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Tytuł:
Application of genetic algorithms to the traveling salesman problem
Autorzy:
Sikora, Tomasz
Gryglewicz-Kacerka, Wanda
Powiązania:
https://bibliotekanauki.pl/articles/30148246.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
evolutionary algorithms
genetic algorithms
traveling salesman problem
TSP
Opis:
The purpose of this paper was to investigate in practice the possibility of using evolutionary algorithms to solve the traveling salesman problem on a real example. The goal was achieved by developing an original implementation of the evolutionary algorithm in Python, and by preparing an example of the traveling salesman problem in the form of a directed graph representing Polish voivodship cities. As part of the work an application in Python was written. It provides a user interface which allows to set selected parameters of the evolutionary algorithm and solve the prepared problem. The results are presented in both text and graphical form. The correctness of the evolutionary algorithm's operation and the implementation was confirmed by performed tests. A large number of tested solutions (2500) and the analysis of the obtained results allowed for a conclusion that an optimal (relatively suboptimal) solution was found.
Źródło:
Applied Computer Science; 2023, 19, 2; 55-62
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Real-time fan scheduling in exploration-exploitation to optimize minimum function objectives
Autorzy:
Larios-Gómez, Mariano
Quintero-Flores, Perfecto M.
Anzures-García, Mario
Camacho-Hernandez, Miguel
Powiązania:
https://bibliotekanauki.pl/articles/30148244.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
real-time task scheduling
genetic algorithms
concurrent computing
Opis:
This paper presents the application of a task scheduling algorithm called Fan based on artificial intelligence technique such as genetic algorithms for the problem of finding minima in objective functions, where equations are predefined to measure the return on investment. This work combines the methodologies of population exploration and exploitation. Results with good aptitudes are obtained until a better learning based on non-termination conditions is found, until the individual provides a better predisposi¬tion, adhering to the established constraints, exhausting all possible options and satisfying the stopping condition. A real-time task planning algorithm was applied based on consensus techniques. A software tool was developed, and the scheduler called FAN was adapted that contemplates the execution of periodic, aperiodic, and sporadic tasks focused on controlled environments, considering that strict time restrictions are met. In the first phase of the work, it is shown how convergence precipitates to an evolution. This is done in a few iterations. In the second stage, exploitation was improved, giving the algorithm a better performance in convergence and feasibility. As a result, a population was used and iterations were applied with a fan algorithm and better predisposition was obtained, which occurs in asynchronous processes while scheduling in real time.
Źródło:
Applied Computer Science; 2023, 19, 2; 43-54
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimisation of a Nacelle Electro-Thermal Ice Protection System for Icing Wind Tunnel Testing
Autorzy:
Gallia, Mariachiara
Carnemolla, Alessandro
Premazzi, Marco
Guardone, Alberto
Powiązania:
https://bibliotekanauki.pl/articles/36810362.pdf
Data publikacji:
2023
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Lotnictwa
Tematy:
in-flight icing
ice protection systems
optimisation
genetic algorithms
nacelle
Opis:
Aircraft are equipped with ice protection systems (IPS), to avoid, delay or remove ice accretion. Two widely used technologies are the thermo-pneumatic IPS and the electro-thermal IPS (ETIPS). Thermopneumatic IPS requires air extraction from the engine negatively affecting its performances. Moreover, in the context of green aviation, aircraft manufacturers are moving towards hybrid or fully electric aircraft requiring all electric on-board systems. In this work, an ETIPS has been designed and optimised to replace the nacelle pneumatic-thermal system. The aim is to minimise the power consumption while assuring limited or null ice formation and that the surface temperature remains between acceptable bounds to avoid material degradation. The design parameters were the length and heat flux of each heater. Runback ice formations and surface temperature were assessed by means of the in-house developed PoliMIce framework. The optimisation was performed using a genetic algorithm, and the constraints were handled through a linear penalty method. The optimal configuration required 33% less power with respect to the previously installed thermo-pneumatic IPS. Furthermore, engine performance is not affected in the case of the ETIPS. This energy saving resulted in an estimated reduction of specific fuel consumption of 3%, when operating the IPS in anti-icing mode.
