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Multi-objective meta-heuristics to solve three-stage assembly flow shop scheduling problem with machine availability constraints

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2015年第3期53卷 944-968页

作者： Shoaardebili, Niloofar Fattahi, Parviz Bu Ali Sina Univ Dept Ind Engn Hamadan Iran

In this paper, a three-stage assembly flow shop scheduling problem with machine availability constraints is taken into account. Two objectives of minimising total weighted completion times (flow time) and minimising sum of weighted tardiness and earliness are simultaneously considered. To describe this problem, a mathematical model is presented. The problem is generalisation of three-machine flow shop scheduling problemand two-stage assembly flow shop scheduling problem. Since these problems are known to be NP-hard, the considered problem is also strongly NP-hard. Therefore, two multi-objective meta-heuristics are presented to efficiently solve this problem in a reasonable amount of time. Comprehensive computational experiments are performed to illustrate the performance of the presented algorithms.

关键词： simulated annealing multi-objective optimisation assembly flow shop machine availability constraint non-dominated sorting genetic algorithm

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Optimizing surface profiles during hot rolling: A genetic algorithms based multi-objective optimization

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COMPUTATIONAL MATERIALS SCIENCE 2006年第1-2期37卷 159-165页

作者： Chakraborti, N. Kumar, B. Siva Babu, V. Satish Moitra, S. Mukhopadhyay, A. Indian Inst Technol Dept Met & Mat Engn Kharagpur 721302 W Bengal India TATA Steel Div Res & Dev Jamshedpur 831001 Bihar India

Several variants of genetic algorithms have been used to study the surface profiles of hot rolled slabs, quantified in terms of crown, a major parameter related to their geometric tolerances. Two different models are presented, and simulations in a multi-objective mode are carried out to generate the relevant Pareto fronts, which, in turn, are tested against the operational data of an integrated steel plant. (c) 2006 Elsevier B.V. All rights reserved.

关键词： rolling hot rolling crown flatness genetic algorithms ant colony optimization differential evolution generalized differential evolution non-dominated sorting genetic algorithm NSGA-II strength Pareto evolutionary algorithm multi-objective optimization pareto front multi-objective evolutionary computation

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non-linear modelling using central composite design to predict green sand mould properties

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2007年第5期221卷 881-895页

作者： Parappagoudar, M. Pratihar, D. K. Datta, G. L. Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

The quality of cast products in green sand moulds is largely influenced by the mould properties, such as green compression strength, permeability, hardness, and others, which depend on the input (process) parameters. The relationships of these properties with input parameters such as sand grain size and shape, amount of binder and water, number of strokes in ramming, etc., are complex in nature. Design of experiments (DOE) with response surface methodology was used to develop the said input-output relationships. An attempt was also made to relate green compression strength to mould hardness. Moreover, responses such as permeability, hardness, and green compression strength were separately expressed as functions of bulk density. The relationship between green compression strength and permeability in a clay-bonded sand system is generally reverse in nature. To ensure good-quality castings, the green sand mould is required to have both adequate permeability as well as strength. This situation will lead to the formulation of a multiobjective optimization problem. A Pareto optimal front of solutions was developed for strength and permeability, using a multiobjective optimization tool - the so-called non-dominated sorting genetic algorithm (NSGA). The Pareto optimal front contains a number of optimal solutions. One optimal solution differs from another on account of the fact that different sets of weightages are given to the objective functions. Thus, the user has to select one set of optimal solutions out of different available sets.

关键词： central composite design design of experiments ANOVA non-dominated sorting genetic algorithm Pareto optimal front

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Robust optimal centralized controller to mitigate the small signal instability in an islanded inverter based microgrid with active and passive loads

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2017年 90卷 225-236页

作者： Raju, E. S. N. P. Jain, Trapti IIT Indore Discipline Elect Engn Indore Madhya Pradesh India

This paper proposes a robust optimal centralized controller to mitigate the small signal instability caused by the interaction between the control loops of rectifier interfaced active loads (RIALs) and the inverter interfaced distributed generations. The proposed controller is based on a robust linear quadratic regulator with prescribed degree-of-stability (LQRPDS). In order to reduce communication system burden and improve the flexibility and reliability of the proposed controller, a kalman state estimator has been designed. The optimal values of the diagonal weighting matrices of LQRPDS and kalman estimator are obtained by formulating a bi-objective optimization problem, which is solved using a fast and elitist multi-objective non-dominated sorting genetic algorithm (NSGA-II). A full dynamical linearized state space model of an islanded MG represented in synchronous (DQ) reference frame has been considered for analyzing the stability. However, schur balanced model order reduction technique has been used to reduce the complexity in the design of the controller. Eigenvalue analysis and time-domain simulations have been presented to demonstrate the robust performance of the controller under various load configurations as well as under step load disturbances. The efficacy of the proposed centralized controller is verified by comparing its performance with that of LQR based centralized controller. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Inverter-based microgrid Linear quadratic regulator with prescribed degree-of-stability non-dominated sorting genetic algorithm Robust optimal centralized controller Small-signal stability

