A grey multi-objective linear model to find critical path of a project by using time, cost, quality and risk parameters

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dc.contributor.author Mahdiraji, Hannan Amoozad
dc.contributor.author Hajiagha, Seyed Hossein Razavi
dc.contributor.author Hashemi, Shide Sadat
dc.contributor.author Zavadskas, Edmundas Kazimieras
dc.contributor.other Ekonomická fakulta cs
dc.date.accessioned 2016-03-09
dc.date.available 2016-03-09
dc.identifier.issn 12123609
dc.identifier.uri https://dspace.tul.cz/handle/15240/13605
dc.description.abstract A project is a series of related activities which are organized to reach a defined goal or satisfy a certain need. Project management plays an important role in different fields of human life. The amount of resources spent on a project renders management of these resources a sensitive task. Determinant factors’ influencing the payoffs of a project mainly encompasses time, cost, quality and also the risk of each activity. Therefore, a critical path method is presented to find the longest path of a project completion time in order to draw managers’ attention to the critical activities. Critical path method is a well-known and widely accepted method to find the critical activities of a project and to concentrate on them for accomplishment of the project without any deviation. Classical critical path methods usually consider only a time factor, but growing complexity and importance of projects entail cost, quality and risk as the critical factors to be considered in project management. Due to the unavailability of certain information relating each factor of each activity, considering a novel approach to deal with such vague and unstable situations is really a controversial issue. Thus, another challenge of the project management contains uncertainty for approximating time, cost, quality, and risk factors of the project activities. Taking into account these two challenges, a grey multi-objective critical path model is proposed in this paper, where parameters of the activities are evaluated as grey numbers, dealing with their uncertainty. Meanwhile, a goal programming based method is illustrated to solve the problem of critical path identification, considering four considerable criteria including time, cost, quality, and risk. Eventually, a numerical example is represented to address applicability of the proposed method. en
dc.format text
dc.format.extent 49-61 s. cs
dc.language.iso en
dc.publisher Technická Univerzita v Liberci cs
dc.publisher Technical university of Liberec, Czech Republic en
dc.relation.ispartof Ekonomie a Management cs
dc.relation.ispartof Economics and Management en
dc.relation.isbasedon Hand, D., Mannila, H., Smyth, P. Principles of Data Mining. MIT Press, 2001. 584 pp. ISBN 978-0262082907.
dc.relation.isbasedon HAYS, W. Statistics. 4th ed. New York: CBS College Publishing, 1988. 750 p. ISBN 978-0030024641.
dc.relation.isbasedon HU, X.H., CERCONE, N. A data warehouse/online analytic processing framework for web usage mining and business intelligence reporting. International Journal of Intelligent Systems. 2004, Vol. 19, Iss. 7, pp. 585-606. ISSN 1098-111X. DOI: 10.1002/int.20012.
dc.relation.isbasedon Joshila GRACE, L., Maheswari, V., Nagamalai, D. Web Log Data Analysis and Mining. Advanced Computing. 2011, Vol. 133, pp. 459-469. ISSN 1865-0929. DOI: 10.1007/978-3-642-17881-8_44.
dc.relation.isbasedon Kapusta, J., Munk, M., Drlik, M. Cut-off time calculation for user session identification by reference length. In: Application of Information and Communication Technologies. IEEE, 2012. pp. 1-6. ISBN 978-1-4673-1739-9. DOI: 10.1109/ICAICT.2012.6398500.
dc.relation.isbasedon KEWEN, L. Analysis of preprocessing methods for web usage data. In: Measurement, Information and Control (MIC). Vol. 1. IEEE, 2012. pp. 383-386. ISBN 978-1-4577-1601-0. DOI: 10.1109/MIC.2012.6273276.
dc.relation.isbasedon LIU, B. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. 2nd ed. Berlin: Springer, 2011. 624 p. ISBN 978-3-642-19459-7.
dc.relation.isbasedon Maheswari, B., Sumathi, P. A New Clustering and Preprocessing for Web Log Mining. In: Computing and Communication Technologies (WCCCT). IEEE, 2014. pp. 25-29. ISBN 978-1-4799-2876-7. DOI: 10.1109/WCCCT.2014.67.
dc.relation.isbasedon Matcher (Java Platform SE 7) [online]. 2014 [cit. 2015-05-10]. Available from: http://docs.oracle.com/javase/7/docs/api/java/util/regex/Matcher.html.
