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Gladston Raj, S.
- Facility Location in Logistic Network Design Using Soft Computing Optimization Models
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PDF Views:105
Authors
Affiliations
1 Bharathiar University, Coimbatore, Tamilnadu, IN
2 Govt. College, Nedumangadu, Thiruvananthapuram, Kerala, IN
1 Bharathiar University, Coimbatore, Tamilnadu, IN
2 Govt. College, Nedumangadu, Thiruvananthapuram, Kerala, IN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 9, No 5 (2017), Pagination: 51-65Abstract
Discovery of the optimal best possibility of location for facilities is the central problem associated in logistics management. The optimal places for the distribution centres (DCs) can be based on the selected attributes that are crucial to locate the best possible locations to increase the speed of the facility service and thus reduce the overall transport cost and time and to provide best service. The major task is to identifying and locating the required number of DCs and its optimum locations are considered as the important goals for the design of any logistics network. The number of DCs will clearly depends upon many factors like population, capacity of the facility, type of facility etc. but locating the optimum locations of DCs will reduce the overall cost. But, for solving such a wide problem space, the powerful tools are the soft computing based approaches and that are well suited and find a meaningful solution in finite time. In this work, we are going to find the optimum locations of DCs for logistics using various soft computing methods.Keywords
Logistic, Heuristic, Hybrid, Inbounded, Crossover, Mutation, Simulated, Annealing, Direct Search.Full Text
References
- Erlenkotter, D., "A Dual-Based Procedure for the Uncapacitated Facility Location." Operations Research, Vol 26(6), pg. 992-1009, 1978.
- Francis, R.L., McGinnis, F.L., Jr., White, J.A., "Facility Layout and Location: An Analytical Approach." Prentice Hall, 2nd Ed., 1974.
- Juliana Karakaneva, "A LOCATION PROBLEM MODELING AND SOLVING", Trakia Journal of Sciences, Vol 1, No 4, pp 1-7, 2003,ISSN 1312-1723, Copyright © 2003 Trakia University
- HK Smith, G Laporte and PR Harper, "Locational analysis: highlights of growth to maturity", http://eprints.soton.ac.uk/68930/1/Locational_Analysis_-_Smith,_Laporte_and_Harper.doc
- Michael J. Bucci, Michael G. Kay, Donald P. Warsing†, Jeffrey A. Joines, "Metaheuristics for Facility Location Problems with Economies of Scale", IIE Transactions
- Michael J. Bucci, Ryan Woolard, Jeffrey Joines, Kristin Thoney, Russell E. King, "An Application of Heuristics Incorporating Economies of Scale to Facility Location Problems in Carpet Recycling”
- Biehl, M., Prater, E., Realff, M.J., 2007, Assessing performance and uncertainty in developing carpet reverse logistics systems, Computers and Operations Research, 34, 443-463.
- . Brimberg, J.,Hansen, P., Mladenovic, N.,Taillard, E.D. ,2000, Improvements and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem, Operations Research, 48, 444460.
- Bucci, M.J., Kay, M.G., Warsing, D.P., Joines, J.A., 2009, Metaheuristics for Facility Location with Economies of Scale, Fitts Department of Industrial and Systems Engineering working paper, North Carolina State University, Raleigh NC.
- Cooper, L., 1963, Location-allocation problems, Operations Research, 11, 331-343.
- CARE (Carpet America Recovery Effort) annual report, 2007, http://www.carpetrecovery.org/pdf/annual_report/07_CARE-annual-rpt.pdf
- CARE(Carpet America Recovery Effort) network website, 2009, http://www.carpetrecovery.org/pdf/reclamation_centers/Carpet_Reclamation_Center s.pdf
- Daskin, M.S.,1995, Network and discrete location: models, algorithms, and applications, John Wiley and Sons, New York.
- de Brito, M.P., Dekker, R., Flapper, S.D.P., 2003, Reverse Logistics - a review of case studies, ERIM Report Series.
