scholarly journals A multi-objective selective maintenance optimization method for series-parallel systems using NSGA-III and NSGA-II evolutionary algorithms

2021 ◽  
Vol 16 (3) ◽  
pp. 372-384
Author(s):  
E.B. Xu ◽  
M.S. Yang ◽  
Y. Li ◽  
X.Q. Gao ◽  
Z.Y. Wang ◽  
...  
Keyword(s):  
Rest Period ◽  
Optimization Method ◽  
Parallel System ◽  
Maintenance Cost ◽  
Nsga Ii ◽  
Allocation Model ◽  
Multi Objective ◽  
Objective Model ◽  
Selective Maintenance ◽  
Personnel Allocation

Aiming at the problem that the downtime is simply assumed to be constant and the limited resources are not considered in the current selective maintenance of the series-parallel system, a three-objective selective maintenance model for the series-parallel system is established to minimize the maintenance cost, maximize the probability of completing the next task and minimize the downtime. The maintenance decision-making model and personnel allocation model are combined to make decisions on the optimal length of each equipment’s rest period, the equipment to be maintained during the rest period and the maintenance level. For the multi-objective model established, the NSGA-III algorithm is designed to solve the model. Comparing with the NSGA-II algorithm that only considers the first two objectives, it is verified that the designed multi-objective model can effectively reduce the downtime of the system.

Multi-objective imperfect selective maintenance optimization for series-parallel systems with stochastic mission duration

2022 ◽  
pp. 1748006X2110666
Author(s):  
Chun Su ◽  
Kui Huang ◽  
Zejun Wen
Keyword(s):  
Optimization Model ◽  
Hazard Rate ◽  
Random Variable ◽  
Maintenance Cost ◽  
Maintenance Optimization ◽  
Engineering System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Mission Duration ◽  
Selective Maintenance

To improve the probability that an engineering system successfully completes its next mission, it is crucial to implement timely maintenance activities, especially when maintenance time or maintenance resources are limited. Taking series-parallel system as the object of study, this paper develops a multi-objective imperfect selective maintenance optimization model. Among it, during the scheduled breaks, potential maintenance actions are implemented for the components, ranging from minimal repair to replacement. Considering that the level of maintenance actions is closely related to the maintenance cost, age reduction coefficient and hazard rate adjustment coefficient are taken into account. Moreover, improved hybrid hazard rate approach is adopted to describe the reliability improvement of the components, and the mission duration is regarded as a random variable. On this basis, a nonlinear stochastic optimization model is established with dual objectives to minimize the total maintenance cost and maximize the system reliability concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is adopted to solve the model. Numerical experiments are conducted to verify the effectiveness of the proposed approach. The results indicate that the proposed model can obtain better scheduling schemes for the maintenance resources, and more flexible maintenance plans are gained.

Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

A Multi-Dwell Temperature Profile Design for the Cure of Thick CFRP Composite Laminates

2021 ◽  
Author(s):  
Wenchang Zhang ◽  
Yingjie Xu ◽  
Xinyu Hui ◽  
Weihong Zhang
Keyword(s):  
Temperature Profile ◽  
Composite Laminates ◽  
Optimization Method ◽  
Maximum Temperature ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimization Result ◽  
Design Variables ◽  
Cure Time ◽  
Thick Laminates

Abstract This paper develops a multi-objective optimization method for the cure of thick composite laminates. The purpose is to minimize the cure time and maximum temperature overshoot in the cure process by designing the cure temperature profile. This method combines the finite element based thermo-chemical coupled cure simulation with the non-dominated sorting genetic algorithm-II (NSGA-II). In order to investigate the influence of the number of dwells on the optimization result, four-dwell and two-dwell temperature profiles are selected for the design variables. The optimization method obtains successfully the Pareto optimal front of the multi-objective problem in thick and ultra-thick laminates. The result shows that the cure time and maximum temperature overshoot are both reduced significantly. The optimization result further illustrates that the four-dwell cure profile is more e ective than the two-dwell, especially for the ultra-thick laminates. Through the optimization of the four-dwell profile, the cure time is reduced by 51.0% (thick case) and 30.3% (ultra-thick case) and the maximum temperature overshoot is reduced by 66.9% (thick case) and 73.1% (ultra-thick case) compared with the recommended cure profile. In addition, Self-organizing map (SOM) is employed to visualize the relationships between the design variables with respect to the optimization result.

