Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2018 | 348 | 144-155

Article title

Metoda SAW z przedziałowymi danymi i wagami uzyskanymi za pomocą przedziałowej entropii Shannona

Content

Title variants

EN
The SAW method with interval data and the weights obtained using the interval Shannon’s entropy

Languages of publication

PL

Abstracts

PL
Celem artykułu jest rozszerzenie metody SAW na problemy podejmowania decyzji z danymi przedziałowymi. Pozwala to na modelowanie problemów rzeczywistych, w których nie można dokładnie zmierzyć danych. Zaproponowana metoda wykorzystuje entropię Shannona, która jest jedną z metod wyznaczania obiektywnych wag kryteriów. Pozwala to uniknąć subiektywizmu i nieprecyzyjności spowodowanej niekompletną wiedzą, osądami, opiniami i preferencjami decydentów.
The aim of the paper is to extend the SAW method for decision-making problems with interval data. This allows modeling real life problems in which data cannot be measured precisely. Moreover, proposed method uses Shannon’s entropy which is one of the methods for finding objective weights of criteria. It allows to avoid subjectivity and imprecision caused by incomplete knowledge, judgments, opinions and preferences of decision makers.

Year

Volume

348

Pages

144-155

Physical description

Contributors

  • Politechnika Białostocka. Wydział Informatyki. Katedra Matematyki

References

  • Abdullah L., Adawiyah C.W.R. (2014), Simple Additive Weighting Methods of Multicriteria Decision Making and Applications: A Decade Review, “International Journal of Information Processing and Management”, 5/1, s. 39-49.
  • Abdullah L., Jamal N.J. (2010), Determination of Weights for Health Related Quality of Life Indicators Among Kidney Patients: A Fuzzy Decision Making Method, “Applied Research in Quality of Life”, 6(4), s. 349-361.
  • Chen C.T. (2000), Extension of the TOPSIS for Group Decision Making Under Fuzzy Environment, “Fuzzy Sets and Systems”, 114(1), s. 1-9.
  • Churchman C.W., Ackoff R.L. (1954), An Approximate Measure of Value, “Journal of Operations Research Society of America”, 2(1), s. 172-187.
  • Gupta S., Gupta A. (2012), A Fuzzy Multi Criteria Decision Making Approach for Vendor Evaluation in a Supply Chain, “Interscience Management Review”, 2(3), s. 10-16.
  • Hu B.Q., Wang S. (2006), A Novel Approach in Uncertain Programming Part I: New Arithmetic and Order Relation for Interval Numbers, “Journal of Industrial and Management Optimization”, 2(4), s. 351-371.
  • Hwang C.L., Yoon K. (1981), Multiple Attribute Decision Making, Lecture Notes in Economics and Mathematical Systems, Springer.
  • Jahanshahloo G.R., Lotfi F.H., Izadikhah M. (2006), An Algorithmic Method to Extend TOPSIS for Decision-making Problems with Interval Data, “Applied Mathematics and Computation”, 175, s. 1375-1384.
  • Kacprzak D. (2017), Objective Weights Based on Ordered Fuzzy Numbers for Fuzzy Multiple Criteria Decision Making Methods, “Entropy”, 19, s. 373.
  • Kobryń A. (2014), Wielokryterialne wspomaganie decyzji w gospodarowaniu przestrzenią, Difin, Warszawa.
  • Lin H.Y., Liao C.J., Chang Y.H. (2010), Applying Fuzzy Simple Additive Weighting System to Health Examination Institution Location Selection, “IEEE International Conference on Industrial Engineering and Engineering Management”, s. 646-650.
  • Lotfi F.H., Fallahnejad R. (2010), Imprecise Shannon’s Entropy and Multi Attribute Decision Making, “Entropy”, 12, s. 53-62.
  • Moore R.E., Kearfott R.B., Cloud M.J. (2009), Introduction to Interval Analysis, SIAM.
  • Rajaie H., Hazrati A., Rashidi A. (2010), Evaluation of Construction Contractors in Developing Countries Using Fuzzy SAW Method, “Proceedings of the International Conference on Computing Civil and Building Engineering”, s. 283-293.
  • Roszkowska E., Kacprzak D. (2016), The Fuzzy SAW and Fuzzy TOPSIS Procedures Based on Ordered Fuzzy Numbers, “Information Sciences”, 369, s. 564-584.
  • Rudnik K., Kacprzak D. (2017), Fuzzy TOPSIS Method with Ordered Fuzzy Numbers for Flow Control in a Manufacturing System, “Applied Soft Computing”, 52, s. 1020-1041.

Document Type

Publication order reference

Identifiers

ISSN
2083-8611

YADDA identifier

bwmeta1.element.cejsh-7b5f1c1d-31b1-484d-8d84-26c2bf19e876
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.