Skip to main content
Log in

Multi-criteria Decisional Approach of the OLAP Analysis by Fuzzy Logic: Green Logistics as a Case Study

  • Research Article - Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

This study aims to propose a decision-making approach combining multi-criteria analysis and fuzzy logic within the online analytical processing data cube model (OLAP). Indeed, most decision-making systems are based on models of operational research. These models are often composed of quantitative data and postulate the existence of a single objective function (criterion) representing the preferences of decision-makers. However, in reality, we are faced with a more complex situation where several criteria (quantitative and/or qualitative) should be taken into account. It is therefore natural to consider different types of data (more criteria) in the design of OLAP cubes and decision-making systems. Multi-criteria decision analysis (MCDA) combined with fuzzy sets theory offers an efficient approach to solve complex decision problems. So we believe it is useful and necessary to envisage, for OLAP cubes, an optimized data model taking into account several criteria, on which we can apply new methods of MCDA. We end our contribution by applying the decision support process of this paper to propose a scheme of green logistics for large industrial zones in the city of Casablanca, Morocco.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Kimball R.: The Data Warehouse Toolkit. Wiley, Hoboken (1996)

    Google Scholar 

  2. Aligon J., Gallinucci E., Golfarelli M., Marcel P., Rizzi S.: A collaborative filtering approach for recommending OLAP sessions. Decis. Support Syst. 69, 20–30 (2015)

    Article  Google Scholar 

  3. Gray J., Chaudhuri S., Bosworth A., Layman A., Reichart D., Venkatrao M., Pellow F., Pirahesh H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Discov. 1, 29–53 (1997)

    Article  Google Scholar 

  4. Gkesoulis, D.; Vassiliadis, P.; Manousis, P.: CineCubes: aiding data workers gain insights from OLAP queries. Inf. Syst. (2015). doi:10.1016/j.is.2014.12.006

  5. Golfarelli M., Graziani S., Rizzi S.: Shrink: an OLAP operation for balancing precision and size of pivot tables. Data Knowl. Eng. 93, 19–41 (2014)

    Article  Google Scholar 

  6. Ben Ahmed, E.; Nabli, A.; Gargouri, F.: On line mining of cyclic association rules from parallel dimension hierarchies. Real World Data Min. Appl., pp. 31–50 (2014)

  7. Cuzzocrea A., Moussa R., Xu G.: OLAP*: effectively and efficiently supporting parallel OLAP over big data. Model Data Eng. 8216, 38–49 (2013)

    Article  Google Scholar 

  8. Song J., Guo C., Wang Z., Zhang Y., Yu G., Pierson J.M.: HaoLap: a Hadoop based OLAP system for big data. J. Syst. Softw. 102, 167–181 (2015)

    Article  Google Scholar 

  9. Kang W.L., Kim H.G., Lee Y.G.: Efficient indexing for OLAP query processing with MapReduce. Comput. Sci. Appl. 330, 783–788 (2015)

    Google Scholar 

  10. Dehne, F.; Kong, Q.; Rau-Chaplin, A.; Zaboli, H.; Zhou, R.: Scalable real-time OLAP on cloud architectures. J. Parallel Distrib. Comput. (2014). doi:10.1016/j.jpdc.2014.08.006

  11. Al-Aqrabi H., Liu L., Hill R., Antonopoulos N.: Cloud BI: future of business intelligence in the cloud. J. Comput. Syst. Sci. 81(1), 85–96 (2015)

    Article  MathSciNet  Google Scholar 

  12. Galindo J., Urrutia A., Piattini M.: Fuzzy Database Modeling, Design and Implementation. Idea Group Publishing, New York (2006)

    Book  Google Scholar 

  13. Galindo J.: New characteristics in FSQL, a fuzzy SQL for fuzzy databases. WSEAS Trans. Inf. Sci. Appl. 2(2), 161–169 (2005)

    MathSciNet  Google Scholar 

  14. González C., Tineo L., Urrutia A.: Fuzzy OLAP: a formal definition. Adv. Comput. Intell. 116, 189–198 (2009)

    Google Scholar 

  15. Kaya M., Alhajj R.: Development of multidimensional academic information networks with a novel data cube based modeling method. Inf. Sci. 265, 211–224 (2014)

    Article  Google Scholar 

  16. Loudcher, S.; Jakawat, W.; Morales, E.P.S.; Favre, C.: Combining OLAP and information networks for bibliographic data analysis: a survey. Scientometrics (2015). doi:10.1007/s11192-015-1539-0

  17. Lee C.K.H., Choy K.L., Ho G.T.S., Chin K.S., Law K.M.Y., Tse Y.K.: A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry. Expert Syst. Appl. 40(7), 2435–2446 (2013)

    Article  Google Scholar 

  18. Meyer, V.; Höpken, W.; Fuchs, M.; Lexhagen, M.: Integration of Data Mining Results into Multi-dimensional Data Models, Information and Communication Technologies in Tourism 2015, pp. 155–168. Springer, Berlin (2014)