Źródło:
Transactions on Aerospace Research; 2023, 1 (270); 32-44
0509-6669
2545-2835
Pojawia się w:
Transactions on Aerospace Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimisation of crop rotations : A case study for corn growing practices in forest-steppe of Ukraine
Autorzy:
Romashchenko, Mykhailo
Bohaienko, Vsevolod
Shatkovskyi, Andrij
Saidak, Roman
Matiash, Tetiana
Kovalchuk, Volodymyr
Powiązania:
https://bibliotekanauki.pl/articles/2203553.pdf
Data publikacji:
2023
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
combinatorial optimisation
corn
crop rotation
genetic algorithms
Opis:
The formation of optimal crop rotations is virtually unsolvable from the standpoint of the classical methodology of experimental research. Here, we deal with a mathematical model based on expert estimates of “predecessor-crop” pairs’ efficiency created for the conditions of irrigation in the forest-steppe of Ukraine. Solving the problem of incorporating uncertainty assessments into this model, we present new models of crop rotations’ economic efficiency taking into account irrigation, application of fertilisers, and the negative environmental effect of nitrogen fertilisers’ introduction into the soil. For the considered models we pose an optimisation problem and present an algorithm for its solution that combines a gradient method and a genetic algorithm. Using the proposed mathematical tools, for several possible scenarios of water, fertilisers, and purchase price variability, the efficiency of growing corn as a monoculture in Ukraine is simulated. The proposed models show a reduction of the profitability of such a practice when the purchase price of corn decreases below 0.81 EUR∙kg-1 and the price of irrigation water increases above 0.32 EUR∙m-3 and propose more flexible crop rotations. Mathematical tools developed in the paper can form a basis for the creation of decision support systems that recommend optimal crop rotation variations to farmers and help to achieve sustainable, profitable, and ecologically safe agricultural production. However, future works on the actualisation of the values of its parameters need to be performed to increase the accuracy.
Źródło:
Journal of Water and Land Development; 2023, 56; 194--202
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of Job Shop Scheduling Problem by Genetic Algorithms: Case Study
Autorzy:
Sahar, Habbadi
Herrou, Brahim
Sekkat, Souhail
Powiązania:
https://bibliotekanauki.pl/articles/24200523.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
optimization
metaheuristics
scheduling
job shop scheduling problem
genetic algorithms
simulation
Opis:
The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.
Źródło:
Management and Production Engineering Review; 2023, 14, 3; 44--56
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Synthesis of Reconfigurable Multiple Shaped Beams of a Concentric Circular Ring Array Antenna Using Evolutionary Algorithms
Autorzy:
Dubey, Sanjay Kumar
Mandal, Debasis
Powiązania:
https://bibliotekanauki.pl/articles/2200965.pdf
Data publikacji:
2023
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
cosec2 beam
differential evolution algorithm (DE)
firefly algorithm (FA)
flat top beam
genetic algorithm (GA)
multiple shaped beam patterns
pencil beam
Opis:
The approach described in this paper uses evolutionary algorithms to create multiple-beam patterns for a concentric circular ring array (CCRA) of isotropic antennas using a common set of array excitation amplitudes. The flat top, cosec2, and pencil beam patterns are examples of multiple-beam patterns. All of these designs have an upward angle of θ = 0◦. All the patterns are further created in three azimuth planes (φ = 0◦, 5◦, and 10◦). To create the necessary patterns, non-uniform excitations are used in combination with evenly spaced isotropic components. For the flat top and cosecant-squared patterns, the best combination of common components, amplitude and various phases is applied, whereas the pencil beam pattern is produced using the common amplitude only. Differential evolutionary algorithm (DE), genetic algorithm (GA), and firefly algorithm (FA) are used to generate the best 4-bit discrete magnitudes and 5-bit discrete phases. These discrete excitations aid in lowering the feed network design complexity and the dynamic range ratio (DRR). A variety of randomly selected azimuth planes are used to verify the excitations as well. With small modifications in the desired parameters, the patterns are formed using the same excitation. The results proved both the efficacy of the suggested strategy and the dominance of DE over GA as well as FA.
