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Wyświetlanie 1-15 z 329
Tytuł:
Planning and management of aircraft maintenance using a genetic algorithm
Autorzy:
Kowalski, Mirosław
Izdebski, Mariusz
Żak, Jolanta
Gołda, Paweł
Manerowski, Jerzy
Powiązania:
https://bibliotekanauki.pl/articles/1841765.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
aircraft operation
maintenance
multi-criteria optimization
genetic algorithm
Opis:
The aim of the article was to develop a tool to support the process of planning and managing aircraft (ac) maintenance. Aircraft maintenance management has been presented for scheduled technical inspections resulting from manufacturers’ technical documentation for ac. The authors defined the problem under investigation in the form of a four-phase decisionmaking process taking into account assignment of aircraft to airports and maintenance stations, assignment of crew to maintenance points, setting the schedules, i.e. working days on which aircraft are directed to maintenance facilities. This approach to the planning and management of aircraft maintenance is a new approach, unprecedented in the literature. The authors have developed a mathematical model for aircraft maintenance planning and management in a multi-criteria approach and an optimisation tool based on the operation of a genetic algorithm. To solve the problem, a genetic algorithm was proposed. The individual steps of the algorithm construction were discussed and its effectiveness was verified using real data.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 1; 143-153
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Planning and management of aircraft maintenance using a genetic algorithm
Autorzy:
Kowalski, Mirosław
Izdebski, Mariusz
Żak, Jolanta
Gołda, Paweł
Manerowski, Jerzy
Powiązania:
https://bibliotekanauki.pl/articles/1841824.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
aircraft operation
maintenance
multi-criteria optimization
genetic algorithm
Opis:
The aim of the article was to develop a tool to support the process of planning and managing aircraft (ac) maintenance. Aircraft maintenance management has been presented for scheduled technical inspections resulting from manufacturers’ technical documentation for ac. The authors defined the problem under investigation in the form of a four-phase decisionmaking process taking into account assignment of aircraft to airports and maintenance stations, assignment of crew to maintenance points, setting the schedules, i.e. working days on which aircraft are directed to maintenance facilities. This approach to the planning and management of aircraft maintenance is a new approach, unprecedented in the literature. The authors have developed a mathematical model for aircraft maintenance planning and management in a multi-criteria approach and an optimisation tool based on the operation of a genetic algorithm. To solve the problem, a genetic algorithm was proposed. The individual steps of the algorithm construction were discussed and its effectiveness was verified using real data.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 1; 143-153
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach
Autorzy:
Yan, F.
Dridi, M.
El Moudni, A.
Powiązania:
https://bibliotekanauki.pl/articles/329874.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
autonomous vehicle
autonomous intersection management
genetic algorithm
dynamic programming
heuristics
pojazd autonomiczny
algorytm genetyczny
programowanie dynamiczne
Opis:
This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2013, 23, 1; 183-200
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm based optimized convolutional neural network for face recognition
Autorzy:
Karlupia, Namrata
Mahajan, Palak
Abrol, Pawanesh
Lehana, Parveen K.
Powiązania:
https://bibliotekanauki.pl/articles/2201023.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
convolutional neural network
genetic algorithm
deep learning
evolutionary technique
sieć neuronowa konwolucyjna
algorytm genetyczny
uczenie głębokie
technika ewolucyjna
Opis:
Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 21--31
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sensor Network Deployment Optimization for Improved Area Coverage Using a Genetic Algorithm
Autorzy:
Szklarski, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/1861643.pdf
Data publikacji:
2016
Wydawca:
Wyższa Szkoła Bezpieczeństwa Publicznego i Indywidualnego Apeiron w Krakowie
Tematy:
network of sensors
genetic algorithm
sensors deployment
area coverage
Opis:
Ensuring optimal coverage is a central objective of every sensor deployment plan. Effective monitoring of the environment helps to minimize manpower and time, while enhancing surveillance capability. In this paper, a solution for improved area coverage was presented. A lattice of a pre-defined parameter has been used as an input for the algorithm. For the purpose of the research the blanket deployment strategy has been adopted. Then, a genetic algorithm has been proposed and implemented to find an optimal solution. The proposed approach has been tested and the conclusions have been drawn. The results proved that the proposed genetic algorithm could already provide satisfactory results, usually finding only suboptimal solutions.
