Wyniki wyszukiwania - BazTech

1

Artificial Neural Network Optimized by Modified Particle Swarm Optimization for Predicting Peak Particle Velocity Induced by Blasting Operations in Open Pit Mines

Bui Xuan‑Nam, Nguyen Hoang, Nguyen Truc Anh

Inżynieria Mineralna

|

2021

|

no. 2

79--90

EN

Blasting is an indispensable part of the open pit mining operations. It plays a vital role in preparing the rock mass for subsequent operations, such as loading/unloading, transporting, crushing, and dumping. However, adverse effects, especially blast-induced ground vibrations, are considered one of the most dangerous problems. In this study, artificial intelligence was supposed to predict the intensity of blast-induced ground vibration, which is represented by the peak particle velocity (PPV). Accordingly, an artificial neural network was designed to predict PPV at the Coc Sau open pit coal mine with 137 blasting events were collected. Aiming to optimize the ANN model, the modified version of the particle swarm optimization (MPSO) algorithm was applied to optimize the ANN model for predicting PPV, called the MPSO-ANN model. For the comparison purposes, two forms of empirical equations, namely United States Bureau of Mining (USBM) and U Langefors - Kihlstrom, were also developed to predict PPV and compared with the proposed MPSO-ANN model. The results showed that the proposed MPSO-ANN model provided a better performance with a mean absolute error (MAE) of 1.217, root-mean-squared error (RMSE) of 1.456, and coefficient of determination (R2) of 0.956. Meanwhile, the empirical models only provided poorer performances with an MAE of 1.830 and 2.012, RMSE of 2.268 and 2.464, and R2 of 0.874 and 0.852 for the USBM and U Langefors – Kihlstrom empirical models, respectively.

2

Cost-efficient project management based on critical chain method with partial availability of resources

Pawiński G., Sapiecha K.

Control and Cybernetics

|

2014

|

Vol. 43, no. 1

95--109

EN

Cost-efficient project management based on Critical Chain Method (CCPM) is investigated in this paper. This is a variant of the resource-constrained project scheduling problem (RCPSP) when resources are only partially available and a deadline is given, but the cost of the project should be minimized. RCPSP is a well- known NP hard problem but originally it does not take into consideration the initial resource workload. A metaheuristic algorithm driven by a metric of a gain was adapted to solve the problem when applied to CCPM. Refinement methods enhancing the quality of the results are developed. The improvement expands the search space by inserting the task in place of an already allocated task, if a better allocation can be found for it. The increase of computation time is reduced by distributed calculations. The computational experiments showed significant efficiency of the approach, in comparison with the greedy methods and with genetic algorithm, as well as high reduction of time needed to obtain the results.

3

Choice of best possible metaheuristic algorithm for the travelling salesman problem with limited computational time: quality, uncertainty and speed

Antosiewicz M., Koloch G., Kamiński B.

Journal of Theoretical and Applied Computer Science

|

2013

|

Vol. 7, nr 1

46--55

EN

We compare six metaheuristic optimization algorithms applied to solving the travelling salesman problem. We focus on three classical approaches: genetic algorithms, simulated annealing and tabu search, and compare them with three recently developed ones: quantum annealing, particle swarm optimization and harmony search. On top of that we compare all results with those obtained with a greedy 2-opt interchange algorithm. We are interested in short-term performance of the algorithms and use three criteria to evaluate them: solution quality, standard deviation of results and time needed to reach the optimum. Following the results from simulation experiments we conclude that simulated annealing and tabu search outperform newly developed approaches in short simulation runs with respect to all three criteria. Simulated annealing finds best solutions, yet tabu search has lower variance of results and converges faster.

4

Zastosowanie metaheurystycznych algorytmów szeregowania zadań w harmonogramowaniu wieloobiektowych przedsięwzięć budowlanych

Podolski M.

Prace Naukowe Instytutu Budownictwa Politechniki Wrocławskiej. Studia i Materiały

|

2006

|

Vol. 87, nr 18

147-156

PL

W pracy przedstawiono modele przedsięwzięć, które wymagają wykonania wielu obiektów budowlanych przy ograniczonym dla realizatora dostępie do zasobów. Są one przypadkami problemów optymalizacji dyskretnej rozważanych zazwyczaj w ramach teorii szeregowania zadań. W związku z wielką liczbą rozwiązań dopuszczalnych takich problemów zastosowano metody przybliżone - algorytmy poszukiwania z zakazami, genetyczny, symulowanego wyżarzania. Zaprezentowane modele zilustrowano przykładami obliczeniowymi.

EN

The paper deals with problems of scheduling multiobject civil engineering projects. The models of these problems are presented. The solution is based on metaheuristic algorithms of job scheduling problems: tabu search, genetic search, simulated annealing. The examples of models and application of these algorithms are also presented.

5

Jednomaszynowy problem sekwencyjny z zasobami - wybrane algorytmy metaheurystyczne

Janiak A., Krzyżanowski T., Marek M.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

|

1999

|

T. 3, z. 1

157-166

PL

W pracy rozpatrzono problem szeregowania zadań na jednej maszynie z zadanymi terminami dostępności i czasami realizacji zależnymi od ilości przydzielonego zasobu przy kryterium minimalizacji maksymalnej nieterminowości. Do rozwiązania tego problemu zaproponowano dwa algorytmy metaheurystyczne. Podano wyniki przeprowadzonych eksperymentów numerycznych.

EN

The paper deals with a single machine scheduling problem with given release dates and processing times dependent on resources. Considered criterion is the maximum lateness minimization. To solve the problem two metaheuristic algorithms are presented and compared.

6

Algorytmy metaheurystyczne rozwiązywania problemu szeregowania zadań z liniami krytycznymi

Wodecki M.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

|

1999

|

T. 3, z. 1

346-354

PL

W pracy rozpatrywany jest jednomaszynowy problem minimalizacji sumy kosztów zadań opóźnionych. W literaturze jest on oznaczany przez 1 ?w,-7} i należy do klasy problemów silnie NP-zupełnych. Do jego rozwiązywania przedstawimy algorytmy oparte na metodach metaheurystycznych: przeszukiwania tabu, symulowanego wyżarzania i algorytmu genetycznego, odpowiednio adoptowanych do rozwiązywania rozpatrywanego zagadnienia. Na podstawie eksperymentów obliczeniowych porównamy także efektywność działania poszczególnych algorytmów.

EN

This paper presents approximation algorithms for the single machine total weighted tardiness problems. The algorithms are based on a metaheuristic metods: tabu search, simulated annealing and genetic algorithm. Results of testing the algorithms on large number of randomly generated examples are also given and analysed.