Journal Paper

Paper Information

Journal: مدیریت صنعتی
Year:1395 | Volume:8 | Issue:2
Start Page:191 | End Page:216

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Information Journal Paper

Title

AN EFFICIENT PREFERENCE LEARNING METHOD BASED ON ELECTRE TRI MODEL FOR MULTI-CRITERIA INVENTORY

Pages

 Start Page 191 | End Page 216

Keywords

PARTICLE SWARM OPTIMIZATION (PSO)Q1

Abstract

 The multiple criteria ABC inventory analysis method is a well-known inventory management method for inventory classification. In most ABC classification techniques, a completely compensatory approach is adopted to classification problems with criteria aggregation. Hence, attention should be paid to noncompensatory approaches with criteria aggregation. Our literature review revealed a lack of research on the non-compensatory approach to this problem. The ELECTRE TRI model is based on outranking relations and adopts a compensatory approach to calculations.However, this model has not been popular in determining preferences of decision makers (parameters) due to its complexity and costly nature. To solve the aforementioned problems, a method was proposed in this research which uses the particle swarm optimization algorithm and simultaneously learns all of the decision maker’s preferences from the data through an evolutionary process and uses it in inventory classification. Unlike the standard data mining models, which carry out nominal classifications, the proposed method offers ABC inventory classification. Results of testing the proposed method on inventory datasets revealed its potential to compete with other standard classification models.

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