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Tytuł:
A logistic optimization for the vehicle routing problem through a case study in the food industry
Autorzy:
Akpinar, Muhammet Enes
Powiązania:
https://bibliotekanauki.pl/articles/1835487.pdf
Data publikacji:
2021
Wydawca:
Wyższa Szkoła Logistyki
Tematy:
vehicle routing problem
time windows
optimization
metaheuristic algorithm
genetic algorithm
trasa pojazdu
okna czasowe
optymalizacja
algorytm metaheurystyczny
algorytm genetyczny
Opis:
In this study, the food delivery problem faced by a food company is discussed. There are seven different regions where the company serves food and a certain number of customers in each region. The time of requesting food for each customer varies according to the shift situation. This type of problem is referred to as a vehicle routing problem with time windows in the literature and the main aim of the study is to minimize the total travel distance of the vehicles. The second aim is to determine which vehicle will follow which route in the region by using the least amount of vehicle according to the desired mealtime. Methods: In this study, genetic algorithm methodology is used for the solution of the problem. Metaheuristic algorithms are used for problems that contain multiple combinations and cannot be solved in a reasonable time. Thus in this study, a solution to this problem in a reasonable time is obtained by using the genetic algorithm method. The advantage of this method is to find the most appropriate solution by trying possible solutions with a certain number of populations. Results: Different population sizes are considered in the study. 1000 iterations are made for each population. According to the genetic algorithm results, the best result is obtained in the lowest population size. The total distance has been shortened by about 14% with this method. Besides, the number of vehicles in each region and which vehicle will serve to whom has also been determined. This study, which is a real-life application, has provided serious profitability to the food company even from this region alone. Besides, there have been improvements at different rates in each of the seven regions. Customers' ability to receive service at any time has maximized customer satisfaction and increased the ability to work in the long term. Conclusions: The method and results used in the study were positive for the food company. However, the metaheuristic algorithm used in this study does not guarantee an optimal result. Therefore, mathematical models or simulation models can be considered in terms of future studies. Besides, in addition to the time windows problem, the pickup problem can also be taken into account and different solution proposals can be developed.
Źródło:
LogForum; 2021, 17, 3; 387-397
1734-459X
Pojawia się w:
LogForum
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cross‐Comparison of Evolutionary Algorithms for Optimizing Design of Sustainable Supply Chain Network under Disruption Risks
Autorzy:
Al-Zuheri, Atiya
Powiązania:
https://bibliotekanauki.pl/articles/2023790.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
comparison
genetic algorithm
particle swarm optimization
sustainable supply chain design
disruption risk
porównanie
algorytm genetyczny
optymalizacja rojem cząstek
projektowanie zrównoważonego łańcucha dostaw
ryzyko zakłóceń
Opis:
Optimization of a sustainable supply chain network design (SSCND) is a complex decision-making process which can be done by the optimal determination of a set of decisions and constraints such as the selection of suppliers, transportation-related facilities and distribution centres. Different optimization techniques have been applied to handle various SSCND problems. Meta- heuristic algorithms are developed from these techniques that are commonly used to solving supply chain related problems. Among them, Genetic algorithms (GA) and particle swarm optimization (PSO) are implemented as optimization solvers to obtain supply network design decisions. This paper aims to compare the performance of these two evolutionary algorithms in optimizing such problems by minimizing the total cost that the system faces to potential disruption risks. The mechanism and implementation of these two evolutionary algorithms is presented in this paper. Also, using an optimization considers ordering, purchasing, inventory, transportation, and carbon tax cost, a numerical real-life case study is presented to demonstrate the validity of the effectiveness of these algorithms. A comparative study for the algorithms performance has been carried out based on the quality of the obtained solution and the results indicate that the GA performs better than PSO in finding lower-cost solution to the addressed SSCND problem. Despite a lot of research literature being done regarding these two algorithms in solving problems of SCND, few studies have compared the optimization performance between GA and PSO, especially the design of sustainable systems under risk disruptions.