- Fleishchmann, M., Krikke, H.R., Dekker, R., Flapper, S.D.P., 2000, A characterization of logistics networks for product recovery, Omega, 28, 653-666.
- Louwers, D., Kip, B.J., Peters, E., Souren, F., Flapper, S.D.P., 1999, A facility location allocation model for reusing carpet, Computers and Industrial Engineering,
- Mirchandani, P.B., Francis, R.L., 1990, Discrete Location Theory, Wiley, NewYork.
- Realff, M.J., Ammons, J.C., Newton, D., 1999, Carpet Recycling: Determining the 38:3, 547-567.
- Realff, M., Systems Planning for Carpet Recycling. 2006, Recycling in Textiles, Editor Youjiang Wang, CRC Press.
- Repa, E.W., 2005, NSWMA's 2005 Tip Fee Survey, National Solid Wastes Management Association Website,://wastec.isproductions.net/webmodules/webarticles/articlefiles/478Tipping%20Fee%20Bulletin%202005.pdf
- Shaju Varghese, Gladston Raj S, “A Genetic Algorithm Based Optimization Model for Facility Location in Logistic Network Design”, International Journal of Applied Engineering Research, Vol 10, No. 69, pp-338-344, 2015, ISSN 0973-4562.
- Shaju Varghese, Gladston Raj S, “Simulated Annealing and Direct Search Based Optimization Models for Facility Location in Logistic Network Design” International Journal of Computer Applications, Vol 132, pp- 31-37, December 2015, ISSN: 0975-8887
- A Modified Binary PSO Based Feature Selection for Automatic Lesion Detection in Mammograms
Abstract Views :257 |
PDF Views:124
Authors
Affiliations
1 Department of Computer Applications, BPC College, Piravom, IN
2 Department of Computer Science, Government College, Nedumangad, IN
3 Department of Imageology, Regional Cancer Center, Thiruvananthapuram, IN
1 Department of Computer Applications, BPC College, Piravom, IN
2 Department of Computer Science, Government College, Nedumangad, IN
3 Department of Imageology, Regional Cancer Center, Thiruvananthapuram, IN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 10, No 2 (2018), Pagination: 39-55Abstract
This paper presents an effective feature selection method that can be applied to build a computer aided diagnosis system for breast cancer in order to discriminate between healthy, benign and malignant parenchyma. Determining the optimal feature set from a large set of original features is an important preprocessing step which removes irrelevant and redundant features and thus improves computational efficiency, classification accuracy and also simplifies the classifier structure. A modified binary particle swarm optimized feature selection method (MBPSO)has been proposed where k-Nearest Neighbour algorithm with leave-one-out cross validation serves as the fitness function. Digital mammograms obtained from Regional Cancer Centre, Thiruvananthapuram and the mammograms from web accessible mini-MIAS database has been used as the dataset for this experiment. Region of interests from the mammograms are automatically detected and segmented. A total of 117 shape, texture and histogram features are extracted from the ROIs. Significant features are selected using the proposed feature selection method.Classification is performed using feed forward artificial neural networks with back propagation learning. Receiver operating characteristics (ROC) and confusion matrix are used to evaluate the performance. Experimental results show that the modified binary PSO feature selection method not only obtains better classification accuracy but also simplifies the classification process as compared to full set of features. The performance of the modified BPSO is found to be at par with other widely used feature selection techniques.Keywords
Binary Particle Swarm Optimization, Feed Forward Artificial Neural Networks, Feature Selection, K-Nearest Neighbour.Full Text
References
- R Siegel, D. Naishadham and A.Jeimal(2013)“Cancer Statistics 2013”, CA: A Cancer Journal for Clinicians, Vol. 63, pp. 11-30.
