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

Federico Toffano (), Michele Garraffa (), Yiqing Lin (), Steven Prestwich (), Helmut Simonis () and Nic Wilson ()
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Federico Toffano: University College Cork
Michele Garraffa: United Technologies Research Centre
Yiqing Lin: United Technologies Research Centre
Steven Prestwich: University College Cork
Helmut Simonis: University College Cork
Nic Wilson: University College Cork

Annals of Operations Research, 2022, vol. 308, issue 1, No 22, 609-640

Abstract: 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.

Keywords: Supplier selection; Preference elicitation; Incremental elicitation; Multi-attribute utility theory; Multi-objective optimization; Mathematical programming (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10479-021-04251-5

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