Źródło:
Journal of Telecommunications and Information Technology; 2023, 1; 8--17
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Testing algorithms for quick rescheduling flow shop problems with FlexSim based simulation and R engine
Autorzy:
Janke, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/27313435.pdf
Data publikacji:
2023
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
flow-shop problem
genetic algorithm
simulation
problem przepływowy
algorytm genetyczny
symulacja
Opis:
Purpose: The aim of this paper is to present a combination of advanced algorithms for finding optimal solutions together with their tests for a permutation flow-shop problem with the possibilities offered by a simulation environment. Four time-constrained algorithms are tested and compared for a specific problem. Design/methodology/approach: Four time-constrained algorithms are tested and compared for a specific problem. The results of the work realisation of the algorithms are transferred to a simulation environment. The entire solution proposed in the work is composed as a parallel environment to the real implementation of the production process. Findings: The genetic algorithm generated the best solution in the same specified short time. By implementing the adopted approach, the correct cooperation of the FlexSim simulation environment with the R language engine was obtained. Research limitations/implications: The genetic algorithm generated the best solution in the same specified short time. By implementing the approach, a correct interaction between the FlexSim simulation environment and the R language engine was achieved. Practical implications: The solution proposed in this paper can be used as an environment to test solutions proposed in production. Simulation methods in the areas of logistics and production have for years attracted the interest of the scientific community and the wider industry. Combining the achievements of science in solving computationally complex problems with increasingly sophisticated algorithms, including artificial intelligence algorithms, with simulation methods that allow a detailed overview of the consequences of changes made seems promising. Originality/value: The original concept of cooperation between the R environment and the FlexSim simulation software for a specific problem was presented.
Źródło:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2023, 168; 163--175
1641-3466
Pojawia się w:
Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
AH Method: a Novel Routine for Vicinity Examination of the Optimum Found with a Genetic Algorithm
Autorzy:
Piętak, Daniel Andrzej
Bilski, Piotr
Napiorkowski, Paweł Jan
Powiązania:
https://bibliotekanauki.pl/articles/2200688.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
heuristics
evolutionary computations
genetic algorithms
uncertainty estimation
parameter study
Opis:
The paper presents a novel heuristic procedure (further called the AH Method) to investigate function shape in the direct vicinity of the found optimum solution. The survey is conducted using only the space sampling collected during the optimization process with an evolutionary algorithm. For this purpose the finite model of point-set is considered. The statistical analysis of the sampling quality based upon the coverage of the points in question over the entire attraction region is exploited. The tolerance boundaries of the parameters are determined for the user-specified increase of the objective function value above the found minimum. The presented test-case data prove that the proposed approach is comparable to other optimum neighborhood examination algorithms. Also, the AH Method requires noticeably shorter computational time than its counterparts. This is achieved by a repeated, second use of points from optimization without additional objective function calls, as well as significant repository size reduction during preprocessing.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 4; 695--708
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applying optimization techniques on cold-formed C-channel section under bending
Autorzy:
El-Lafy, Heba F.
Elgendi, Elbadr O.
Morsy, Alaa M.
Powiązania:
https://bibliotekanauki.pl/articles/27312402.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
kształtownik zimnogięty
optymalizacja
algorytm genetyczny
cold-formed sections
single optimization
multi-objective optimization
genetic algorithms
effective width method
C-channel beams
Opis:
There are no standard dimensions or shapes for cold-formed sections (CFS), making it difficult for a designer to choose the optimal section dimensions in order to obtain the most cost-effective section. A great number of researchers have utilized various optimization strategies in order to obtain the optimal section dimensions. Multi-objective optimization of CFS C-channel beams using a non-dominated sorting genetic algorithm II was performed using a Microsoft Excel macro to determine the optimal cross-section dimensions. The beam was optimized according to its flexural capacity and cross-sectional area. The flexural capacity was computed utilizing the effective width method (EWM) in accordance with the Egyptian code. The constraints were selected so that the optimal dimensions derived from optimization would be production and construction-friendly. A Pareto optimal solution was obtained for 91 sections. The Pareto curve demonstrates that the solution possesses both diversity and convergence in the objective space. The solution demonstrates that there is no optimal solution between 1 and 1.5 millimeters in thickness. The solutions were validated by conducting a comprehensive parametric analysis of the change in section dimensions and the corresponding local buckling capacity. In addition, performing a single-objective optimization based on section flexural capacity at various thicknesses The parametric analysis and single optimization indicate that increasing the dimensions of the elements, excluding the lip depth, will increase the section’s carrying capacity. However, this increase will depend on the coil’s wall thickness. The increase is more rapid in thicker coils than in thinner ones.
Źródło:
International Journal of Applied Mechanics and Engineering; 2022, 27, 4; 52--65
1734-4492
2353-9003
Pojawia się w:
International Journal of Applied Mechanics and Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic detection of brain tumors using genetic algorithms with multiple stages in magnetic resonance images
Autorzy:
Annam, Karthik
Kumar, Sunil G.
Babu, Ashok P.