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Form-finding method for the target configuration under dead load of a new type of spatial self-anchored hybrid cable-stayed suspension bridges

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ENGINEERING STRUCTURES 2021年 227卷 111407-111407页

作者： Wang, Xiaoming Wang, Huan Zhang, Ji Sun, Yuan Bai, Yunteng Zhang, Yufeng Wang, Haicheng Changan Univ Key Lab Transport Ind Bridge Detect Reinforcement Xian 710064 Shaanxi Peoples R China Suzhou Univ Sci & Technol Sch Civil Engn Suzhou 215011 Jiangsu Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Hubei Peoples R China Xian Highway Res Inst Xian 710065 Peoples R China China Acad Bldg Res Beijing 100013 Peoples R China

General form-finding problems of cable-supported bridges are established based on a design scenario in which rigidly fixed starting control points must be given as necessary design constraints prior to independent analysis of any of its cable subsystems. This article presents a form-finding method to address a new case, in which the starting control point serves as an intermediate, flexibly variable connection, to couple two related cable subsystems in a multi-nonlinear environment for the target configuration under dead load (TCUD) of a novel type of spatial self-anchored hybrid cable-stayed suspension (HCSS) bridge. A two-layer framework is proposed by integrating finite element analysis (FEA) and analytical formulas with optimization algorithms to form a self regulated interactive analysis among subsystems in an iterative manner. The outer layer seeks to achieve self equilibrium of the global system under the control information of the TCUD, while the inner layer optimizes the subsystems in terms of the initial tensions in the main cables, stay-cables, branches and hangers to obtain a rational mechanical state of the bridge. Then, the TCUD and the intermediate starting control points are determined. To achieve computational stability, a high-performance accelerated Steffens-Newton (ASN) differential algorithm is developed for the shape finding of the cable-hanger subsystem, whereas the non-dominated sorting genetic algorithm (NSGA-II) is adopted as a multiobjective optimizer for TCUD optimization of the other subsystems. The proposed framework is applied to a real-scale self-anchored HCSS bridge, and its validity and performance are demonstrated by comparison studies with a non-optimal scheme and in-field test data.

关键词： Form-finding analysis Self-anchored hybrid cable-stayed suspension bridge Cable force optimization Flexible components non-dominated sorting genetic algorithm Accelerated Steffens-Newton algorithm

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A Multiobjective Approach for the Optimal Placement of Protection and Control Devices in Distribution Networks With Microgrids

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IEEE ACCESS 2022年 10卷 41776-41788页

作者： Reiz, Cleberton de Lima, Tayenne Dias Leite, Jonatas Boas Javadi, Mohammad Sadegh Gouveia, Clara Sofia Sao Paulo State Univ Dept Elect Engn BR-15385 Ilha Solteira Brazil Inst Syst & Comp Engn Technol & Sci P-4200465 Porto Portugal

Protection and control systems represent an essential part of distribution networks by ensuring the physical integrity of components and by improving system reliability. Protection devices isolate a portion of the network affected by a fault, while control devices reduce the number of de-energized loads by transferring loads to neighboring feeders. The integration of distributed generation has the potential to enhance the continuity of energy services through islanding operation during outage conditions. In this context, this study presents a multi-objective optimization approach for sizing and allocating protection and control devices in distribution networks with microgrids supplied by renewable energy sources. Reclosers, fuses, remote-controlled switches, and directional relays are considered in the formulation. Demand and generation uncertainties define the islanding operation and the load transfer possibilities. A non-dominated sorting genetic algorithm is applied in the solution of the allocation problem considering two conflicting objectives: cost of energy not supplied and equipment cost. The compromise programming is then performed to achieve the best solution from the Pareto front. The results show interesting setups for the protection system and viability of islanding operation.

关键词： Circuit faults Fuses Microgrids Uncertainty Renewable energy sources Distribution networks Reliability Distribution systems microgrids protection system planning non-dominated sorting genetic algorithm compromise programming

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Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles

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ENERGY 2019年 185卷 1054-1062页

作者： Meng, Jinhao Cai, Lei Stroe, Daniel-Ioan Luo, Guangzhao Sui, Xin Teodorescu, Remus Northwestern Polytech Univ Sch Automat Xian 710072 Shaanxi Peoples R China Xian Univ Technol Fac Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Shaanxi Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Lithium-ion (Li-ion) batteries have become the dominant choice for powering the Electric Vehicles (EVs). In order to guarantee the safety and reliability of the battery pack in an EV, the Battery Management System (BMS) needs information regarding the battery State of Health (SOH). This paper estimates the battery SOH from the optimal partial charging voltage profiles, which is a straightforward and effective solution for the EV applications. In order to further improve the accuracy and efficiency of the SOH estimation, a novel method optimizing single and multiple voltage ranges during the EV charging process is proposed in this paper. non-dominated sorting genetic algorithm II (NSGA-II) is applied to automatically select the optimal multiple voltage ranges, while the grid search technique is used to find the optimal single voltage range. The non-dominated solutions from NSGA-II enable the SOH estimation at different battery charging stages, which gives more freedom to the implementation of the proposed method. Three Nickel Manganese Cobalt (NMC)-based batteries from EV, which have been aged under calendar ageing for 360 days, are used to validate the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： State of health estimation Partial voltage range Lithium-ion battery Electric vehicle non-dominated sorting genetic algorithm

