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

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Date
2021-10-21
Authors
Toffano, Federico
Garraffa, Michele
Lin, Yiqing
Prestwich, Steven D.
Simonis, Helmut
Wilson, Nic
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Publisher
Springer
Published Version
10.1007/s10479-021-04251-5
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Abstract
This paper introduces an interactive framework to guide decision-makers in a multi-criteria supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit the decision-maker’s preferences among the criteria by processing pre-collected data from different stakeholders. We propose a different approach where the preferences are elicited through an active learning loop. At each step, the framework optimally solves a combinatorial problem multiple times with different weights assigned to the objectives. Afterwards, a pair of solutions among those computed is selected using a particular query selection strategy, and the decision-maker expresses a preference between them. These two steps are repeated until a specific stopping criterion is satisfied. We also introduce two novel fast query selection strategies, and we compare them with a myopically optimal query selection strategy. Computational experiments on a large set of randomly generated instances are used to examine the performance of our query selection strategies, showing a better computation time and similar performance in terms of the number of queries taken to achieve convergence. Our experimental results also show the usability of the framework for real-world problems with respect to the execution time and the number of loops needed to achieve convergence.
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Keywords
Supplier selection , Preference elicitation , Incremental elicitation , Multi-attribute utility theory , Multi-objective optimization , Mathematical programming
Citation
Toffano, F., Garraffa, M., Lin, Y., Prestwich, S. D., Simonis, H. and Wilson, N. (2021) 'A multi-objective supplier selection framework based on user-preferences', Annals of Operations Research, 308, pp. 609-640. doi: 10.1007/s10479-021-04251-5
Link to publisher’s version
https://doi.org/10.1007/s10479-021-04251-5
URI
https://hdl.handle.net/10468/13409
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Computer Science - Journal Articles
Insight Centre for Data Analytics - Journal Articles
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© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.