scholarly journals Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

2019 ◽  
Vol 11 (23) ◽  
pp. 6728 ◽  
Author(s):  
Zhang ◽  
Huang ◽  
Liu ◽  
Li
Keyword(s):  
Fuel Consumption ◽  
High Efficiency ◽  
Optimization Method ◽  
Pollutant Emissions ◽  
Multi Objective Optimization ◽  
Optimization Scheme ◽  
Nsga Ii ◽  
Multi Objective ◽  
International Airport ◽  
And Control

High-efficiency taxiing for safe operations is needed by all types of aircraft in busy airports to reduce congestion and lessen fuel consumption and carbon emissions. This task is a challenge in the operation and control of the airport’s surface. Previous studies on the optimization of aircraft taxiing on airport surfaces have rarely integrated waiting constraints on the taxiway into the multi-objective optimization of taxiing time and fuel emissions. Such studies also rarely combine changes to the airport’s environment (such as airport elevation, field pressure, temperature, etc.) with the multi-objective optimization of aircraft surface taxiing. In this study, a multi-objective optimization method for aircraft taxiing on an airport surface based on the airport’s environment and traffic conflicts is proposed. This study aims to achieve a Pareto optimized taxiing scheme in terms of taxiing time, fuel consumption, and pollutant emissions. This research has the following contents: (1) Previous calculations of aircraft taxiing pathways on the airport’s surface have been based on unimpeded aircraft taxiing. Waiting on the taxiway is excluded from the multi-objective optimization of taxiing time and fuel emissions. In this study, the waiting points were selected, and the speed curve was optimized. A multi-objective optimization scheme under aircraft taxiing obstacles was thus established. (2) On this basis, the fuel flow of different aircraft engines was modified with consideration to the aforementioned environmental airport differences, and a multi-objective optimization scheme for aircraft taxiing under different operating environments was also established. (3) A multi-objective optimization of the taxiing time and fuel consumption of different aircraft types was realized by acquiring their parameters and fuel consumption indexes. A case study based on the Shanghai Pudong International Airport was also performed in the present study. The taxiway from the 35R runway to the 551# stand in the Shanghai Pudong International Airport was optimized by the non-dominant sorting genetic algorithm II (NSGA-II). The taxiing time, fuel consumption, and pollutant emissions at this airport were compared with those of the Kunming Changshui International Airport and Lhasa Gonggar International Airport, which have different airport environments. Our research conclusions will provide the operations and control departments of airports a reference to determine optimal taxiing schemes.

scholarly journals Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 220 ◽  
Author(s):  
Juan Chen ◽  
Yuxuan Yu ◽  
Qi Guo
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Traffic Congestion ◽  
Control Method ◽  
Optimization Method ◽  
Speed Limit ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.

scholarly journals A FUZZY MULTI-OBJECTIVE MODEL AND APPLICATION FOR THE DISCRETE DYNAMIC TEMPORARY FACILITIES LOCATION PLANNING PROBLEM

2016 ◽  
Vol 22 (3) ◽  
pp. 357-372 ◽  
Author(s):  
Jiuping XU ◽  
Qiurui LIU ◽  
Xiao LEI
Keyword(s):  
Computational Study ◽  
Hydroelectric Station ◽  
Optimization Method ◽  
Planning Problem ◽  
Construction Sites ◽  
Multi Objective ◽  
Objective Model ◽  
Hybrid Optimization Model ◽  
Construction Site Layout ◽  
Decision Making Model

The location of temporary facilities on construction sites is essential to the enhancement of productivity and safety, but it is complex due to the unique issues associated with construction. To positively contribute to the dynamic construction site layout planning field, this paper proposes a new fuzzy multi-objective decision making model. The proposed hybrid optimization model utilizes fuzzy numbers and logic to represent the closeness relationship between temporary facilities. The two main phases presented represent a specific advance in knowledge in through: (1) an opti­mization model that considers both uncertainty and dynamic elements; (2) the application of this optimization to a spe­cial project. A multi-objective simulated annealing-based genetic algorithm (MOSA-based GA) is proposed to solve the model and the case of Jinping-II hydroelectric station is studied to evaluate the model’s performance. The computational study was carried out to demonstrate the practicality and efficiency of the proposed optimization method. The study is applicable and useful to the profession.

