scholarly journals Optimization of Inspection Plan for Transformer Verification Line Under Uninterrupted State

2021 ◽  
Vol 2113 (1) ◽  
pp. 012036
Author(s):  
Shunsheng Guo ◽  
Bitao Yin ◽  
Xiang Sun ◽  
Zhao Peng ◽  
Xiaobin Tu
Keyword(s):  
Inspection Time ◽  
Delivery Mode ◽  
Multi Objective Optimization ◽  
False Detection ◽  
Nsga Ii ◽  
Inspection Method ◽  
Hybrid Delivery ◽  
Inspection Plan ◽  
The Cost ◽  
Detection Cost

Abstract At present, transformer verification line of metering centre adopts fixed cycle inspection method manually. This method requires downtime for detection, which costs a lot of time and cost. Moreover, the inspection cycle is determined based on experience and lacks rigorous basis. To solve this problem, a hybrid delivery of inspection devices is proposed to realize non-stop detection and reduce the cost of inspection time. Considering impact of cost and false detection risk on inspection cycle, a multi-objective optimization model of inspection cycle based on inspection and false detection cost is proposed. Based on NSGA-II algorithm, perturbation population is introduced to enhance the global search ability, which aims to minimize the cost of inspection and false detection. Taking the verification line’s inspection plan of the metering centre as an example. It is solved by ENSGA-II algorithm, and feasibility of hybrid delivery mode is verified, which reduced downtime by 14.58%. A more reasonable inspection cycle is obtained, inspection cost is reduced by 29.57%, and false detection cost is reduced by 6.34%. It provides a reference for the formulation of inspection plan in the actual production process.

scholarly journals Multi-objective optimization of retaining wall using genetic algorithm

2021 ◽  
Vol 8 (1-2) ◽  
pp. 58-65
Author(s):  
Filip Dodigović ◽  
Krešo Ivandić ◽  
Jasmin Jug ◽  
Krešimir Agnezović
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Retaining Wall ◽  
Silty Sand ◽  
Embedment Depth ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Definition Of ◽  
The Cost

The paper investigates the possibility of applying the genetic algorithm NSGA-II to optimize a reinforced concrete retaining wall embedded in saturated silty sand. Multi-objective constrained optimization was performed to minimize the cost, while maximizing the overdesign factors (ODF) against sliding, overturning, and soil bearing resistance. For a given change in ground elevation of 5.0 m, the width of the foundation and the embedment depth were optimized. Comparing the algorithm's performance in the cases of two-objective and three objective optimizations showed that the number of objectives significantly affects its convergence rate. It was also found that the verification of the wall against the sliding yields a lower ODF value than verifications against overturning and soil bearing capacity. Because of that, it is possible to exclude them from the definition of optimization problem. The application of the NSGA-II algorithm has been demonstrated to be an effective tool for determining the set of optimal retaining wall designs.

A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II

2012 ◽  
pp. 143-164
Author(s):  
Zahid Raza ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Turnaround Time ◽  
Computational Grid ◽  
Computational Grids ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Scheduling Model ◽  
Comparison Of The Results ◽  
The Cost

Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA – II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.

An Efficient Non-dominated Sorting Method for Evolutionary Algorithms

2008 ◽  
Vol 16 (3) ◽  
pp. 355-384 ◽  
Author(s):  
Hongbing Fang ◽  
Qian Wang ◽  
Yi-Cheng Tu ◽  
Mark F. Horstemeyer
Keyword(s):  
Evolutionary Algorithms ◽  
Divide And Conquer ◽  
Computational Time ◽  
Sorting Algorithm ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Overall Efficiency ◽  
Dominance Tree ◽  
The Cost

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN2) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.

A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II

2010 ◽  
Vol 1 (2) ◽  
pp. 74-94 ◽  
Author(s):  
Zahid Raza ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Turnaround Time ◽  
Computational Grid ◽  
Computational Grids ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Scheduling Model ◽  
Comparison Of The Results ◽  
The Cost

Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA – II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.

scholarly journals Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes

2021 ◽  
pp. 92-108
Author(s):  
Orlenys López-Pintado ◽  
Marlon Dumas ◽  
Maksym Yerokhin ◽  
Fabrizio Maria Maggi
Keyword(s):  
Resource Utilization ◽  
Cycle Time ◽  
Search Space ◽  
Hill Climbing ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Resource Allocations ◽  
Nsga Ii ◽  
Multi Objective ◽  
The Cost

AbstractThe allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

scholarly journals Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 107
Author(s):  
Rongchao Jiang ◽  
Zhenchao Jin ◽  
Dawei Liu ◽  
Dengfeng Wang
Keyword(s):  
Ride Comfort ◽  
Lightweight Design ◽  
Dynamic Performance ◽  
Vehicle Dynamic ◽  
Multi Objective Optimization ◽  
Original Design ◽  
Dynamic Simulations ◽  
Nsga Ii ◽  
Torsion Beam ◽  
Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

scholarly journals In-Plane Heatwave Thermography as Digital Inspection Technique for Fasteners in Aircraft Fuselage Panels

2020 ◽  
Vol 11 (1) ◽  
pp. 132
Author(s):  
Michael Stamm ◽  
Peter Krüger ◽  
Helge Pfeiffer ◽  
Bernd Köhler ◽  
Johan Reynaert ◽  
...  
Keyword(s):  
Inspection Time ◽  
Scanning Laser ◽  
Aviation Industry ◽  
Automated Inspection ◽  
Comparison Study ◽  
Laser Doppler Vibrometry ◽  
Inspection Method ◽  
Aircraft Fuselage ◽  
Temporal Tracking ◽  
Lock In

The inspection of fasteners in aluminium joints in the aviation industry is a time consuming and costly but mandatory task. Until today, the manual procedure with the bare eye does not allow the temporal tracking of a damaging behavior or the objective comparison between different inspections. A digital inspection method addresses both aspects while resulting in a significant inspection time reduction. The purpose of this work is to develop a digital and automated inspection method based on In-plane Heatwave Thermography and the analysis of the disturbances due to thermal irregularities in the plate-like structure. For this, a comparison study with Ultrasound Lock-in Thermography and Scanning Laser Doppler Vibrometry as well as a benchmarking of all three methods on one serviceable aircraft fuselage panel is performed. The presented data confirm the feasibility to detect and to qualify countersunk rivets and screws in aluminium aircraft fuselage panels with the discussed methods. The results suggest a fully automated inspection procedure which combines the different approaches and a study with more samples to establish thresholds indicating intact and damaged fasteners.

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