Domala, Narsaiah
Powiązania:
https://bibliotekanauki.pl/articles/27314266.pdf
Data publikacji:
2022
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
MRI brain tumor
GLCM
SURF
genetic optimization
advanced machine learning
Opis:
The field of biomedicine is still working on a solution to the challenge of diagnosing brain tumors, which is now one of the most significant challenges facing the profession. The possibility of an early diagnosis of brain cancer depends on the development of new technologies or instruments. Automated processes can be made possible thanks to the classification of different types of brain tumors by utilizing patented brain images. In addition, the proposed novel approach may be used to differentiate between different types of brain disorders and tumors, such as those that affect the brain. The input image must first undergo pre-processing before the tumor and other brain regions can be separated. Following this step, the images are separated into their respective colors and levels, and then the Gray Level Co-Occurrence and SURF extraction methods are used to determine which aspects of the photographs contain the most significant information. Through the use of genetic optimization, the recovered features are reduced in size. The cut-down features are utilized in conjunction with an advanced learning approach for the purposes of training and evaluating the tumor categorization. Alongside the conventional approach, the accuracy, inaccuracy, sensitivity, and specificity of the methodology under consideration are all assessed. The approach offers an accuracy rate greater than 90%, with an error rate of less than 2% for every kind of cancer. Last but not least, the specificity and sensitivity of each kind are higher than 90% and 50%, respectively. The usage of a genetic algorithm to support the approach is more efficient than using the other ways since the method that the genetic algorithm utilizes has greater accuracy as well as higher specificity.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 4; 36--43
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
ForestTaxator : a tool for detection and approximation of cross-sectional area of trees in a cloud of 3D points
Autorzy:
Małaszek, Maciej
Zembrzuski, Andrzej
Gajowniczek, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/2201227.pdf
Data publikacji:
2022
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Tematy:
point cloud
genetic algorithms
trees
3D scan
Opis:
In this paper we propose a novel software, named ForestTaxator, supporting terrestrial laser scanning data processing, which for dendrometric tree analysis can be divided into two main processes: tree detection in the point cloud and development of three-dimensional models of individual trees. The usage of genetic algorithms to solve the problem of tree detection in 3D point cloud and its cross-sectional area approximation with ellipse-based model is also presented. The detection and approximation algorithms are proposed and tested using various variants of genetic algorithms. The work proves that the genetic algorithms work very well: the obtained results are consistent with the reference data to a large extent, and the time of genetic calculations is very short. The attractiveness of the presented software is due to the fact that it provides all necessary functionalities used in the forest inventory field. The software is written in C# and runs on the .NET Core platform, which ensures its full portability between Windows, MacOS and Linux. It provides a number of interfaces thus ensuring a high level of modularity. The software and its code are made freely available.
Źródło:
Machine Graphics & Vision; 2022, 31, 1/4; 19--48
1230-0535
2720-250X
Pojawia się w:
Machine Graphics & Vision
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Genetic algorithms in active vibration reduction problem
Autorzy:
Grochowina, Marcin
Tyburski, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/2202428.pdf
Data publikacji:
2022
Wydawca:
Politechnika Poznańska. Instytut Mechaniki Stosowanej
Tematy:
active vibration control
genetic algorithm
PID
aktywna kontrola drgań
algorytm genetyczny
Opis:
The design of active vibration reduction systems usually consists in selecting a control algorithm and determining the value of its settings. This article presents the results of research on the concept of using genetic algorithms to induce the settings of control systems. To test the concept, a simple pulse-excited flat bar model was selected. The vibrations were suppressed by the PID controller. Genetic algorithms with two types of crossover were tested - arithmetic and uniform. As a result, the settings for the PID controller were obtained, enabling effective reduction of vibrations in a short time.