Źródło:
Advances in Science and Technology. Research Journal; 2021, 15, 4; 342-351
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of the process of restoring the continuity of the WDS based on the matrix and genetic algorithm approach
Autorzy:
Antonowicz, Ariel
Urbaniak, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/2173692.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
WNTR
Water Network Tool for Resilience
aggregation of failures
water distribution system
EPANET Solver
Graph Searching Algorithms
genetic algorithm
optimization
post-disaster events
agregacja awarii
system dystrybucji wody
EPANET
algorytm wyszukiwania grafów
algorytm genetyczny
optymalizacja
wydarzenia po katastrofie
Opis:
The article discusses an example of the use of graph search algorithms with trace of water analysis and aggregation of failures in the occurrence of a large number of failures in the Water Supply System (WSS). In the event of a catastrophic situation, based on the Water Distribution System (WDS) network model, information about detected failures, the condition and location of valves, the number of repair teams, criticality analysis, the coefficient of prioritization of individual network elements, and selected objective function, the algorithm proposes the order of repairing the failures should be analyzed. The approach proposed by the authors of the article assumes the selection of the following objective function: minimizing the time of lack of access to drinking water (with or without prioritization) and minimizing failure repair time (with or without failure aggregation). The algorithm was tested on three different water networks (small, medium, and large numbers of nodes) and three different scenarios (different numbers of failures and valves in the water network) for each selected water network. The results were compared to a valve designation approach for closure using an adjacency matrix and a Strategic Valve Management Model (SVMM).
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 4; art. no. e141594
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
Autorzy:
Arif, Salah
Djellal, Adel
Djebbari, Nawel
Belhaoues, Saber
Touati, Hassen
Guellati, Fatma Zohra
Bensouilah, Mourad
Powiązania:
https://bibliotekanauki.pl/articles/2174666.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
microcystis cell density
Multiple Linear Regression
Support Vector Machine
Particle Swarm Optimization
Genetic Algorithm
Bird Swarm Algorithm
Opis:
Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Travel management optimization based on air pollution condition using Markov decision process and genetic algorithm (case study: Shiraz city)
Autorzy:
Bagheri, Mohammad
Ghafourian, Hossein
Kashefiolasl, Morteza
Pour, Mohammad Taghi Sadati
Rabbani, Mohammad
Powiązania:
https://bibliotekanauki.pl/articles/223520.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
air pollution
dynamic optimization
genetic algorithm
Markov decision-making process
zarządzanie transportem
optymalizacja
zanieczyszczenie powietrza
algorytm genetyczny
proces decyzyjny Markowa
Opis:
Currently, air pollution and energy consumption are the main issues in the transportation area in large urban cities. In these cities, most people choose their transportation mode according to corresponding utility including traveller's and trip’s characteristics. Also, there is no effective solution in terms of population growth, urban space, and transportation demands, so it is essential to optimize systematically travel demands in the real network of roads in urban areas, especially in congested areas. Travel Demand Management (TDM) is one of the well-known ways to solve these problems. TDM defined as a strategy that aims to maximize the efficiency of the urban transport system by granting certain privileges for public transportation modes, Enforcement on the private car traffic prohibition in specific places or times, increase in the cost of using certain facilities like parking in congested areas. Network pricing is one of the most effective methods of managing transportation demands for reducing traffic and controlling air pollution especially in the crowded parts of downtown. A little paper may exist that optimize urban transportations in busy parts of cities with combined Markov decision making processes with reward and evolutionary-based algorithms and simultaneously considering customers’ and trip’s characteristics. Therefore, we present a new network traffic management for urban cities that optimizes a multi-objective function that related to the expected value of the Markov decision system’s reward using the Genetic Algorithm. The planned Shiraz city is taken as a benchmark for evaluating the performance of the proposed approach. At first, an analysis is also performed on the impact of the toll levels on the variation of the user and operator cost components, respectively. After choosing suitable values for the network parameters, simulation of the Markov decision process and GA is dynamically performed, then the optimal decision for the Markov decision process in terms of total reward is obtained. The results illustrate that the proposed cordon pricing has significant improvement in performance for all seasons including spring, autumn, and winter.
Źródło:
Archives of Transport; 2020, 53, 1; 89-102
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem
Autorzy:
Białogłowski, M.
Staniaszek, M.
Laskowski, W.
Grudniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/91276.pdf
Data publikacji:
2018
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Tematy:
traveling salesman problem
Nearest Neighbor
Monte Carlo
Simulated Annealing
Genetic Algorithm
particle swarm optimization (PSO)
Opis:
We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.
Źródło:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2018, 12, 19; 7-24
1896-396X
2082-8349
Pojawia się w:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Passivity-based optimal control of discrete-time nonlinear systems
Autorzy:
Binazadeh, T.
Shafiei, M. H.