Źródło:
Security Dimensions; 2016, 19(19); 150-181
2353-7000
Pojawia się w:
Security Dimensions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of fractal compression of 3d images using a genetic algorithm
Autorzy:
Khanmirza, Z
Ramezani, F
Motameni, H
Powiązania:
https://bibliotekanauki.pl/articles/102068.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
stereo system
fractal compression
genetic algorithm
Opis:
3D image technologies are widely recognized as the next generation of visual presentation considering the achievement of more natural experiences. To produce such images, two cameras are placed in a bit different position. When we seek to compress such images, we need a procedure to compress two images synchronously. In this paper, a procedure is presented for a suitable compression based on fractal compression which shows that we obtain high compression rate with an appropriate image quality; however, since the proposed procedure has a low search speed, we used genetic algorithm to remove the case.
Źródło:
Advances in Science and Technology. Research Journal; 2015, 9, 26; 124-128
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A static calibration of mems 3-axis accelerometer using a genetic algorithm
Autorzy:
Marinov, Marin
Petrov, Zhivo
Powiązania:
https://bibliotekanauki.pl/articles/198726.pdf
Data publikacji:
2019
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
MEMS accelerometers
calibration
bias
genetic algorithm
akcelerometr MEMS
kalibracja
obciążenie
algorytm genetyczny
Opis:
In this paper, a procedure for MEMS accelerometer static calibration using a genetic algorithm, considering non-orthogonality was presented. The results of simulations and real accelerometer calibration are obtained showing high accuracy of parameters estimation.
Źródło:
Zeszyty Naukowe. Transport / Politechnika Śląska; 2019, 105; 157-168
0209-3324
2450-1549
Pojawia się w:
Zeszyty Naukowe. Transport / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm for vehicle routing in logistic networks with practical constraints
Autorzy:
Koloch, Grzegorz
Lewandowski, Michał
Zientara, Marcin
Grodecki, Grzegorz
Matuszak, Piotr
Kantorski, Igor
Nowackig, Adam
Powiązania:
https://bibliotekanauki.pl/articles/1981356.pdf
Data publikacji:
2021-12-30
Wydawca:
Główny Urząd Statystyczny
Tematy:
rich vehicle routing problem
brownfield
hubs and satellites
genetic algorithm
Opis:
We optimise a postal delivery problem with time and capacity constraints imposed on vehicles and nodes of the logistic network. Time constraints relate to the duration of routes, whereas capacity constraints concern technical characteristics of vehicles and postal operation outlets. We consider a method which can be applied to a brownfield scenario, in which capacities of outlets can be relaxed and prospective hubs identified. As a solution, we apply a genetic algorithm and test its properties both in small case studies and in a simulated problem instance of a larger (i.e. comparable with real-world instances) size. We show that the genetic operators we employ are capable of switching between solutions based on direct origin-to-destination routes and solutions based on transfer connections, depending on what is more beneficial in a given problem instance. Moreover, the algorithm correctly identifies cases in which volumes should be shipped directly, and those in which it is optimal to use transfer connections within a single problem instance, if an instance in question requires such a selection for optimality. The algorithm is thus suitable for determining hubs and satellite locations. All considerations presented in this paper are motivated by real-life problem instances experienced by the Polish Post, the largest postal service provider in Poland, in its daily plans of delivering postal packages, letters and pallets.
Źródło:
Przegląd Statystyczny; 2021, 68, 3; 16-40
0033-2372
Pojawia się w:
Przegląd Statystyczny
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Acoustical Assessment of Automotive Mufflers Using FEM, Neural Networks, and a Genetic Algorithm
Autorzy:
Chang, Y.-C.
