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第37届中国控制会议

作者： Cheng-Shou Lai Jing Lu Yu Qiao Transportation Engineering College Dalian Maritime University

Aiming at giving full play to the advantages of enterprise operation under the background of professional division of labor, logistics assignment in the port supply chain is studied. The uncertainty of shipper’s demand, unit cost and operating time, which can be described as interval numbers, and an optimization model for port supply chain based on minimization of costs and total operating cycle is established. As the objective function is a nonlinear interval uncertain problem, a double nested genetic algorithm is designed for the assignment problem. In the algorithm, the inner-layer genetic algorithm（IP-GA） is used to solve the objective function interval in the uncertainty domain, while the outer layer is nondominated sorting genetic algorithm（NSGA-Ⅱ）. Finally, a case study is given to demonstrate the superiority and feasibility of the proposed algorithm for multi-objective interval optimization.

关键词： Port Supply Chain Interval Analysis Intergenerational genetic algorithm non-dominated sorting genetic algorithm nonlinear Programming

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NSGA-II applied to dynamic flexible job shop scheduling problems with machine breakdown

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MODERN PHYSICS LETTERS B 2018年第34-36期32卷

作者： Chen, Chao Ji, Zhicheng Wang, Yan Jiangnan Univ Minist Educ Engn Res Ctr IoT Technol Applicat Wuxi 214122 Peoples R China

This paper focuses on multi-objective dynamic flexible job shop scheduling problem (MODFJSP) with machine breakdown. First, a multi-objective dynamic scheduling model is established, with objectives to minimize makespan and total machine workload. Second, according to the processing status of faulty machine, a hybrid rescheduling strategy including transfer rescheduling strategy and complete rescheduling strategy is proposed to react to stochastic machine breakdown. The performance of two rescheduling strategies is analyzed in terms of the scheduling efficiency and its stability, from the delay extent and initial scheduling deviation, respectively. Besides, the optimal adaptation conditions of both scheduling strategies are obtained. Furthermore, the non-dominated sorting genetic algorithm (NSGA-II) is employed to solve the constructed model. Experimental results demonstrate the effectiveness of the proposed strategies on reducing the impact of machine breakdown in real scheduling.

关键词： Flexible job shop dynamic scheduling machine breakdown non-dominated sorting genetic algorithm

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Operational Amplifier Circuit Sizing Based on NSGA-II and Particle Swarm Optimization

Operational Amplifier Circuit Sizing Based on NSGA-II and Pa...

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International Conference on Networks and Advances in Computational Technologies (NetACT)

作者： Sasikumar, A. Muthaiah, R. SASTRA Univ Sch Comp Thanjavur India

ISBN: (纸本)9781509065905

This paper presents the simulation based design of analog integrated circuit design using an evolutionary algorithm. We design a basic two stage opamp using multi objective optimization based techniques, namely particle swarm optimization and non-dominated sorting genetic algorithm. These two algorithms are new kind of multi objective optimization method used to design analog IC transistor sizes and biasing currents. The necessities of these algorithms are to reduce the circuit design time and improve the accuracy of performance parameters. In this work, we automatically synthesize transistor sizes with constraint function using PSO and NSGA-II.

关键词： analog design automation integrated circuits particle swarm optimization non-dominated sorting genetic algorithm

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Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm

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HEALTH CARE MANAGEMENT SCIENCE 2017年第1期20卷 55-75页

作者： Feng, Yen-Yi Wu, I-Chin Chen, Tzu-Li Mackay Mem Hosp Dept Emergency Med Taipei 10449 Taiwan Fu Jen Catholic Univ Dept Informat Management New Taipei 24205 Taiwan

The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic;furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

关键词： Simulation optimization non-dominated sorting genetic algorithm Multi-objective computing budget allocation Medical resource allocation Emergency department

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Improved NSGA-II algorithm based on differential evolution mechanism 36

Improved NSGA-II algorithm based on differential evolution m...

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36th Chinese Control Conference (CCC)

作者： Zhang, Wei Zhang, Jiao-long Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454000 Henan Peoples R China

ISBN: (纸本)9789881563934

From simulation experiments of the multi-objective optimization control of wastewater treatment process (WWTP), it can be found that the number of obtained Pareto solutions is less using the normal non-dominated sorting genetic algorithm-II (NSGA-II) sometimes. To achieve a satisfactory optimal performance, an improved NSGA-II algorithm based on differential evolution mechanism is proposed in this paper. The simulation results show that the diversity of the solutions is enhanced and a better homogeneity of non-inferior solutions is kept using the proposed method.

关键词： non-dominated sorting genetic algorithm differential evolution multi-objective optimization uniform wastewater treatment

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An ANN-based optimization model for facility layout problem using simulation technique

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SCIENTIA IRANICA 2017年第1期24卷 364-377页

作者： Azimi, P. Soofi, P. Islamic Azad Univ Qazvin Branch Fac Ind & Mech Engn Qazvin Iran

A real manufacturing system faces lots of real-world situations, such as stochastic behaviors;the lack of attention to this issue is noticeable in the previous research. The aim of this paper is to find the optimum layout and the most appropriate handling transporters for the problem by a novel solving algorithm. The new model contains two objective functions including the Material Handling Costs (MHC) and the complication time of jobs (makespan). Real-world situations such as stochastic processing times, random breakdowns, and cross traffics among transporters are considered in this paper. Several experiment designs have been produced using DOE technique in simulation software and an Artificial Neural Network (ANN) as a meta-model is used to estimate the objective functions in the metaheuristic algorithms. A hybrid non-dominated sorting genetic algorithm (HNSGA-II) is applied for the optimization task. The proposed methodology is evaluated through a real case study. First, simulation model is validated by comparing it with a real data set. Then, the prediction performance of ANN is investigated. Finally, the ability of H-NSGA-II in searching the solution space is compared with the traditional NSGA-II. The results show that the proposed approach, combing simulation, ANN, and H-NSGA-II, provides promising solutions for practical applications. (C) 2017 Sharif University of Technology. All rights reserved.

