An Interactive Polyhedral Approach for Multi-objective Combinatorial Optimization with Incomplete Preference Information

Benabbou, Nawal; Lust, Thibaut

doi:10.1007/978-3-030-35514-2_17

Cited by 1 publication

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References 24 publications

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“…A recent paper (Benabbou and Lust 2019) proposed a similar interactive preference elicitation procedure, i.e., the queries for the user are computed using the solutions associated with the extreme points of the polytope representing the preferences learned so far. From the experimental results, it looks like the best method for query selection was Max-Dist, i.e., computing the query as the pair of solutions that maximize the corresponding Euclidean distance.…”

Section: Max Min Discrepancy (Mmd): Select a Querymentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Max Min Discrepancy (Mmd): Select a Querymentioning

confidence: 99%