dc.relation.isbasedon Munk, M., Kapusta, J., Švec, P. Data preprocessing evaluation for web log mining: Reconstruction of activities of a web visitor. Procedia Computer Science. 2010, Vol. 1, Iss. 1, pp. 2273-2280. ISSN 1877-0509. DOI:10.1016/j.procs.2010.04.255.
dc.relation.isbasedon Munk, M., Kapusta, J., Švec, P., Turčáni, M. Data advance preparation factors affecting results of sequence rule analysis in Web Log Mining. E+M Ekonomie a Management. 2010, Vol. 13, Iss. 4, pp. 143-160. ISSN 1212-3609.
dc.relation.isbasedon Nithya, P., Sumathi, P. Novel Pre-Processing Technique for Web Log Mining by Removing Global Noise, Cookies and Web Robots. International Journal of Computer Applications. 2012, Vol. 53, Iss. 17, pp. 1-6. ISSN 0975-8887. DOI: 10.5120/8510-1684.
dc.relation.isbasedon Pamutha, T., Chimphlee, S., Kimpan, C., Sanguansat, P. Data preprocessing on Web Server Log Files for Mining User Access Patterns. International Journal of Research and Reviews in Wireless Communications. 2012, Vol. 2, Iss. 2, pp. 92-98. ISSN 2046-6447. Available also from http://sci-tech.dusit.ac.th/page/research/siriporn.pdf.
dc.relation.isbasedon Patil, P., Patil, U. Preprocessing of web server log file for web mining. World Journal of Science and Technology. 2012, Vol. 2, Iss. 3, pp. 14-18. ISSN 2231-2587.
dc.relation.isbasedon Pilkova, A., Volna, J., Papula, J., Holienka, M. The Influence of Intellectual Capital on Firm Performance Among Slovak SMEs. In: Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning (ICICKM-2013), Reading: Academic Conferences and Publishing International Limited, 2013. pp. 329-338. ISBN 978-1-909507-80-7.
dc.relation.isbasedon Poggi, N., Muthusamy, V., Carrera, D., Khalaf, R. Business process mining from e-commerce web logs. Lecture Notes in Computer Science. 2013, Vol. 8094, pp. 65-80. ISSN 0302-9743. DOI: 10.1007/978-3-642-40176-3_7.
dc.relation.isbasedon Reddy, K., Varma, G., Babu, I. Preprocessing the web server logs: an illustrative approach for effective usage mining. ACM SIGSOFT Software Engineering Notes. 2012, Vol. 37, Iss. 3, pp. 1-5. DOI: 10.1145/180921.2180940.
dc.relation.isbasedon RUD, O. Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy. Hoboken, NJ: Wiley & Sons, 2009. 283 p. ISBN 978-0-470-39240-9.
dc.relation.isbasedon Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M. A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis. INFORMS Journal on Computing. 2003, Vol. 15, Iss. 2, pp. 171-190. ISBN 1526-5528. DOI: 10.1287/ijoc.15.2.171.14445.
dc.relation.isbasedon Sumathi, C., Padmaja Valli, R., Santhanam, T. An overview of preprocessing of web log files for web usage mining. Journal of Theoretical and Applied Information Technology. 2011, Vol. 34, Iss. 1, pp. 88-95. ISSN 1992-8645.
dc.relation.isbasedon Witten, I., Frank, E. Data Mining: Practical Machine Learning Tools and Techniques. 1 st ed. Morgan Kaufmann Publishers Inc., 2000. ISBN 978-1558605527.
dc.relation.isbasedon Yadav, M.P., Feeroz, M., Yadav, V.K. Mining the customer behavior using web usage mining in e-commerce. In: Third international conference on computing communication & networking technologies (ICCCNT). IEEE, 2012. pp. 1-5. DOI: 10.1109/ICCCNT.2012.6395938.
dc.rights CC BY-NC
dc.subject SMEs en
dc.subject internationalization en
dc.subject success en
dc.subject key factors en
dc.subject success evaluation en
dc.subject.classification C02
dc.subject.classification C61
dc.subject.classification M11
dc.title A grey multi-objective linear model to find critical path of a project by using time, cost, quality and risk parameters en
dc.type Article en
dc.date.defense 2016-03-09
dc.publisher.abbreviation TUL
dc.relation.isrefereed true
dc.identifier.doi 10.15240/tul/001/2016-1-004
dc.identifier.eissn 2336-5604
local.relation.volume 19
local.relation.issue 1
local.relation.abbreviation E&M en
local.relation.abbreviation E+M cs
local.faculty Faculty of Economics
local.citation.spage 49
local.citation.epage 61
local.access open
local.fulltext yes


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