关键词： Facility layout Artificial neural network Discrete-event simulation non-dominated sorting genetic algorithm

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A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2016年第9期67卷 1184-1199页

作者： Long, Jianyu Zheng, Zhong Gao, Xiaoqiang Pardalos, Panos M. Chongqing Univ Chongqing Peoples R China Univ Florida Gainesville FL USA

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.

关键词： scheduling steel plant multi-objective optimization non-dominated sorting genetic algorithm release time heuristic

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Multi-objective re-synchronizing of bus timetable: Model, complexity and solution

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2016年 67卷 149-168页

作者： Wu, Yinghui Yang, Hai Tang, Jiafu Yu, Yang Jiangsu Univ Sci & Technol Sch Econ & Management 2 Mengxi Rd Zhenjiang 212003 Jiangsu Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Dongbei Univ Finance & Econ Coll Management Sci & Engn Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

This work is originally motived by the re-planning of a bus network timetable. The existing timetable with even headways for the network is generated using line by line timetabling approach without considering the interactions between lines. Decision-makers (i.e., schedulers) intend to synchronize vehicle timetable of lines at transfer nodes to facilitate passenger transfers while being concerned with the impacts of re-designed timetable on the regularity of existing timetable and the accustomed trip plans of passengers. Regarding this situation, we investigate a multi-objective re-synchronizing of bus timetable (MSBT) problem, which is characterized by headway-sensitive passenger demand, uneven headways, service regularity, flexible synchronization and involvement of existing bus timetable. A multi-objective optimization model for the MSBT is proposed to make a trade-off between the total number of passengers benefited by smooth transfers and the maximal deviation from the departure times of the existing timetable. By clarifying the mathematical properties and solution space of the model, we prove that the MSBT problem is NP-hard, and its Pareto-optimal front is non-convex. Therefore, we design a non-dominated sorting genetic (NSGA-II) based algorithm to solve this problem. Numerical experiments show that the designed algorithm, compared with enumeration method, can generate high-quality Pareto solutions within reasonable times. We also find that the timetable allowing larger flexibility of headways can obtain more and better Pareto-optimal solutions, which can provide decision-makers more choice. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Bus timetabling Passenger transfer Multi-objective optimization non-dominated sorting genetic algorithm

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Optimal design of multichannel fiber Bragg grating filters using Pareto multi-objective optimization algorithm

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OPTICS COMMUNICATIONS 2016年 358卷 59-64页

作者： Chen, Jing Liu, Tundong Jiang, Hao Xiamen Univ Sch Informat Sci & Engn Xiamen 361005 Peoples R China Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

A Pareto-based multi-objective optimization approach is proposed to design multichannel FBG filters. Instead of defining a single optimal objective, the proposed method establishes the multi-objective model by taking two design objectives into account, which are minimizing the maximum index modulation and minimizing the mean dispersion error. To address this optimization problem, we develop a two-stage evolutionary computation approach integrating an elitist non-dominated sorting genetic algorithm (NSGA-II) and technique for order preference by similarity to ideal solution (TOPSIS). NSGA-ll is utilized to search for the candidate solutions in terms of both objectives. The obtained results are provided as Paretic) front. Subsequently, the best compromise solution is determined by the TOPSIS method from the Paretic) front according to the decision maker's preference. The design results show that the proposed approach yields a remarkable reduction of the maximum index modulation and the performance of dispersion spectra of the designed filter can be optimized simultaneously. (C) 2015 Elsevier B.V. All rights reserved

关键词： Fiber Bragg grating (FBG) Multichannel filter Multi-objective optimization non-dominated sorting genetic algorithm

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Performance-cost optimization of tuned mass damper under low-moderate seismic actions

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2016年第18期25卷 1103-1122页

作者： Greco, Rita Marano, Giuseppe Carlo Fiore, Alessandra Tech Univ Bari DICATECH Dept Civil Engn Environm Terr Bldg & Chem Via Orabona 4 I-70125 Bari Italy Tech Univ Bari Dept Civil Engn & Architecture DICAR Via Orabona 4 I-70125 Bari Italy Fuzhou Univ Coll Civil Engn SIBERC 2 Xue Yuan Rd Fuzhou Fujian Peoples R China

In this paper, a multi-objective optimization of a single tuned mass damper is proposed at the aim to control vibrations induced in building structures under low-moderate earthquakes. The optimum design is carried out by considering both economic and performance criteria. The cost of the device is the economic objective that is assumed directly related to its mechanical parameters. At the aim to control the damage level and the behaviour of components and equipment, the ratio between the absolute accelerations of the protected structure and the unprotected one is assumed as the device performance. A multi-objective optimization is then formulated, and Pareto optimum solutions are achieved by the non-dominated sorting genetic algorithm in its second version. Finally, a sensitivity analysis of the optimum solution with respect to some input data is carried out. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： tuned mass damper random vibrations cost of protection system multi-objective optimization non-dominated sorting genetic algorithm

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