Źródło:
Vibrations in Physical Systems; 2022, 33, 2; art. no. 2022219
0860-6897
Pojawia się w:
Vibrations in Physical Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Genetic based algorithms to solving multi-quays berth allocation problem with setup time constraints
Autorzy:
Sangsawang, Chatnugrob
Longploypad, Cholthida
Powiązania:
https://bibliotekanauki.pl/articles/2203780.pdf
Data publikacji:
2022
Wydawca:
Wyższa Szkoła Logistyki
Tematy:
Multi-Quay
Berth Allocation Problem
genetic algorithm
Sequence-Dependent Setup Times
problem alokacji miejsc postojowych
algorytm genetyczny
czasy konfiguracji zależne od sekwencji
Opis:
Background: This study focuses on efficient berth planning in multi-purpose terminal composed of multiple quays. A multi-quay berth offers infrastructure, equipment, and services for different types of cargo and vessels to meet the needs of users from various freight markets. Moreover, each berth from any quay can be dedicated for one or two different types of cargo and vessels. To improve port efficiency in terms of reducing the waiting time of ships, this study addresses the Multi-Quay Berth Allocation Problem (MQ-BAP), where discrete berthing layout is considered along with setup time constraints and practical constraints such as time windows and safety distances between ships. Sequence dependent setup times may arise due to the berth can convert from dedicated function to another function according to the variance of cargo demand. This problem was inspired by a real case of a multi-purpose port in Thailand. Methods: To solve the problem, we propose a mixed-integer programming model to find the optimal solutions for small instances. Furthermore, we adapted a metaheuristic solution approach based on Genetic algorithm (GA) to solving the MQ-BAP model in large-scale problem cases. Results: Numerical experiments are carried out on randomly generated instances for multi-purpose terminals to assess the effectiveness of the proposed model and the efficiency of the proposed algorithm. The results show that our proposed GA provides a near-optimal solution by average 4.77% from the optimal and show a higher efficiency over Particle swarm optimization (PSO) and current practice situation, which are first come first serve (FCFS) rule by 1.38% and 5.61%, respectively. Conclusions: We conclude that our proposed GA is an efficient algorithm for near-optimal MQ-BAP with setup time constraint at acceptable of computation time. The computational results reveal that the reliability of the metaheuristics to deal with large instances is very efficient in solving the problem considered.
Źródło:
LogForum; 2022, 18, 4; 505--515
1734-459X
Pojawia się w:
LogForum
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan
Autorzy:
Korol, Tomasz
Fotiadis, Anestis K.
Powiązania:
https://bibliotekanauki.pl/articles/19322547.pdf
Data publikacji:
2022
Wydawca:
Instytut Badań Gospodarczych
Tematy:
fuzzy logic
genetic algorithms
artificial neural networks
consumer bankruptcy
the financial crisis of households
Opis:
Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention.  Purpose of the article: This study proposes artificial intelligence solutions to address the increased importance of the personal bankruptcy phenomenon and the growing need for reliable forecasting models. The objective of this paper is to develop six models for forecasting personal bankruptcies in Poland and Taiwan with the use of three soft-computing techniques. Methods: Six models were developed to forecast the risk of insolvency: three for Polish households and three for Taiwanese consumers, using fuzzy sets, genetic algorithms, and artificial neural networks. This research relied on four samples. Two were learning samples (one for each country), and two were testing samples, also one for each country separately. Both testing samples contain 500 bankrupt and 500 nonbankrupt households, while each learning sample consists of 100 insolvent and 100 solvent natural persons. Findings & value added: This study presents a solution for effective bankruptcy risk forecasting by implementing both highly effective and usable methods and proposes a new type of ratios that combine the evaluated consumers? financial and demographic characteristics. The usage of such ratios also improves the versatility of the presented models, as they are not denominated in monetary value or strictly in demographic units. This would be limited to use in only one country but can be widely used in other regions of the world.
Źródło:
Oeconomia Copernicana; 2022, 13, 2; 407-438
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-criteria human resources planning optimisation using genetic algorithms enhanced with MCDA
Autorzy:
Jurczak, Marcin
Miebs, Grzegorz
Bachorz, Rafał A.
Powiązania:
https://bibliotekanauki.pl/articles/2204085.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
mathematical optimisation
multi-criteria optimisation
scheduling
job shop problem
MCDA
Opis:
The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimisation for job scheduling. The proposed genetic procedure leads to the Pareto front, and the application of the multiple criteria decision aiding (MCDA) approach allows extraction of the final solution. Definition of the key performance indicator (KPI), reflecting relevant features of the solutions, and the efficiency of the genetic procedure provide the Pareto front comprising the representative set of feasible solutions. The application of chosen MCDA, namely elimination et choix traduisant la réalité (ELECTRE) method, allows for the elicitation of the decision maker (DM) preferences and subsequently leads to the final solution. This solution fulfils all of the DM expectations and constitutes the best trade-off between considered KPIs. The proposed method is an efficient combination of genetic optimisation and the MCDA method.
Źródło:
Operations Research and Decisions; 2022, 32, 4; 57--74
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
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