Wyniki wyszukiwania - BazTech

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Passenger’s routes planning in stochastic common-lines’ multi-modal transportation network through integrating Genetic Algorithm and Monte Carlo simulation

Peng Yong, Mo Zhiyao, Liu Song

Archives of Transport

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2021

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Vol. 59, iss. 3

73--92

EN

In the urban transportation network, most passengers choose public transportation to travel. However, bad weather, accidents, traffic jams and other factors lead to uncertainty in transportation network. Besides, transport vehicles running on the same segments of routes and belonging to different modes or routes make the transportation network more complicated. In order to improve the efficiency of passenger’s travel, this paper aim to introducing an approach for optimizing passenger travel routes. This approach takes the travel cost and the number of transfers as constraints to finding the shortest total travel duration of passenger in urban transportation network. The running duration and dwell duration of the vehicles in the network are uncertain, and the vehicles are running according to the timetables. As transportation modes, bus, rail transit and walk are considered. In terms of methodological contribution, this paper combines Genetic Algorithm (GA) and Monte Carlo simulation to deal with optimization problem under stochastic conditions. This paper uses Monte Carlo simulation to simulate the running duration and dwell time of vehicles in different scenarios to deal with the uncertainty of the network. The shortest path of passenger’s travel is solved by GA. Two kinds of population management strategies including single population management strategy and multiple population management strategy are designed to guide the solution population evolving process. The two kinds of population management strategies of GA are tested in numerical example. The satisfactory convergence performance and efficiency of the model and algorithm is verified by the numerical example. The numerical example also demonstrated that the multiple population management strategy of GA can get better results in a shorter CPU time. At the same time, the influences of some significant variables on solution are performed. This paper is able to provide a scientific quantitative support to the path scheme selection under the influence of common-lines and timetables of different modes of transportation in stochastic urban multimodal transportation network.

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Przykład wykorzystania metody GERTS do modelowania robot górniczych

Brzychczy E.

Prace Naukowe Instytutu Górnictwa Politechniki Wrocławskiej. Konferencje

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2004

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Vol. 107, nr 39

205-215

PL

W artykule przedstawiono przykład wykorzystania metody GERTS do modelowania robot g6rniczych i wskazano na dalsze możliwości wykorzystania sieci stochastycznych w tym zakresie.

EN

In article an example of using GERTS method In mine Works modeling is presented. There is also shown further possibilities of using stochastic networks in this fields.

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Prediction of cleaving patterns of proteasomes

Hadeler K.

Journal of Medical Informatics & Technologies

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2002

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Vol. 3

IP25--26

EN

Proteasomes are enzymes which perform an essential step in degrading proteins in eukaryotic cells. In mammals they play an important role in MHC I ligand generation and thus in the regulation of specific immune responses. The cleavages or cuts made by proteasomes on typical protein substrates are not uniquely determined by adjacent amino acids in the substrate nor do they follow simple statistical rules. There are several approaches to understanding the cleaving patterns either by statistical analysis or by designing proteasome models in the form of stochastic networks. Here the latter approach will be presented. A network simulating a proteasome molecule or rather a family of such molecules has been trained on experimental data in order to extract cleaving rules. The training uses experimentally meaningful goal functions and a stochastic hill-climbing process. The network can reproduce experimentally observed cleaving patterns and also to some extent predict such patterns. The eventually obtained affinity parameters of the optimized model correspond well with experimentally determined cleavage motifs. The model can be adapted to existing types of experimentally observed proteasomes such as defect mutants or interferon-inducible proteasomes.