scholarly journals Application Research on the Maintainability Allocation Method of a Certain Shooter Seat

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liying Jin ◽  
Wensheng Wang ◽  
HouYong Shu ◽  
Xuemei Ma ◽  
Chenxing Liang ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Allocation Scheme ◽  
Application Research ◽  
Product Lifecycle ◽  
Nsga Ii ◽  
Trade Off ◽  
Allocation Method ◽  
Mean Time

In view of the traditional maintainability allocation method for a certain shooter seat for maintainability allocation did not consider the lifecycle expense problem, the improved NSGA-II algorithm (iNSGA-II, for short) is adopted to establish a multiobjective comprehensive trade-off model for a certain shooter seat product lifecycle maintenance-related expenses and mean time to repair (MTTR, for short) and construct multiobjective optimization problem. The experimental results show that the Pareto optimal solution effectively solves the limitation of the traditional maintainability allocation method and then provides a basis for a certain shooter seat to obtain a reasonable maintainability allocation scheme. The superiority of the iNSGA-II algorithm to optimize the maintainability allocation of a certain shooter seat was verified by comparing it with the traditional maintainability allocation method.

An Improved Initial Population Strategy for Compliant Mechanism Designs Using Evolutionary Optimization

2008 ◽  
Author(s):  
Deepak Sharma ◽  
Kalyanmoy Deb ◽  
N. N. Kishore
Keyword(s):  
Local Search ◽  
Optimization Problem ◽  
Optimization Procedure ◽  
Evolutionary Optimization ◽  
Initial Population ◽  
Nsga Ii ◽  
Trade Off ◽  
Continuum Structures ◽  
Population Strategy ◽  
Straight Lines

In this paper, an improved initial random population strategy using a binary (0–1) representation of continuum structures is developed for evolving the topologies of path generating complaint mechanism. It helps the evolutionary optimization procedure to start with the structures which are free from impracticalities such as ‘checker-board’ pattern and disconnected ‘floating’ material. For generating an improved initial population, intermediate points are created randomly and the support, loading and output regions of a structure are connected through these intermediate points by straight lines. Thereafter, a material is assigned to those grids only where these straight lines pass. In the present study, single and two-objective optimization problems are solved using a local search based evolutionary optimization (NSGA-II) procedure. The single objective optimization problem is formulated by minimizing the weight of structure and a two-objective optimization problem deals with the simultaneous minimization of weight and input energy supplied to the structure. In both cases, an optimization problem is subjected to constraints limiting the allowed deviation at each precision point of a prescribed path so that the task of generating a user-defined path is accomplished and limiting the maximum stress to be within the allowable strength of material. Non-dominated solutions obtained after NSGA-II run are further improved by a local search procedure. Motivation behind the two-objective study is to find the trade-off optimal solutions so that diverse non-dominated topologies of complaint mechanism can be evolved in one run of optimization procedure. The obtained results of two-objective optimization study is compared with an usual study in which material in each grid is assigned at random for creating an initial population of continuum structures. Due to the use of improved initial population, the obtained non-dominated solutions outperform that of the usual study. Different shapes and nature of connectivity of the members of support, loading and output regions of the non-dominated solutions are evolved which will allow the designers to understand the topological changes which made the trade-off and will be helpful in choosing a particular solution for practice.

scholarly journals Visualizing Exact and Approximated 3D Empirical Attainment Functions

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Tea Tušar ◽  
Bogdan Filipič
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Low Cost ◽  
Optimal Solution ◽  
Maximum Intensity Projection ◽  
Maximum Intensity ◽  
Engineering Optimization ◽  
Trade Off ◽  
Visualization Techniques ◽  
Casting Optimization

Most real-world engineering optimization problems are inherently multiobjective, for example, searching for trade-off solutions of high quality and low cost. As no single optimal solution exists for such problems, multiobjective optimizers provide sets of optimal (or near-optimal) trade-off solutions to choose from. The empirical attainment function (EAF) is a powerful tool that can be used to analyze and compare the performance of these optimizers. While the visualization of EAFs is rather straightforward in two objectives, the three-objective case presents a great challenge as we need to visualize a large number of 3D cuboids. This paper addresses the visualization of exact as well as approximated 3D EAF values and differences in these values provided by two competing multiobjective optimizers. We show that the exact EAFs can be visualized using slicing and maximum intensity projection (MIP), while the approximated EAFs can be visualized using slicing, MIP, and direct volume rendering. In addition, the paper demonstrates the use of the proposed visualization techniques on a steel casting optimization problem.