Powiązania:
https://bibliotekanauki.pl/articles/205917.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
nonlinear discrete-time systems optimal passivity-based control
genetic optimization algorithm
Opis:
In this paper, a passivity-based optimal controlmethod for a broad class of nonlinear discrete-time systems is proposed. The resulting control law is a static output feedback law which is practically preferred with respect to the state feedback law and is simple to implement. The control law has a general structure with adjustable parameters which are tuned, using an optimization method (genetic algorithm), to minimize an arbitrary cost function. By choosing this cost function it is possible to shape the transient response of the closed-loop system, as it is desirable. An illustrative ex ample shows the effectiveness of the proposed approach.
Źródło:
Control and Cybernetics; 2013, 42, 3; 627-637
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Solving scheduling problems with integrated online sustainability observation using heuristic optimization
Autorzy:
Burduk, Anna
Musiał, Kamil
Balashov, Artem
Batako, Andre
Safonyk, Andrii
Powiązania:
https://bibliotekanauki.pl/articles/2173719.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
production scheduling
sustainable development
genetic algorithm
meta-heuristics
intelligent optimization methods of production systems
tabu search
harmonogramowanie produkcji
zrównoważony rozwój
algorytm genetyczny
przeszukiwanie tabu
metaheurystyki
inteligentne metody optymalizacji systemów produkcyjnych
Opis:
The paper deals with the issue of production scheduling for various types of employees in a large manufacturing company where the decision-making process was based on a human factor and the foreman’s know-how, which was error-prone. Modern production processes are getting more and more complex. A company that wants to be competitive on the market must consider many factors. Relying only on human factors is not efficient at all. The presented work has the objective of developing a new employee scheduling system that might be considered a particular case of the job shop problem from the set of the employee scheduling problems. The Neuro-Tabu Search algorithm and the data gathered by manufacturing sensors and process controls are used to remotely inspect machine condition and sustainability as well as for preventive maintenance. They were used to build production schedules. The construction of the Neuro-Tabu Search algorithm combines the Tabu Search algorithm, one of the most effective methods of constructing heuristic algorithms for scheduling problems, and a self-organizing neural network that further improves the prohibition mechanism of the Tabu Search algorithm. Additionally, in the paper, sustainability with the use of Industry 4.0 is considered. That would make it possible to minimize the costs of employees’ work and the cost of the overall production process. Solving the optimization problem offered by Neuro-Tabu Search algorithm and real-time data shows a new way of production management.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 6; art. no. e143830
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multiobjective geometry optimization of bldc motor using an evolutionary algorithm
Wielokryterialna optymalizacja geometrii bezszczotkowego silnika prądu stałego z wykorzystaniem algorytmu genetycznego
Autorzy:
Caramia, R
Piotuch, R.
Pałka, R.
Powiązania:
https://bibliotekanauki.pl/articles/1368136.pdf
Data publikacji:
2013
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Napędów i Maszyn Elektrycznych Komel
Tematy:
synchronous motor
optimization
genetic algorithm
Pareto Front
silnik synchroniczny
optymalizacja
algorytm genetyczny
front Pareto
Opis:
W pracy przedstawiono metodę optymalizacji bezszczotkowego silnika prądu stałego z 4 czteroma biegunami i 24 żłobkami. W szczególności praca koncentruje się na optymalizacji wielokryterialnej z wykorzystaniem algorytmów genetycznych (Optimizaton Toolbox) realizowanych w środowisku Matlab, sprzęgniętym ze środowiskiem Maxwell 14. Matlab został użyty do przeprowadzenia procesu optymalizacji oraz przetwarzania danych liczbowych. Środowisko Maxwell zostało użyte do tworzenia geometrii oraz do przeprowadzenia obliczeń Metodą Elementów Skończonych. Celem pracy była maksymalizacja wartości momentu maksymalnego silnika przy minimalnej masie silnika. Wyniki badań symulacyjnych wykonanych dla modelu 2D pokazały, że sprzęgnięcie obu pakietów obliczeniowych jest możliwe i daje satysfakcjonujące rezultaty. Wykorzystując prosty algorytm genetyczny uzyskano 25% wzrost wartości średniej momentu silnika przy spadku masy silnika o 14%. Otrzymane wyniki zostały poddane weryfikacji z wykorzystaniem modelu 3D.
