Pareto solution sets of the multi-objective optimisation models. The empirical results show that if extreme events occur in Indonesia or if ships transporting LNG to China suddenly encounter pirate attacks along the North Africa China maritime transportation route, China should adjust its LNG importation strategies in a timely manner to ensure the security of LNG importation. By considering various significant risks, the new analytical framework proposed in this study offers an effective tool for making decisions about the optimal portfolio for LNG importation. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： LNG importation portfolio Maritime transportation risk Political risk Emergency management nsga-ii algorithm

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Study on Improving Multi-objective Flexible Job Shop Scheduling Based on Memetic algorithm in The nsga-ii Framework 2

Study on Improving Multi-objective Flexible Job Shop Schedul...

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2nd International Conference on Cloud Computing and Internet of Things (CCIOT)

作者： Xu, Liang Xia, Zhao Yi Ming, Huang Dalian Jiao Tong Univ Sch software Dalian Peoples R China

ISBN: (纸本)9781467398237

This paper was the research about multi-objective flexible job shop scheduling based on Memetic algorithm under the improved Non-dominated sorting genetic algorithm ii (nsga-ii). On the basis of nsga-ii, a strategy of improving elite which was based on circular crowding distance was designed to increase the diversity of population distribution, prevent algorithm trapping in locally optimal solution, and avoid the disadvantage of premature genetic algorithm. And the research of multi-objective flexible job shop scheduling was established to optimize indexes, which was based on maximum completion time and different equipment load index.

关键词： job-shop scheduling nsga-ii algorithm Memetic algorithm multi-objective flexible job shop

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