New distributed-topsis approach for multi-criteria decision-making problems in a big data context

被引:11
|
作者
Lamrini, Loubna [1 ]
Abounaima, Mohammed Chaouki [1 ]
Alaoui, Mohammed Talibi [1 ]
机构
[1] Sidi Mohammed Ben Abdellah Univ, Fac Sci & Technol, Lab Intelligent Syst & Applicat, Fes, Morocco
关键词
Multi-criteria decision making (MCDM); TOPSIS; MapReduce; Apache Spark; Apache Hadoop; PySpark; Dataproc; Google cloud platform (GCP);
D O I
10.1186/s40537-023-00788-3
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Nowadays, the online environment is extra information-rich and allows companies to offer and receive more and more options and opportunities in multiple areas. Thus, decision-makers have abundantly available alternatives to choose from the best one or rank from the most to the least preferred. However, in the multicriteria decision-making field, most tools support a limited number of alternatives with as narrow criteria as possible. Decision-makers are forced to apply a screening or filtering method to reduce the size of the problem, which will slow down the process and eliminate some potential alternatives from the rest of the decision-making process. Implementing MCDM methods in high-performance parallel and distributed computing environments becomes crucial to ensure the scalability of multicriteria decision-making solutions in Big Data contexts, where one can consider a vast number of alternatives, each being described on the basis of a number of criteria.In this context, we consider TOPSIS one of the most widely used MCDM methods. We present a parallel implementation of TOPSIS based on the MapReduce paradigm. This solution will reduce the response time of the decision-making process and facilitate the analysis of the robustness and sensitivity of the method in a high-dimension problem at a reasonable response time.Three multicriteria analysis problems were evaluated to show the proposed approach's computational efficiency and performance. All experiments are carried out within GCP's Dataproc, a service allowing the execution of Apache Hadoop and Spark tasks in Google Cloud. The results of the tests obtained are very significant and promising.
引用
收藏
页数:21
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