Chiu, M.-C.
Wu, M.-R.
Powiązania:
https://bibliotekanauki.pl/articles/177901.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
acoustics
finite element method
genetic algorithm
muffler optimization
polynomial neural network model
Opis:
In order to enhance the acoustical performance of a traditional straight-path automobile muffler, a multi-chamber muffler having reverse paths is presented. Here, the muffler is composed of two internally parallel/extended tubes and one internally extended outlet. In addition, to prevent noise transmission from the muffler’s casing, the muffler’s shell is also lined with sound absorbing material. Because the geometry of an automotive muffler is complicated, using an analytic method to predict a muffler’s acoustical performance is difficult; therefore, COMSOL, a finite element analysis software, is adopted to estimate the automotive muffler’s sound transmission loss. However, optimizing the shape of a complicated muffler using an optimizer linked to the Finite Element Method (FEM) is time-consuming. Therefore, in order to facilitate the muffler’s optimization, a simplified mathematical model used as an objective function (or fitness function) during the optimization process is presented. Here, the objective function can be established by using Artificial Neural Networks (ANNs) in conjunction with the muffler’s design parameters and related TLs (simulated by FEM). With this, the muffler’s optimization can proceed by linking the objective function to an optimizer, a Genetic Algorithm (GA). Consequently, the discharged muffler which is optimally shaped will improve the automotive exhaust noise.
Źródło:
Archives of Acoustics; 2018, 43, 3; 517-529
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Genetic Algorithm to Minimize the Total Tardiness for M-Machine Permutation Flowshop Problems
Autorzy:
Chung, Chia-Shin
Flynn, James
Rom, Walter
Staliński, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/475000.pdf
Data publikacji:
2012
Wydawca:
Fundacja Upowszechniająca Wiedzę i Naukę Cognitione
Tematy:
genetic algorithm
scheduling
permutation flowshop
tardiness
Opis:
The m-machine, n-job, permutation flowshop problem with the total tardiness objective is a common scheduling problem, known to be NP-hard. Branch and bound, the usual approach to finding an optimal solution, experiences difficulty when n exceeds 20. Here, we develop a genetic algorithm, GA, which can handle problems with larger n. We also undertake a numerical study comparing GA with an optimal branch and bound algorithm, and various heuristic algorithms including the well known NEH algorithm and a local search heuristic LH. Extensive computational experiments indicate that LH is an effective heuristic and GA can produce noticeable improvements over LH.
Źródło:
Journal of Entrepreneurship, Management and Innovation; 2012, 8, 2; 26-43
2299-7075
2299-7326
Pojawia się w:
Journal of Entrepreneurship, Management and Innovation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A robust algorithm to solve the signal setting problem considering different traffic assignment approaches
Autorzy:
Adacher, L.
Gemma, A.
