Temat: genetic comparison - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Genetic comparison of Campylobacter jejuni isolated from different cattle farms
Autorzy:: Bednarski, M.
Wieliczko, A.
Mazurkiewicz, M.
Powiązania:: https://bibliotekanauki.pl/articles/31410.pdf
Data publikacji:: 2011
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: genetic comparison
Campylobacter jejuni
isolation
cattle
animal farm
genotyping
ERIC-PCR method
Opis:: To compare the genotypes of Campylobacter jejuni, isolates of cattle origin were collected from 9 Polish farms and genotyped by ERIC-PCR. We identified 28 genotypes among the 43 C. jejuni isolates, and demonstrated high genomic diversity. The highest level of diversity was observed in strains isolated from stanchion-barn animals in opposition to those from the loose-housing system.
Źródło:: Polish Journal of Veterinary Sciences; 2011, 14, 2
1505-1773
Pojawia się w:: Polish Journal of Veterinary Sciences
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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

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