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

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Toffano, Federico
Garraffa, Michele
Lin, Yiqing
Prestwich, Steven D.
Simonis, Helmut
Wilson, Nic
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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.
Supplier selection , Preference elicitation , Incremental elicitation , Multi-attribute utility theory , Multi-objective optimization , Mathematical programming
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
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