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Multi-objective performance optimal design of large-scale injection molding machine

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2009年第3-4期41卷 242-249页

作者： Wei, Zhe Feng, Yi-xiong Tan, Jian-rong Wang, Jin-long Li, Zhong-kai Zhejiang Univ Coll Mech & Energy Engn Hangzhou 310027 Peoples R China Qingdao Technol Univ Sch Comp Engn Qingdao 266033 Peoples R China

In order to solve the complex multi-objective optimal performance design of large-scale injection molding machines, NSGA-II is used to find a much better spread of design solutions and better convergence near the true Pareto-optimal front. The combination of the design method and the injection molding machine is discussed. Screw diameter performance, stick inside distance performance, mold moving route performance and mold-locked force performance are chosen as the four main performance evaluation indexes. Some related parameters are associated to get a performance indication. And performance optimization design parameter constraints are listed to make the design solutions to have practical significance. The mathematical models of two objectives and the mathematical models of three objectives are analyzed. Finally, the instance of HTF180X1N large-scale injection molding machine is taken as an example to demonstrated that such method is effective and practical.

关键词： Multi-objective optimization design genetic algorithm Unitary performance design non-dominated sorting genetic algorithm Injection molding machine

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Multi-objective optimization operation with corrective control actions for meshed AC/DC grids including multi-terminal VSC-HVDC

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2017年 93卷 178-193页

作者： Kim, M. K. Chung Ang Univ Dept Energy Syst Engn 84 Heukseok Ro Seoul 06974 South Korea

This paper presents a multi-objective optimal operation of meshed AC/DC power grids including multi terminal voltage-source-converter-based high-voltage direct current (VSC-MTDC) systems. The proposed approach is modeled as a corrective security-constrained optimal power flow (CSC-OPF) problem, with the minimization of both the operation cost and power loss as the objectives. Moreover, it provides a cost-effective solution to assist in decision-making, and improves the system security during operation. The N - 1 contingency security criterion is enforced for both AC and DC transmission networks, and corrective control is used to eliminate or alleviate post-contingency security violations. The corrective control actions used in this paper include not only secure operation control actions, but also economical post-contingency corrective control of the multi-terminal VSC-HVDC. To increase the computation speed, a contingency screening technique is applied to CSC-OPF by efficiently selecting the most severe case of the N 1 contingency, as obtained using a voltage security index (VSI). The proposed approach uses the non-dominated sorting genetic algorithm (NSGA-II) to find multi-objective OPF solutions by checking the post-contingency state feasibility while taking into account post-contingency corrective actions. Simulation results confirm the validity and effectiveness of this approach. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Contingency screening Corrective control action Multi-terminal VSC HVDC non-dominated sorting genetic algorithm Security-constrained optimal power flow

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Bi-objective optimization for improving the locomotion performance of the vibration-driven robot

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ARCHIVE OF APPLIED MECHANICS 2021年第5期91卷 2073-2088页

作者： Diao, Binbin Zhang, Xiaoxu Fang, Hongbin Xu, Jian Tongji Univ Sch Aerosp Engn & Appl Mech Shanghai 200092 Peoples R China Fudan Univ Inst AI & Robot Shanghai 200433 Peoples R China Fudan Univ Engn Res Ctr AI & Robot Minist Educ Shanghai 200433 Peoples R China Fudan Univ Shanghai Engn Res Ctr AI & Robot Shanghai 200433 Peoples R China

The average velocity and the backward velocity are two critical factors to evaluate the locomotion performance of a vibration-driven robot. These two factors are often antagonistic when the ground friction is isotropic and, consequently, lead to a bi-objective optimization. The goal of this paper is to give a practicable bi-objective optimization scheme for vibration-driven locomotion robots. To this end, a generalized dynamic model of the multi-module vibration-driven locomotion robot is built by assuming that the ground friction is isotropic Coulomb type and the driving force is ideal. The non-dominated sorting genetic algorithm II (NSGA-II) is then employed to construct the optimization scheme and then obtain the Pareto front. A numerical example with a two-module vibration-driven system is provided to demonstrate the necessity and effectiveness of applying the bi-objective optimization method. A robot prototype is further designed, and a series of experimental tests are carried out. By choosing the motors' rotary speed and phase shift as optimization parameters, the average and backward velocities are extracted to check against the Pareto front. The consistency between the theoretical predictions and the experimental results shows the practicability of the proposed bi-objective optimization scheme. Therefore, the proposed optimization scheme will provide comprehensive guidance for system design and optimization in micro-locomotion robots.

关键词： Vibration-driven locomotion system Micro-locomotion robot Isotropic Coulomb's dry friction non-dominated sorting genetic algorithm Pareto front

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