On Pareto Optimal Solution for Production and Maintenance Jobs Scheduling Problem in a Job Shop and Flow Shop with an Immune Algorithm

2014 ◽  
Vol 1036 ◽  
pp. 875-880 ◽  
Author(s):  
Iwona Paprocka ◽  
Wojciech M. Kempa ◽  
Krzysztof Kalinowski ◽  
Cezary Grabowik
Keyword(s):  
Job Shop ◽  
Flow Shop ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Immune Algorithm ◽  
Flow Shop Scheduling ◽  
Pareto Optimal ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Mean Time

In the paper a job shop and flow shop scheduling problems with availability time constraint for maintenance are considered. Unavailability time due to maintenance is estimated basing on information about predicted Mean Time To Failure/To First Failure and Mean Time of Repair of a machine. Maintenance actions are introduced into a schedule to keep the machine available in a good operation condition. The efficiency of predictive schedules (PS) is evaluated using criteria: makespan, flow time, total tardiness, idle time. The efficiency of reactive schedules (RSs) is evaluated using criteria: solution and quality robustness. For basic schedule generation Multi Objective Immune Algorithm is applied. For predictive scheduling Minimal Impact of Disturbed Operation on the Schedule is applied. After doing computer simulations for the job shop scheduling problem following question arises: do dominated Pareto optimal basic schedules achieve better PSs? Although a single Pareto-optimal solution is achieved on Pareto-optimal frontier three different schedules have the same quality in the flow shop scheduling problem. The question is: which schedule is the most robust solution?

Game Theory Approach in Multicriteria Optimization of Function Generating Mechanisms

1979 ◽  
Vol 101 (3) ◽  
pp. 398-406 ◽  
Author(s):  
S. S. Rao ◽  
S. K. Hati
Keyword(s):  
Game Theory ◽  
Multicriteria Optimization ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Manufacturing Cost ◽  
Pareto Optimal ◽  
Theory Approach ◽  
Trade Off ◽  
Structural Error ◽  
And Behavior

The multicriteria optimization of function generating mechanisms is considered using game theory. Specifically the design of spherical four-revolute function generating mechanisms, with the objective of minimizing structural error, mechanical error and a measure of manufacturing cost, is considered. Both the side and behavior constraints are included in the formulation of the probabilistic model of the system. The solution concepts of game theory are translated in finding an optimal trade-off between the three objectives. A method of obtaining the desired Pareto-optimal solution, which maximizes a specified supercriterion, is discussed. The proposed method of synthesis is illustrated by considering the generation of the function y = sin x.

Simulation Optimization of Multi-Objective Flexible Job Shop Scheduling

2013 ◽  
Vol 365-366 ◽  
pp. 602-605
Author(s):  
Gui Cong Wang ◽  
Chuan Peng Li ◽  
Huan Yong Cui
Keyword(s):  
Simulation Model ◽  
Job Shop ◽  
Simulation Optimization ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Pareto Optimal ◽  
Flexible Job Shop ◽  
Final Solution ◽  
Nsga Ii ◽  
Multi Objective

Current scheduling approach for multiple objective flexible job shop problem (FJSP) cannot construct a precise scheduling model and obtain a satisfactory scheduling result at the same time. To deal with this problem, a simulation optimization scheduling approach was presented which was composed of two basic modules: the Fast non-dominated Sorting Genetic Algorithm (NSGA-II) module and Witness simulation module. Firstly, a multi-objective mathematical model was found for FJSP and NSGA-II was applied to solve. Then, a set of Pareto optimal solution was obtained by NSGA-II module. In order to select the final solution from the Pareto optimal solution for FJSP, the simulation model was set up by Witness, every Pareto solution was as input for simulation model. Finally, the final solution can be selected according other performance indicators.

Hybrid Multi-objective Optimization Approach in Water Flooding

2021 ◽  
pp. 1-22
Author(s):  
Mohammed Al-Aghbari ◽  
Ashish M. Gujarathi ◽  
Majid Al-Wadhahi ◽  
Nirupam Chakraborti
Keyword(s):  
Neural Network ◽  
Water Injection ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Data Driven ◽  
Water Flooding ◽  
Real Field ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Evolutionary Neural Network

Abstract Non-dominated Sorting Genetic Algorithm, second version (NSGA-II) is used as a stochastic optimization technique successfully in different engineering applications. In this study, a data-driven optimization strategy based upon evolutionary neural network algorithm (EvoNN) is developed for providing input into NSGA-II optimization. Evolutionary neural-network data-driven model is built and trained using initial solutions generated by NSGA-II optimization coupled with the reservoir simulation model. Evolutionary optimization incorporated in the EvoNN strategy is applied in the trained data-driven model to generate the Pareto optimal solution, which is then used as a guiding input into NSGA-II optimization. The described method is applied in two case studies (i.e. Brugge field model & water injection pattern model). The Pareto optimal solutions obtained with data-driven model guided NSGA-II in both models show improvement in convergence and diversity of the solution. The convergence to the Pareto optimal solution has improved by 9% for case-1 (i.e. Brugge field) and by 43% for case-2 (i.e. water injection pattern model). In addition, the Pareto optimal solution obtained by the proposed hybridization has shown improvement in the water oil ratio (WOR) up to 6% in the Brugge field and up to 97% in the water injection pattern model. This improvement can lead to wide applications in using evolutionary optimizations in real field simulation models at acceptable computation time.

Sign in / Sign up

Export Citation Format

Share Document