Could service provider selection with ranked attribute weights: a PROMETHEE II-based study

The growing number of cloud services offered by different companies imposes new decision-making challenges to implement since different services can perform well for different performance criteria.This may suggest the use of multicriteria decision processes to identify the most appropriate cloud solution based on decision-maker (DM) preferences.However, depending on the multicriteria method adopted, it requires the precise estimation of criteria weights.For decision contexts in which the DM does not feel able to indicate or cannot specify the exact weight values, this paper considers the use of surrogate weights within the PROMETHEE II method to obtain a ranking of cloud service providers based on a set of performance criteria.We perform a numerical simulation using a quality service dataset composed of 2507 web services suppliers, and realistic data from a make-to-order (MTO) Brazilian company to demonstrate the usefulness of the decision approach for addressing the cloud service selection problem.

Authors

Institutions

Publication Details

Journal
Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção
Published
2024-11-07
DOI
https://doi.org/10.14488/enegep2024_tn_st_413_2030_47413
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Could service provider selection with ranked attribute weights: a PROMETHEE II-based study

Isaac Pergher, Macáliston Gonçalves Da Silva, LUCIMÁRIO GOIS DE OLIVEIRA SILVA, Diego A. de J. Pacheco
Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção
Multi-Criteria Decision Making
article

Could service provider selection with ranked attribute weights: a PROMETHEE II-based study

Isaac Pergher, Macáliston Gonçalves Da Silva, LUCIMÁRIO GOIS DE OLIVEIRA SILVA, Diego A. de J. Pacheco
article en

Abstract

The growing number of cloud services offered by different companies imposes new decision-making challenges to implement since different services can perform well for different performance criteria.This may suggest the use of multicriteria decision processes to identify the most appropriate cloud solution based on decision-maker (DM) preferences.However, depending on the multicriteria method adopted, it requires the precise estimation of criteria weights.For decision contexts in which the DM does not feel able to indicate or cannot specify the exact weight values, this paper considers the use of surrogate weights within the PROMETHEE II method to obtain a ranking of cloud service providers based on a set of performance criteria.We perform a numerical simulation using a quality service dataset composed of 2507 web services suppliers, and realistic data from a make-to-order (MTO) Brazilian company to demonstrate the usefulness of the decision approach for addressing the cloud service selection problem.

Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção
Universidade Federal de Pernambuco (BR), Universidade Luterana do Brasil (BR)
Openalex Percentile: Top 76%
Multi-Criteria Decision Making
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Could service provider selection with ranked attribute weights: a PROMETHEE II-based study — Isaac Pergher, Macáliston Gonçalves Da Silva, et al. · Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção (2024) | TGRS Research Map | TGRS