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Application of Incomplete Analytic Hierarchy Process and Choquet Integral to Select the best Supplier and Order Allocation in Petroleum Industry
Author(s) -
Nazanin Maleki,
Maryam Bagherifard,
Mohammad Reza Gholamian
Publication year - 2020
Publication title -
international journal of engineering. transactions b: applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 17
ISSN - 1728-144X
DOI - 10.5829/ije.2020.33.11b.20
Subject(s) - analytic hierarchy process , choquet integral , multiple criteria decision analysis , ranking (information retrieval) , selection (genetic algorithm) , operations research , computer science , mathematical optimization , pareto principle , rank (graph theory) , analytic network process , constraint (computer aided design) , mathematics , artificial intelligence , fuzzy logic , geometry , combinatorics
In a powerful industry, supplier selection is one of the complex processes that can increase productivity and competitive advantages. Supplier selection includes different quantitative, qualitative, and also interactive criteria. In addition, the selection process has always faced with inadequate and incomplete data. Multi-criteria decision making (MCDM) is a useful approach that can be applied, for addressing the opting problems of a supplier considering mentioned issues. In this approach, the interaction between criteria can be considered with several methods, such as Choquet integral, which is a practical method for decision ranking. Also, incomplete data can be covered with incomplete analytic hierarchy process (AHP) method. Therefore, in this study, an application of Choquet integral along with incomplete AHP method is provided for supplier selection problem at the petroleum industry. After achieving the ranking rate of suppliers, requested orders are assigned to preferred suppliers by using multi-objective linear programming (MOLP) model and ɛ-constraint method to generate the Pareto optimal points. As a result, supplier 3 with weight 0.8274 was the most preferred supplier in which 50% of total orders was assigned to this supplier as the best selection. doi: 10.5829/ije.2020.33.11b.20

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