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Wyszukujesz frazę "mapping method" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Analysis of the Grid Sampling Method for Noise Mapping
Autorzy:
Gómez Escobar, V.
Barrigón Morillas, J. M.
Rey Gozalo, G
Vilchez-Gómez, R.
Carmona del Rio, J.
Méndez Sierra, J. A.
Powiązania:
https://bibliotekanauki.pl/articles/176666.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
grid method
noise mapping
urban noise
Opis:
The grid method is the most widely used technique for measurement-based noise assessment, and indeed is part of the ISO 1996-2 standard. Nevertheless it has certain disadvantages. The present work is an analysis of the grid method for evaluating noise, firstly in the city of C´aceres and, secondly in two other smaller towns. Using as reference a 200 metre grid study, a study was made of the effect of varying the size and form of the grid on the city’s overall noise value, the percentage of data found to lie above some reference thresholds, and the noise value assigned to a certain zone of the city. The ISO 1996 recommendations of the necessity of new sampling points and the method’s predictive capacity for these new measurements were also analyzed.
Źródło:
Archives of Acoustics; 2012, 37, 4; 499-514
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Productivity improvement picking order by appropriate method, value stream mapping analysis, and storage design: a case study in automotive part center
Autorzy:
Purba, H. H.
Mukhlisin, -
Aisyah, S.
Powiązania:
https://bibliotekanauki.pl/articles/952858.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
picking order
productivity
value stream mapping (VSM)
picking method
Opis:
Spare parts are one of the important pillars in the after-sales service of automotive business. Customers will satisfied and comfortable if the availability of spare parts is guaranteed. Spare Part Center is one of function to support unit sales and as well as profit-oriented, so the accuracy and speed of spare part acceptance by the customer is an important key to winning the competition. Order Picking is one of the supply chain processes that play a role in warehouse operations to meet customer needs. Order Picking is the most expensive activity in warehousing and can reach 55% of the total cost of warehousing operations, so it is considered a top priority in increasing productivity, even reaching 65% of total warehouse operating costs. The purpose of this research is to increase productivity in the process of picking order through reduction of processing time. Increased productivity is done by improving the working method of the picking process. From the result the comparing, the method by zone requires less total picking time (193.712 seconds) than by routing (249.559 seconds) decreased 55.85 second time, in other words, an increase of 22.38%. With the Visual Stream Mapping (VSM) in this research can reduce to travel time, it means that the total distance traveled is small than the current method. The impact from VSM approach will eliminate time for preparation of 1.960 seconds, and take empty trolley of 200 seconds. In this case some of traveling non-value adding for customer, so this is should be taken off from process picking because it is a waste.
Źródło:
Management and Production Engineering Review; 2018, 9, 1; 71-81
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration
Autorzy:
Tang, Minan
Zhang, Kaiyue
Wang, Qianqian
Cheng, Haipeng
Yang, Shangmei
Du, Hanxiao
Powiązania:
https://bibliotekanauki.pl/articles/1841286.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Chebyshev chaotic mapping
distributed generation
distribution network reconfiguration
fuzzy decision method
Pareto optimal
pmdMOFOA
population Manhattan distance
Opis:
In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance (PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.
Źródło:
Archives of Electrical Engineering; 2021, 70, 2; 307-323
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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