- (2012-2014) Statistics of Breast Cancer in India. “Trends of Breast Cancer in India” [online] Availablehttp://www.breastcancerindia.net/statistics/trends.html
- (2013-14) World Cancer Research Fund International, “Cancer Facts and Figures” [online] Available http://www.wcrf.org/int/cancer-facts-and-figures
- (2016) American Cancer Society, “Breast Cancer Signs and Symptoms” [online] Available https://www.cancer.org/cancer/breast-cancer/about/breast-cancer-signs-and-symptoms.html
- K.Hu, X. Gao and F.Li (2011) “Detection of suspicious lesions by adaptive thresholding based on multi resolution analysis in mammograms”, IEEE Transactions on Instrumentation and Measurement, Vol. 60 (2), pp. 462-472.
- K.U Sheba and S. Gladston Raj (2016) “Objective quality assessment of image enhancement methods in digital mammography-A comparative study”,Signal and Image Processing: An International Journal, Vol. 7(4), pp.1-13.
- M.J.G Calas, B Gutfilen and W.C.A Pereira (2012) CAD and Mammography: Why use this tool?”, Radilogical Brasileira, Vol. 45 (1) , pp. 46-52.
- J.Deeba and S.T Selvi(2014) “Computer-aided detection of breast cancer on mammograms: A Swarm Intelligence optimized wavelet neural network approach”, Journal of Biomedical Informatics, Vol. 49, pp.45-52.
- El-Baz, G.M Beache, G Gimel’farb et.al., (2013)“ Computer-aided diagnosis system for lung cancer: challenges and methodologies”, International Journal of Biomedical Imaging, Vol 2013, Article T D 942353, 46 pages.doi:10,1155/2013/942353.
- K.U Sheba and Gladston Raj S.,(2017) “Detection of lesions in mammograms using grey-level, texture and shape features”, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9 Sp-16, pp. 919-936.
- B.I Shak and Anis(2016) “Variable selection using support vector regression and random forests: A comparative study”,Intelligent Data Analysis, Vol. 20 (1), pp. 83-104.
- B. Xue, MZhang and W.N Browne(2012) “New fitness functions in binary particle swarm optimization for feature selection”, IEEE World Congress on Computational Intelligence (WCCI 2012),Brisbane, Australia.
- A.Unler and A. Murat(2010) “A discrete particle swarm optimization method for feature selection in binary classification problems”, European Journal of Operational Research, Vol. 206, pp. 528-539.
- V. Kothari, J. Anuradha, S. Shah and P.Mittal (2012) “A survey on particle swarm optimization in feature selection”, In: Krishna P.Y, Babu M.R, Ariwa E. (eds), Global Trends in Information systems and software applications, Vol. 270, pp. 192-201.
- X. Wang, J. Yang, X. Teng, W. Xia and R. Jensen(2007)“ Feature selection based on rough sets and particle swarm optimization”, Pattern Recognition Letters, Vol. 28(4), pp. 459-471.
- B.Xue, S. Nguyen and M. Zhang(2014) “Anew binary particle swarm optimization algorithm for feature selection”, In: Esparcia-Alca’zar A., Mora A.(eds). Applications of Evolutionary Computation. EvoAapplications 2014, Lecture Notes in Computer science, Vol. 8602. Springer Berlin, Heidelberg. pp 501-513.
- B.Tran, B. Xue and M. Zhang (2014) “Overview of PSO for feature selection in classification”, In: Dick G et.al (eds). Simulated Evolution and Learning (SEAL 2014),Lecture Notes in Computer Science, Vol. 8886, Springer, Cham, pp. 605-617.
- Z.Yong, G. Dunwei, H.Ying and Z. Wanqiu (2015) “Feature selection algorithm based on bare bones PSO”. Neurocomputing, Vol.148(6), pp. 150-157.
- M.T Wong, X.He, W.C. Yeh, Z Ibrahim and Y.Y Chung (2014) “Feature selection and mass classification using PSO and SVM”, In: Loo C.K, Yap K.S, Wong K.W, Beng Jin A.T, Huang K (eds)., Neural information processing. ICONN 2014, Lecture Notes in Computer Science, Vol. 8836, Springer, Cham, pp. 439-446.