This paper presents a methodology for the optimization of a Brush Less Direct Current motor (BLDC) with 4 poles and 24 slots. In particular, it is focused on a multiobjective optimization using a genetic algorithm developed in Matlab optimization Toolbox, that is coupled with Maxwell 14. The first one has been used for the optimization and the post-processing of the data, the second one for the Finite Element (FE) analysis and for the geometry creation. Aim of the optimization was to maximize the maximum torque value and minimize the mass of a motor. The simulation results of a 2D model showed that the coupling was possible and give satisfactory results. Using simple genetic algorithm it was possible to increase the average torque value of 25% and lower the mass of the main part of the motor of 14%. Obtained results were verified using a 3D model.
Źródło:
Maszyny Elektryczne: zeszyty problemowe; 2013, 3, 100/1; 89-94
0239-3646
2084-5618
Pojawia się w:
Maszyny Elektryczne: zeszyty problemowe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Generalized route planning approach for hazardous materials transportation with equity consideration
Autorzy:
Chai, H.
He, R.-C.
Jia, X.-yan
Ma, Ch.-x
Dai, C.-jie
Powiązania:
https://bibliotekanauki.pl/articles/223759.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
hazardous materials transportation
transportation
route optimization
risk equity
multi-objective optimization
NSGA-II algorithm
genetic algorithm
transport materiałów niebezpiecznych
materiały niebezpieczne
optymalizacja trasy
kapitał własny
optymalizacja wielokryterialna
algorytm NSGA-II
algorytm genetyczny
Opis:
Hazardous materials transportation should consider risk equity and transportation risk and cost. In the hazardous materials transportation process, we consider risk equity as an important condition in optimizing vehicle routing for the long-term transport of hazardous materials between single or multiple origin-destination pairs (O-D) to reduce the distribution difference of hazardous materials transportation risk over populated areas. First, a risk equity evaluation scheme is proposed to reflect the risk difference among the areas. The evaluation scheme uses standard deviation to measure the risk differences among populated areas. Second, a risk distribution equity model is proposed to decrease the risk difference among populated areas by adjusting the path frequency between O-D pairs for hazardous materials transportation. The model is converted into two sub models to facilitate decision-making, and an algorithm is provided for each sub model. Finally, we design a numerical example to verify the accuracy and rationality of the model and algorithm. The numerical example shows that the proposed model is essential and feasible for reducing the complexity and increasing the portability of the transportation process.
Źródło:
Archives of Transport; 2018, 46, 2; 33-46
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
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:
Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method
Autorzy:
Chang, Y.-C.
Chiu, M.-C.
Xie, J.-L.
Powiązania:
https://bibliotekanauki.pl/articles/178126.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
finite element method
polynomial neural network model
genetic algorithm
group method of data handling
reciprocating compressor
optimization
Opis:
Industry often utilizes acoustical hoods to block noise emitted from reciprocating compressors. However, the hoods are large and bulky. Therefore, to diminish the size of the compressor, a compact discharge muffler linked to the compressor outlet is considered. Because the geometry of a reciprocating compressor is irregular, COMSOL, a finite element analysis software, is adopted. In order to explore the acoustical performance, a mathematical model is established using a finite element method via the COMSOL commercialized package. Additionally, to facilitate the shape optimization of the muffler, a polynomial neural network model is adopted to serve as an objective function; also, a Genetic Algorithm (GA) is linked to the OBJ function. During the optimization, various noise abatement strategies such as a reverse expansion chamber at the outlet of the discharge muffler and an inner extended tube inside the discharge muffler, will be assessed by using the artificial neural network in conjunction with the GA optimizer. Consequently, the discharge muffler that is optimally shaped will decrease the noise of the reciprocating compressor.
Źródło:
Archives of Acoustics; 2017, 42, 2; 189-197
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Assessment of High-Order-Mode Analysis and Shape Optimization of Expansion Chamber Mufflers
Autorzy:
Chiu, M. C.
Chang, Y. C.
