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

Simple item page
dc.contributor.authorMahdiraji, Hannan Amoozad
dc.contributor.authorHajiagha, Seyed Hossein Razavi
dc.contributor.authorHashemi, Shide Sadat
dc.contributor.authorZavadskas, Edmundas Kazimieras
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2016-03-09
dc.date.available2016-03-09
dc.date.defense2016-03-09
dc.description.abstractA 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.formattext
dc.format.extent49-61 s.cs
dc.identifier.doi10.15240/tul/001/2016-1-004
dc.identifier.eissn2336-5604
dc.identifier.issn12123609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/13605
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.isbasedonHand, D., Mannila, H., Smyth, P. Principles of Data Mining. MIT Press, 2001. 584 pp. ISBN 978-0262082907.
dc.relation.isbasedonHAYS, W. Statistics. 4th ed. New York: CBS College Publishing, 1988. 750 p. ISBN 978-0030024641.
dc.relation.isbasedonHU, 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.isbasedonJoshila 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.isbasedonKapusta, 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.isbasedonKEWEN, 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.isbasedonLIU, 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.isbasedonMaheswari, 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.isbasedonMatcher (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.isbasedonMunk, 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.isbasedonMunk, 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.isbasedonNithya, 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.isbasedonPamutha, 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.isbasedonPatil, 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.isbasedonPilkova, 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.isbasedonPoggi, 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.isbasedonReddy, 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.isbasedonRUD, 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.isbasedonSpiliopoulou, 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.isbasedonSumathi, 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.isbasedonWitten, I., Frank, E. Data Mining: Practical Machine Learning Tools and Techniques. 1 st ed. Morgan Kaufmann Publishers Inc., 2000. ISBN 978-1558605527.
dc.relation.isbasedonYadav, 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.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectSMEsen
dc.subjectinternationalizationen
dc.subjectsuccessen
dc.subjectkey factorsen
dc.subjectsuccess evaluationen
dc.subject.classificationC02
dc.subject.classificationC61
dc.subject.classificationM11
dc.titleA grey multi-objective linear model to find critical path of a project by using time, cost, quality and risk parametersen
dc.typeArticleen
local.accessopen
local.citation.epage61
local.citation.spage49
local.facultyFaculty of Economics
local.fulltextyes
local.relation.abbreviationE&Men
local.relation.abbreviationE+Mcs
local.relation.issue1
local.relation.volume19
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
EM_1_2016_4.pdf
Size:
1.04 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Číslo 1