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Optymalizacja lokalizacji centrów dystrybucyjnych z wykorzystaniem wielokryterialnego algorytmu genetycznego ɛ-NSGA-II

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Rudziński F. , Głuszek A. , Piekoszewski J.

2014

tom nr 6

9223--9231

Praca przedstawia zastosowanie genetycznego algorytmu optymalizacji wielokryterialnej ɛ-NSGA-II (ang. Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm) do rozwiązywania problemu rozmieszczenia centrów dystrybucyjnych w sieci dystrybucyjnej (logistycznej). Zastosowana technika optymalizacji umożliwia uzyskanie zestawu tzw. rozwiązań Pareto-optymalnych, reprezentujących różny poziom kompromisu pomiędzy przyjętymi kryteriami oceny. Wykorzystano model sieci dystrybucyjnej zorientowany na minimalizację całkowitego kosztu utrzymania sieci, minimalizację emisji dwutlenku węgla wydalanego przez silniki spalinowe do atmosfery oraz maksymalizację niezawodności usług transportowych. Rezultaty eksperymentów oraz analiza porównawcza proponowanego podejścia z alternatywną techniką, tj. hybrydową metodą ɛ -wymuszeń (ang. ɛ-constraint method) wykazały wysoką użyteczność algorytmu ɛ-NSGA-II w rozwiązywaniu tego rodzaju problemów oraz jej wyraźną przewagę nad konkurencyjna metodą.

The paper presents an application of the multi-objective genetic algorithm ɛ-NSGA-II (Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm) in the logistic facilities location problems. This technique allows to obtain a set of so-called Pareto-optimal solutions representing different levels of compromise between the criteria of their evaluation. A model of the distribution network used in the paper is focused on minimizing the total maintenance cost of the network, minimizing carbon emissions emitted by internal combustion engines into the atmosphere and maximizing the customer service reliability. The results of experiments and a comparative analysis of the proposed approach with an alternative technique, ie. hybrid ɛ-constraint method prove a high utility of ɛ-NSGA-II algorithm in solving this kind of problems. A distinct advantage of the approach over the alternative technique has been demonstrated as well.

Unsupervised Pixel Classification in Satellite Imagery: A Two-stage Fuzzy Clustering Approach

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Mukhopadhyay A. , Maulik U.

tom Vol. 86, nr 4

411-428

A popular approach for landcover classification in remotely sensed satellite images is clustering the pixels in the spectral domain into several fuzzy partitions. It has been observed that performance of the clustering algorithms deteriorate with more and more overlaps in the data sets. Motivated by this observation, in this article a two-stage fuzzy clustering algorithm is described that utilizes the concept of points having significant membership to multiple classes. The points situated in the overlapped regions of different clusters are first identified and excluded from consideration while clustering. Thereafter, these points are given class labels based on Support vector Machine classifier which is trained by the remaining points. The well known Fuzzy C-Means algorithm and some recently proposed genetic clustering schemes are utilized in the process. The effectiveness of the two-stage clustering technique has been demonstrated on IRS remote sensing satellite images of the cities of Bombay and Calcutta and compared with other well known clustering techniques. Also statistical significance test has been carried out to establish the statistical significance of the clustering results.

Optimization of an aluminum alloy anti-collision side beam hot stamping process using a multi-objective genetic algorithm

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Zhou J. , Wang B. , Lin J. , Fu L.

tom Vol. 13, no. 3

401--411

The process parameters of aluminum alloy hot stamping are essential for product forming quality. In the case of an anti-collision side beam inside car doors, the finite-element model of aluminum alloy hot stamping is set up, and the forming quality is investigated under an ordinary process condition. The blank hold force (BHF) has a significant impact on the forming quality in hot stamping. Using the Latin hypercube method to sample the simulation data points and the finite-element (FE) model to calculate the forming quality indices of the data points according to the response value of the indices, the quadratic response surfaces between the process parameter inputs and the forming quality indices are initialized. Using the multi-objective genetic algorithm NSGA-II (non-dominated sorting genetic algorithm) to optimize the responses of the process parameters, the Pareto solutions corresponding to combinations of the blank hold force and stamping speed are obtained. Finally, based on the optimal process parameters, stamping tests are carried out. Compared with the results of the stamping trial and numerical simulation, it is demonstrated that the finite-element model can predict forming defects and be consistent with the actual condition and that the optimization procedure proposed in the paper is feasible.