Powiązania:
https://bibliotekanauki.pl/articles/178079.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
higher order wave
eigenfunction
optimization
genetic algorithm
Opis:
A substantial quantity of research on muffler design has been restricted to a low frequency range using the plane wave theory. Based on this theory, which is a one-dimensional wave, no higher order wave has been considered. This has resulted in underestimating acoustical performances at higher frequencies when doing muffler analysis via the plane wave model. To overcome the above drawbacks, researchers have assessed a three-dimensional wave propagating for a simple expansion chamber muffler. Therefore, the acoustic effect of a higher order wave (a high frequency wave) is considered here. Unfortunately, there has been scant research on expansion chamber mufflers equipped with baffle plates that enhance noise elimination using a higher-order-mode analysis. Also, space-constrained conditions of industrial muffler designs have never been properly addressed. So, in order to improve the acoustical performance of an expansion chamber muffler within a constrained space, the optimization of an expansion chamber muffler hybridized with multiple baffle plates will be assessed. In this paper, the acoustical model of the expansion chamber muffler will be established by assuming that it is a rigid rectangular tube driven by a piston along the tube wall. Using an eigenfunction (higher- order-mode analysis), a four-pole system matrix for evaluating acoustic performance (STL) is derived. To improve the acoustic performance of the expansion chamber muffler, three kinds of expansion chamber mufflers (KA-KC) with different acoustic mechanisms are introduced and optimized for a targeted tone using a genetic algorithm (GA). Before the optimization process is performed, the higher-order-mode mathematical models of three expansion chamber mufflers (A-C) with various allocations of inlets/outlets and various chambers are also confirmed for accuracy. Results reveal that the STL of the expansion chamber mufflers at the targeted tone has been largely improved and the acoustic performance of a reverse expansion chamber muffler is more efficient than that of a straight expansion chamber muffler. Moreover, the STL of the expansion chamber mufflers will increase as the number of the chambers that separate with baffles increases.
Źródło:
Archives of Acoustics; 2014, 39, 4; 489-499
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multicriteria optimization method of LNG distribution
Autorzy:
Chłopińska, E.
Gucma, M.
Powiązania:
https://bibliotekanauki.pl/articles/117110.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
cargo handling
Liquefied Natural Gas (LNG)
Multicriteria Optimization Method
LNG Distribution
Marine Diesel Oil (MDO)
Heavy Fuel Oil (HFO)
Vector Evaluated Genetic Algorithms (VEGA)
Multi-Objective Genetic Algorithm (MOGA)
Opis:
Liquefied Natural Gas (LNG) is considered as a realistic substation of marine fuel in 21 century. Solution of building new engines or converting diesels into gas fueled propulsion meets the stringent international emission regulations. For HFO (heavy fuel oil) or MDO (marine diesel oil) propelled vessels, operation of bunkering is relatively wide known and simple. Its due to the fact that fuel itself doesn’t require high standards of handling. Where for LNG as a fuel its very demanding process – it evaporates and requires either consuming by bunker vessel or reliquefication. Distribution of such bunker is becoming multidimensional problem with time and space constrains. The objective of the article is to review the methods of optimization using genetic algorithms for a model of LNG distribution. In particular, there will be considered methods of solving problems with many boundry criteria whose objective functions are contradictory. Methods used for solving the majority of problems are can prevent the simultaneous optimization of the examined objectives, e.g. the minimisation of costs or distance covered, or the maximisation of profits or efficiency etc. Here the standard genetic algorithms are suitable for solving multi-criteria problems by using functions producing a diversity of results depending on the adopted approach.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 2; 493-497
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ligament-based spine-segment mechanisms
Autorzy:
Ciszkiewicz, A.
Milewski, G.
Powiązania:
https://bibliotekanauki.pl/articles/202123.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
intervertebral joint
optimization
parameters estimation
genetic algorithm
elastostatic analysis
staw międzykręgowy
optymalizacja
szacowanie parametrów
algorytm genetyczny
analiza elastostatyczna
Opis:
Nowadays, a growing interest in spine-segment mechanisms for humanoid robots can be observed. The ones currently available are mostly inspired by an intervertebral joint but rarely use its structure and behaviour as input data. The aim of this study was to propose and verify an approach to spine-segment mechanisms synthesis, in which the mechanisms were obtained directly from a ligament system of the intervertebral joint through numerical optimization. The approach consists of two independent optimization procedures performed with genetic algorithm. The first one searches for the optimal structure, while the second estimates its geometrical and stiffness parameters. The mechanisms are rated by their ability to reproduce the static behaviour of the joint in selected aspects. Both procedures use the lumbar L4-L5 intervertebral joint reference data. The approach was tested in two numerical scenarios. It was possible to obtain a mechanism with 7 flexible linear legs that accurately emulated the elastostatic behaviour of the intervertebral joint under moment loads. The results prove that the proposed method is feasible and worth exploring. It may be employed in design of bioinspired joints for use in humanoid robots and can also serve as an initial step in the design of prosthetic and orthotic devices for a human spine.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 5; 705-712
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 110

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