Evolutionary Algorithm Based Multi-Objective Aerodynamics Optimization Method for Low Reynolds Number Airfoil

2014 ◽  
Vol 660 ◽  
pp. 487-491 ◽  
Author(s):  
Lavi R. Zuhal ◽  
Yohanes Bimo Dwianto ◽  
Pramudita Satria Palar
Keyword(s):  
Reynolds Number ◽  
Aerodynamic Characteristic ◽  
Low Reynolds Number ◽  
Optimization Method ◽  
Aerodynamic Characteristics ◽  
Population Based ◽  
Nsga Ii ◽  
Multi Objective ◽  
Low Reynolds ◽  
Low Reynolds Number Airfoil

This paper presents the development of multi-objective population-based optimization method, called Non-dominated Sorting Genetic Algorithm II (NSGA-II), to optimize the aerodynamic characteristic of a low Reynolds number airfoil. The optimization is performed by changing the shape of the airfoil to obtain geometry with the best aerodynamic characteristics. The results of the study show that the developed optimization tool, coupled with modified PARSEC parameterization, has yielded optimum airfoils with better aerodynamic characteristics compared to original airfoil. Additionally, it is found that the developed method has better performance compared to similar methods found in literature.

scholarly journals Using NSGAII to Solve Bi-objective Bed Allocation Problems

2015 ◽  
Vol 1 (3) ◽  
pp. 397
Author(s):  
Jalal A. Sultan ◽  
Ban A. Mitras ◽  
Raghad M. Jasim
Keyword(s):  
Queuing Theory ◽  
Optimization Problems ◽  
Admission Rate ◽  
Nsga Ii ◽  
Multi Objective ◽  
Decision Aiding ◽  
Proposed Model ◽  
Objective Model ◽  
Bed Allocation ◽  
Suitable Framework

The Bed Allocation Problem (BAP) is NP-complete and always high dimensional. In this paper, a bi-objective decision aiding model based on queuing theory is introduced for allocation of beds in a hospital. The problem is modeled as an M/PH/n queue. The objectives include maximizing the patient admission rate human resources, in particular, maximization of the nursing work hours. The proposed model is solved by using Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which is a very effective algorithm for solving multi-objective optimization problems and finding optimal Pareto front. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that multi-objective model was presented suitable framework for bed allocation and optimum use.

Multi-Objective Optimization of the Impeller Geometry for an Automotive Torque Converter Using RBF and NSGA-II Methods

2020 ◽  
Author(s):  
Jie Chen ◽  
Guangqiang Wu
Keyword(s):  
Great Influence ◽  
Optimization Method ◽  
Torque Converter ◽  
Numerical Characteristic ◽  
Multi Objective Optimization ◽  
Peak Efficiency ◽  
Nsga Ii ◽  
Characteristic Curves ◽  
Multi Objective ◽  
Impeller Geometry

Abstract Since impeller shape has great influence on hydraulic performance of a torque converter, a multi-objective optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) has been used to redesign the impeller geometry. Radial basis function (RBF) is attempted to establish the surrogate models for performance responses in impeller design. A sophisticated automotive torque converter case is exemplified, which demonstrates that RBF provides a better surrogate accuracy and NSGA-II is more effective than the other methods studied. To verify the optimization results, the complete numerical characteristic curves of the torque converter with the optimized impeller are compared to the validated numerical characteristic curves of the initial torque converter. The numerical results show that the stall torque ratio and peak efficiency are increased by 3.18% and 1.4%, respectively. The results indicate a reasonable improvement in the optimal design of torque converter impeller and a higher performance using the NSGA-II method.

scholarly journals Multi-Objective Model Predictive Control for Real-Time Operation of a Multi-Reservoir System

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1898 ◽  
Author(s):  
Nay Myo Lin ◽  
Xin Tian ◽  
Martine Rutten ◽  
Edo Abraham ◽  
José M. Maestre ◽  
...  
Keyword(s):  
Decision Making ◽  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Nsga Ii ◽  
Multi Objective ◽  
Reservoir System ◽  
Time Operation ◽  
Real Time Operation ◽  
Objective Model

This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

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