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A multi-objective supplier selection framework based on user-preferences.

Federico Toffano1, Michele Garraffa2,3, Yiqing Lin4

  • 1Insight Centre for Data Analytics, School of Computer Science and IT, University College Cork, Cork, Ireland.

Annals of Operations Research
|January 17, 2022
PubMed
Summary
This summary is machine-generated.

This study presents an interactive framework for multi-criteria supplier selection using active learning. It efficiently guides decision-makers by learning preferences through pairwise comparisons, improving computation time.

Keywords:
Incremental elicitationMathematical programmingMulti-attribute utility theoryMulti-objective optimizationPreference elicitationSupplier selection

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Area of Science:

  • Operations Research
  • Decision Science
  • Supply Chain Management

Background:

  • Traditional multi-criteria supplier selection relies on pre-collected data and complex preference elicitation.
  • Existing methods can be time-consuming and may not fully capture nuanced decision-maker preferences.

Purpose of the Study:

  • To introduce an interactive framework for multi-criteria supplier selection.
  • To develop an active learning approach for preference elicitation in supplier selection.
  • To propose and evaluate novel query selection strategies for improved efficiency.

Main Methods:

  • An active learning loop framework that iteratively elicits decision-maker preferences.
  • Optimal solving of combinatorial problems with varying objective weights.
  • Introduction and comparison of two novel query selection strategies against a myopic approach.

Main Results:

  • The proposed framework demonstrates competitive performance in terms of convergence speed.
  • Novel query selection strategies offer improved computation time compared to existing methods.
  • Experimental validation on diverse instances confirms framework usability and efficiency for real-world applications.

Conclusions:

  • The interactive framework effectively guides multi-criteria supplier selection through active learning.
  • The novel query selection strategies enhance computational efficiency without sacrificing performance.
  • The framework is suitable for practical, real-world supplier selection scenarios.