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The objective of this paper is to propose a hybrid decision-making methodology based on affinity diagram, fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) to evaluate, rank and select the most appropriate cloud solutions to accommodate and manage big data projects. In fact, the strategic priority of many corporations consists in the creation of competitive advantages by using new available technologies, processes and governance mechanisms, such as big data and cloud computing. Since the technology is permanently subject to advances and developments, the question for many businesses is how to benefit from big data using the power of technical flexibility that cloud computing can provide. In this context, selecting the most adequate cloud solution to host big data projects is a complex issue that requires an extensive evaluation process. Thus, to assist users to efficiently select their most preferred cloud solution, we propose a hybrid decision-making methodology that meets these requirements in four stages. In the first stage, the identification of evaluation criteria is performed by a decision-making committee using Affinity Diagram. Due to the varied importance of the selected criteria, a FAHP process is used in the second stage to assign the importance weights for each criterion, while FTOPSIS process, in the third stage, employs these weighted criteria as inputs to evaluate and measure the performance of each alternative. In the last step, a sensitivity analysis is performed to evaluate the impact of criteria weights on the final rankings of alternatives.

The location selection for Landfill of Industrial Wastes (LIW) is a very significant task in waste management studies, which has significant impacts on sustainable development of the region. Furthermore, the selection of the appropriate and efficient sites for LIWs is an important multi-criteria decision making problem. The present document suggests an OLAP/GIS-Fuzzy AHP-TOPSIS based methodology for evaluation and selection of best sites for LIWs. In this respect, the candidate locations are specified based on the combination of On-Line Analytical Processing and Geographic Information System (OLAP/GIS). The Fuzzy Analytical Hierarchy Process (Fuzzy-AHP), a multi-criteria decision-making method is applied to analyze the structure of the problem and obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values in decision-making. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is taken into account to assess and rank the alternative locations. Finally, a hypothetical application of the proposed approach is illustrated by a case study of location selection for LIWs in Morocco. The results show that the proposed methodology can successfully achieve the aim of this work.

Geospatial Business Intelligence (Geospatial BI) is a system that combines multidimensional analysis and cartographic visualization. It plays an important role in decision making process for enterprises. Adopting such a comprehensive solution may result in the great investment decision for them, so great deal of attention should be given in the selection of the optimal system. As there are many impacting factors in the selection of Geospatial BI system, the same process is considered as a complex multi-criteria decision making problem. In this paper, we explore the application of an integrated methodology for the evaluation of various Geospatial BI alternatives. The proposed methodology integrates the three well-known decision-making techniques, namely Modified Delphi, fuzzy analytic hierarchical process (fuzzy-AHP), and preference ranking organization method for enrichment evaluations (PROMETHEE). In this respect, the modified Delphi is used to select the most impacting factors by a few decision-makers. The fuzzy-AHP is employed to analyze the structure of the problem and to obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values. Then, the PROMETHEE technique is used for optimal ranking of the alternative system choices. A step-by-step, numerical study is illustrated by using the proposed methodology on the decision making problem of a company that is faced to five Geospatial BI solutions. The results demonstrate that the proposed methodology can successfully accomplish our goal of this study.

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.

OLAP (Online Analytical Processing) system appears as a revolutionary technology that provides adequate analytic solutions for decision support. Using OLAP, analysts and policymakers are able to process and analyze data in an interactive, fast, and effective way according to several axes. This provides a clear vision of their business at any time and in real time. However, these systems suffer from certain limitations related to the consideration of both the multicriteria and fuzzy aspects of multidimensional data when making decisions. To overcome these limitations, we propose an integrated decision-making prototype based on OLAP system and multicriteria analysis (MCA) to generate a hybrid analysis process dealing with complex multicriteria decision-making situations. This proposed solution, based on our previous contributions, allows the analyst to extract data from OLAP data cube model and analyze them using OLAP operators and MCA methods.

Fuzzy multi-criteria group decision making (FMCGDM) process is usually used when a group of decision-makers faces imprecise data or linguistic variables to solve the problems. However, this process contains many methods that require many time-consuming calculations depending on the number of criteria, alternatives and decision-makers in order to reach the optimal solution. In this study, a web-based FMCGDM framework that offers decision-makers a fast and reliable response service is proposed. The proposed framework includes commonly used tools for multi-criteria decision-making problems such as fuzzy Delphi, fuzzy AHP and fuzzy TOPSIS methods. The integration of these methods enables taking advantages of the strengths and complements each method’s weakness. Finally, a case study of location selection for landfill waste in Morocco is performed to demonstrate how this framework can facilitate decision-making process. The results demonstrate that the proposed framework can successfully accomplish the goal of this study.

Actually, a set of ETL software (Extract, Transform and Load) is available to constitute a major investment market. Each ETL uses its own techniques for extracting, transforming and loading data into data warehouse, which makes the task of evaluating ETL software very difficult. However, choosing the right software of ETL is critical to the success or failure of any Business Intelligence project. As there are many impacting factors in the selection of ETL software, the same process is considered as a complex multi-criteria decision making (MCDM) problem. In this study, an application of decision-making methodology that employs the two well-known MCDM techniques, namely Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods is designed. In this respect, the aim of using AHP is to analyze the structure of the ETL software selection problem and obtain weights of the selected criteria. Then, TOPSIS technique is used to calculate the alternatives’ ratings. An example is given to illustrate the proposed methodology. Finally, a software prototype for demonstrating both methods is implemented.

The decisions support Systems are based on decision technologies enabling organizations to make better use of their data flow analysis by simplifying them. Indeed, OLAP (On Line Analytical Processing) tools such as decisional technology, offer the possibility of archiving, management, analysis and multidimensional modeling. However, they are limited in the consideration of the multicriteria and quality aspect of the decision problem. To overcome these limitations, we proposed in this research, a methodological approach for integrating multicriteria analysis in OLAP systems. Our aim of this approach is to create a hybrid model of data (OLAP / MCA), which will expand the capabilities of OLAP through the contribution of MCA as a tool for multi-criteria analysis. In order to validate our contribution, we present the results of experiments showing the efficiency and performance of our approach.

Landfill location selection is a multi-criteria decision problem and has a strategic importance for many regions. The conventional methods for landfill location selection are insufficient in dealing with the vague or imprecise nature of linguistic assessment. To resolve this problem, fuzzy multi-criteria decision-making methods are proposed. The aim of this paper is to use fuzzy TODIM (the acronym for Interactive and Multi-criteria Decision Making in Portuguese) and the fuzzy analytic hierarchy process (AHP) methods for the selection of landfill location. The proposed methods have been applied to a landfill location selection problem in the region of Casablanca, Morocco. After determining the criteria affecting the landfill location decisions, fuzzy TODIM and fuzzy AHP methods are applied to the problem and results are presented. The comparisons of these two methods are also discussed.