Supplier selection based on hierarchical potential support vector machine

Guo, Xuesong; Yuan, Zhiping; Tian, Bojing

doi:10.1016/j.eswa.2008.08.074

Cited by 82 publications

(40 citation statements)

References 25 publications

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“…Quality of material [6,21, Price of material [6,21,[26][27][28][29][30][31][33][34][35][36][37][38][39][40][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] Certification of products [26,31,40,42,44,50,52,57] Delivery time [21,[26][27][28][29][30][31][33][34][35][36]…”

Section: Criteria Referencesmentioning

confidence: 99%

Novel Integrated Multi-Criteria Model for Supplier Selection: Case Study Construction Company

et al. 2017

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Supply chain presents a very complex field involving a large number of participants. The aim of the complete supply chain is finding an optimum from the aspect of all participants, which is a rather complex task. In order to ensure optimum satisfaction for all participants, it is necessary that the beginning phase consists of correct evaluations and supplier selection. In this study, the supplier selection was performed in the construction company, on the basis of a new approach in the field of multi-criteria model. Weight coefficients were obtained by DEMATEL (Decision Making Trial and Evaluation Laboratory) method, based on the rough numbers. Evaluation and the supplier selection were made on the basis of a new Rough EDAS (Evaluation based on Distance from Average Solution) method, which presents one of the latest methods in this field. In order to determine the stability of the model and the applicability of the proposed Rough EDAS method, an extension of the COPRAS and MULTIMOORA method by rough numbers was also performed in this study, and the findings of the comparative analysis were presented. Besides the new approaches based on the extension by rough numbers, the results are also compared with the Rough MABAC (MultiAttributive Border Approximation area Comparison) and Rough MAIRCA (MultiAttributive Ideal-Real Comparative Analysis). In addition, in the sensitivity analysis, 18 different scenarios were formed, the ones in which criteria change their original values. At the end of the sensitivity analysis, SCC (Spearman Correlation Coefficient) of the obtained ranges was carried out, confirming the applicability of the proposed approaches.

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Section: Criteria Referencesmentioning

confidence: 99%

Novel Integrated Multi-Criteria Model for Supplier Selection: Case Study Construction Company

et al. 2017

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“…Indeed, the selection of suitable suppliers is Sustainability 2018, 10, 564 3 of 17 adopted by Sadeghieh [30], while reference [31] combined SVM with hierarchical system of feature to address the problem of supplier selection.…”

Section: Introductionmentioning

confidence: 99%

Supplier Selection Study under the Respective of Low-Carbon Supply Chain: A Hybrid Evaluation Model Based on FA-DEA-AHP

Zhang

2018

Sustainability

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Abstract:With the development of global environment and social economy, it is an indispensable choice for enterprises to achieve sustainable growth through developing low-carbon economy and constructing low-carbon supply chain. Supplier is the source of chain, thus selecting excellent low-carbon supplier is the foundation of establishing efficient low-carbon supply chain. This paper presents a novel hybrid model for supplier selection integrated factor analysis (FA), data envelopment analysis (DEA), with analytic hierarchy process (AHP), namely FA-DEA-AHP. First, an evaluation index system is built, incorporating product level, qualification, cooperation ability, and environmental competitiveness. FA is utilized to extract common factors from the 18 pre-selected indicators. Then, DEA is applied to establish the pairwise comparison matrix and AHP is employed to rank these low-carbon suppliers comprehensively and calculate the validity of the decision-making units. Finally, an experiment study with seven cement suppliers in a large industrial enterprise is carried out in this paper. The results reveal that the proposed technique can not only select effective suppliers, but also realize a comprehensive ranking. This research has enriched the methodology of low-carbon supplier evaluation and selection, as well as owns theoretical value in exploring the coordinated development of low-carbon supply chain to some extent.

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“…Supply chain management spans all the movement and storage of raw materials, work-in-process inventory, and finished goods from the point of origin to the point of consumption. To select suppliers, researchers have proposed various methods such as data envelopment analysis (DEA) (Hadi-Vencheh and Niazi-Motlagh 2011), analytical hierarchy process (AHP) (Perić et al 2013), fuzzy analytical hierarchy process (FAHP) (Kilincci andOnal 2011, Chamodrakas et al 2010), analytic network process (ANP) (Lin 2009), technique for order performance by similarity to ideal solution (TOPSIS) (Mokhtarian and Hadi-Vencheh 2012), game theory (Agha Mohammad Ali Kermani et al 2012), potential support vector machine (P-SVM) (Guo et al 2009), preference programming (PP), fuzzy logic (Florez-Lopez 2007), fuzzy case-based reasoning (CBR) (Faez et al 2009), smimulated annealing (Che 2012), etc.. Aissaoui et al (2007) gave a comprehensive literature review of the supplier selection and order lot-sizing modeling. The review covers the entire purchasing process, considers both parts and services outsourcing activities, and covers internet-based procurement environments.…”

Section: Introductionmentioning

confidence: 99%

Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study

Heidarzade

Mahdavi

Mahdavi–Amiri

2016

Applied Soft Computing

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Supplier selection based on hierarchical potential support vector machine

Cited by 82 publications

References 25 publications

Novel Integrated Multi-Criteria Model for Supplier Selection: Case Study Construction Company

Novel Integrated Multi-Criteria Model for Supplier Selection: Case Study Construction Company

Supplier Selection Study under the Respective of Low-Carbon Supply Chain: A Hybrid Evaluation Model Based on FA-DEA-AHP

Supplier selection using a clustering method based on a new distance for interval type-2 fuzzy sets: A case study

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