- S. Sivakumar and C. Chandrasekhar (2014) “Modified PSO based feature selection for classification of lung CT images”, International Journal of Computer Science and Information Technologies, Vol. 5(2), pp. 2095-2098.
- I. Zyout and I. Abdel-Qader(2011) “Classification of microcalcification clusters via PSO-KNN heuristic parameter selection and GLCM features”, International Journal of Computer Applications, Vol. 31(2), pp. 34-39.
- M.T Wong, X.He and H Nguyen (2012) “Particle Swarm Optimization based feature selection in mammogram mass classification”, Computerized Health Care (ICCH 2012), International conference on, pp.152-157, Dec 2012.
- J. Kennedy and R. Eberhart(1995) “Particle Swarm Optimization”, In:Proceedings of the 1995 IEEE international conference on neural networks,Perth, Australia, Vol. 4,pp. 1942-1948.
- J. Kennedy and R. Eberhart (1997), “A discrete binary version of particle swarm algorithm”, In: Proceedings of the 1997 IEEE International Conference on Systems, Man and Cybernetics (SMC 97), Vol. 5, pp. 4104-4108.
- Y.Shi and R. Eberhart(1998) “A Modified Particle Swarm Optimizer”, In: Proceedings of IEEE International Conference on Evolutionary Computation, World Congress on Computational Intelligence, Anchorage, Alaska.
- B.Xue, M.Zhang and W.N Browne( 2012) “ New Fitness Functions in binary particle swarm optimization for feature selection, IEEE World Congress on Computational Intelligence (WCCI 2012),Brisbane, Australia.
- S.Zhang, X.Li, M.Zong, X. Zhu and R.Wang (2017) “Efficient KNN classification with different numbers of nearest neighbors”, IEEE Transactions on Neural Networks and Learning systems, Vol. 99, pp.1-12.
- A.Vehtari, A.Gelman andJ.Gabry (2017) “Practicalbayesian model evaluation using leave-one-out cross validation and WAIC”, Statistics and Computing, Vol. 27(5),pp.1413-1432.
- J Suckling et.al (1994). The mammographic Image Analysis Society Digital Mammogram database. Exerpta Medica. International Congress Series 1069, pp. 375-378.
- K.U Sheba and S. Gladston Raj (2017) “Detection of Lesions in Mammograms using grey-level, texture and shape features”, Journal of Advanced Research in Dynamical and Control Systems, Vol.(16)-Special Issue, pp. 919-936.
- K.U Sheba and S. Gladston Raj (2017) “Adaptive fuzzy logic based bi-histogram equalization for contrast enhancement of mammograms”, In: Proceedings of IEEE International Conference on Intelligent Computing, Instrumentation and Control Technologies, Kannur, Kerala.(to be published)
- M.J Homer (2004) “Breast Imaging, Standard of care and the expert”,Radiologic Clinics of North America, Vol. 42(5), pp. 963-974.
- P.Tahmasebi and A.Hezarkhani (2011) “Application of a modular feed forward neural network for grade estimation”, Natural Resources Research, Vol. 20(1), pp. 25-32.
- Z.M Hera and D.F Gillies (2015) “ A review of feature selection and feature extraction methods applied on micro array data”, Advances on Bioinformatics, Vol. 2015, Article ID 198363, 13 pages. doi 10.1155/2015/198363
- I. Guyon, J. Weston, S.Barnhill and V.Vapnik, (2002) “Gene selection for cancer classification using support vector machines”, Mach.Learn, Vol. 46(1-3), pp. 389-422.
- T.Hayes, S.Usami et.al, (2015) “Using classification and regression Trees (CART) and random forest to analyze attrition: Results from two simulations”, Psychology Aging, Vol. 30(4), pp. 911-929.