Powiązania:
https://bibliotekanauki.pl/articles/330229.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
genetic algorithm
surrogate method
traffic signal synchronization
traffic assignment
simulation model
algorytm genetyczny
metoda zastępcza
synchronizacja sygnału ruchu
model symulacji
Opis:
In this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers’ comfort, fuel consumption efficiency, pollution and safety. In a traffic network, the signal control strategy affects the travel time on the roads and influences drivers’ route choice behavior. The paper presents an algorithm for signal setting optimization of signalized junctions in a congested road network. The objective function used in this work is a weighted sum of delays caused by the signalized intersections. We propose an iterative procedure to solve the problem by alternately updating signal settings based on fixed flows and traffic assignment based on fixed signal settings. To show the robustness of our method, we consider two different assignment methods: one based on user equilibrium assignment, well established in the literature as well as in practice, and the other based on a platoon simulation model with vehicular flow propagation and spill-back. Our optimization algorithm is also compared with others well known in the literature for this problem. The surrogate method (SM), particle swarm optimization (PSO) and the genetic algorithm (GA) are compared for a combined problem of global optimization of signal settings and traffic assignment (GOSSTA). Numerical experiments on a real test network are reported.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2017, 27, 4; 815-826
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm-based approach for flexible job shop rescheduling problem with machine failure interference
Autorzy:
Liang, Zhongyuan
Zhong, Peisi
Zhang, Chao
Yang, Wenlei
Xiong, Wei
Yang, Shihao
Meng, Jing
Powiązania:
https://bibliotekanauki.pl/articles/27320976.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
genetic algorithm
rescheduling
machine failure
flexible job shop scheduling
Opis:
Rescheduling is the guarantee to maintain the reliable operation of production system process. In production system, the original scheduling scheme cannot be carried out when machine breaks down. It is necessary to transfer the production tasks in the failure cycle and replan the production path to ensure that the production tasks are completed on time and maintain the stability of production system. To address this issue, in this paper, we studied the event-driven rescheduling policy in dynamic environment, and established the usage rules of right-shift rescheduling and complete rescheduling based on the type of interference events. And then, we proposed the rescheduling decision method based on genetic algorithm for solving flexible job shop scheduling problem with machine fault interference. In addition, we extended the "mk" series of instances by introducing the machine fault interference information. The solution data show that the complete rescheduling method can respond effectively to the rescheduling of flexible job shop scheduling problem with machine failure interference.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 4; art. no. 171784
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm and B&B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy
Autorzy:
Sadiqi, Assia
El Abbassi, Ikram
El Barkany, Abdellah
Darcherif, Moumen
El Biyaali, Ahmed
Powiązania:
https://bibliotekanauki.pl/articles/1841396.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
scheduling
maintenance
genetic algorithm
branch
bound
MILP
modeling
optimization
CPLEX
Python
Opis:
The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient.
Źródło:
Management and Production Engineering Review; 2020, 11, 4; 138-148
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A generalized varying-domain optimization method for fuzzy goal programming with priorities based on a genetic algorithm
Autorzy:
Li, S. Y.
Hu, C. F.
Teng, C. J.
Powiązania:
https://bibliotekanauki.pl/articles/970454.pdf
Data publikacji:
2004
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
rozmyte programowanie celowe
priorytet
algorytm genetyczny
fuzzy goal programming
priorities
SQP
genetic algorithm
GENOCOP III
Opis:
This paper proposes a generalized domain optimization method for fuzzy goal programming with different priorities. According to the three possible styles of the objective function, the domain optimization method and its generalization are correspondingly proposed. This method can generate the results consistent with the decision-maker's priority expectations, according to which the goal with higher priority may have higher level of satisfaction. However, the reformulated optimization problem may be nonconvex for the reason of the nature of the original problem and the introduction of the varying-domain optimization method. It is possible to obtain a local optimal solution for nonconvex programming by the SQP algorithm. In order to get the global solution of the new programming problem, the co-evolutionary genetic algorithm, called GENOCOP III, is used instead of the SQP method. In this way the decision-maker can get. the optimum of the optimization problem. We demonstrate the power of this proposed method based on genetic algorithm by illustrative examples.
Źródło:
Control and Cybernetics; 2004, 33, 4; 633-652
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An evolutionary algorithm determining a defuzzyfication functional
Autorzy:
Kosiński, W.
Markowska-Kaczmar, U.
Powiązania:
https://bibliotekanauki.pl/articles/1943273.pdf
Data publikacji:
2007
Wydawca:
Politechnika Gdańska
Tematy:
ordered fuzzy numbers
defuzzyfication
genetic algorithm
Opis:
Order fuzzy numbers are defined that make it possible to deal with fuzzy inputs quantitatively, exactly in the same way as with real numbers, together with four algebraic operations. An approximation formula is given for a defuzzyfication functional that plays the main role when dealing with fuzzy controllers and fuzzy inference systems. A dedicated evolutionary algorithm is presented in order to determine the form of a functional when a training set is given. The form of a genotype composed of three types of chromosomes and the fitness function are given and Genetic operators are proposed.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2007, 11, 1-2; 47-58
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 329

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