  19. Somsuk N., Laosirihongthong T.: A fuzzy AHP to prioritize enabling factors for strategic management of university business incubators: resource-based view. Technol. Forecast. Soc. Change 85, 198–210 (2014)

    Article  Google Scholar 

  20. Chen J.F., Hsieh H.N., Do Q.H.: Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Appl. Soft Comput. 28, 100–108 (2015)

    Article  MATH  Google Scholar 

  21. Patil S.K., Kant R.: A fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge management adoption in supply chain to overcome its barriers. Expert Syst. Appl. 41(2), 679–693 (2014)

    Article  Google Scholar 

  22. Taylana O., Bafailb A.O., Abdulaala R.M.S., Kabli M.R.: Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Appl. Soft Comput. 17, 105–116 (2014)

    Article  Google Scholar 

  23. Beikkhakhian Y., Javanmardi M., Karbasian M., Khayambashi B.: The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods. Expert Syst. Appl. 42(15), 6224–6236 (2015)

    Article  Google Scholar 

  24. Wang C.H., Wang J.: Combining fuzzy AHP and fuzzy Kano to optimize product varieties for smart cameras: a zero-one integer programming perspective. Appl. Soft Comput. 22, 410–416 (2014)

    Article  Google Scholar 

  25. Calabrese A., Costa R., Menichini T.: Using fuzzy AHP to manage intellectual capital assets: an application to the ICT service industry. Expert Syst. Appl. 40(1), 3747–3755 (2013)

    Article  Google Scholar 

  26. Kubler S., Voisin A., Derigent W., Thomas A., Rondeau E., Framling K.: Group fuzzy AHP approach to embed relevant data on communicating material. Comput. Ind. 65(4), 675–692 (2014)

    Article  Google Scholar 

  27. Chen T.Y.: An interval type-2 fuzzy PROMETHEE method using a likelihood-based outranking comparison approach. Inf. Fusion 25, 105–120 (2015)

    Article  Google Scholar 

  28. Kilic H.S., Zaim S., Delen D.: Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods. Expert Syst. Appl. 42(5), 2343–2352 (2015)

    Article  Google Scholar 

  29. Wang X.: A comprehensive decision making model for the evaluation of green operations initiatives. Technol. Forecast. Soc. Change 95, 191–207 (2015)

    Article  Google Scholar 

  30. Parameshwaran R., Baskar C., Karthik T.: An integrated framework for mechatronics based product development in a fuzzy environment. Appl. Soft Comput. 27, 376–390 (2015)

    Article  Google Scholar 

  31. Zardari, N. H.; Ahmed, K.; Shirazi, S. M.; Yusop, Z. B.: Weighting Methods and Their Effects on Multi-criteria Decision Making Model Outcomes in Water Resources Management. Briefs in Water Science and Technology. Springer, Berlin (2015)

  32. Kilic H.S., Zaim S., Delen D.: Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods. Expert Syst. Appl. 42(5), 2343–2352 (2015)

    Article  Google Scholar 

  33. Zadeh L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  34. Saaty T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)

    Google Scholar 

  35. Taylor B.W.: Introduction to Management Science. Pearson Education Inc., New Jersey (2004)

    Google Scholar 

  36. Yang C.C., Chen B.S.: Key quality performance evaluation using fuzzy AHP. J. Chin. Inst. Ind. Eng. 21(6), 543–550 (2004)

    Google Scholar 

  37. Gumus A.T.: Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert Syst. Appl. 36(2), 4067–4074 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  38. Thalhammer T., Schrefl M., Mohania M.: Active data warehouses: complementing OLAP with analysis rules. Data Knowl. Eng. 39(3), 241–269 (2001)

    Article  Google Scholar 

  39. Kimball R., Ross M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edn. Wiley, Hoboken (2002)

    Google Scholar 

  40. Taha Z., Rostam S.: A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell. J. Intell. Manuf. 23(6), 2137–2149 (2012)

    Article  Google Scholar 

  41. Pentaho community, Mondrian. http://community.pentaho.com/projects/mondrian/. Accessed 3 August 2014

  42. Zhu, G.-N.; Hu, J.; Qi, J.; Gu, C.-C.; Peng, Y.-H.: An integrated AHP and VIKOR for design concept evaluation based on rough number. Adv. Eng. Inform. (2015). doi:10.1016/j.aei.2015.01.010

  43. Mosadegh R., Warnken J., Tomlinson R., Mirfenderesk H.: Comparison of fuzzy-AHP and AHP in a spatial multi-criteria decision making model for urban land-use planning. Comput. Environ. Urban Syst. 49, 54–65 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Omar Boutkhoum.

Electronic Supplementary Material

The Below is the Electronic Supplementary Material.

ESM 1 (DOCX 767 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boutkhoum, O., Hanine, M., Tikniouine, A. et al. Multi-criteria Decisional Approach of the OLAP Analysis by Fuzzy Logic: Green Logistics as a Case Study. Arab J Sci Eng 40, 2345–2359 (2015). https://doi.org/10.1007/s13369-015-1724-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-015-1724-8

Keywords

Navigation