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

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.

A comprehensive framework and literature review of supplier selection under different purchasing strategies

“…A multi-objective setting, with regards to a goal programming variant, has been widely adopted to solve multi-objective programming with a linear function such as preemptive goal programming (PGP), nonpreemptive goal programming (non-PGP), weighted fuzzy goal programming (WF-GP) (Choudhary and Shankar, 2014), fuzzy relaxed normalized goal programming (F-RNGP) (Jadidi et al, 2014), improved multi-choice goal programming (MCGP) (Jadidi et al, 2015), weighted and min-max MCGP (Ho, 2019), revised multi-segment goal programming (Karimi and Rezaeinia, 2014), and (interactive) fuzzy goal programming ((I) F-GP) (Kazemi et al, 2015;Wong, 2020;Sheikhalishahi and Torabi, 2014), fuzzy multi-objective linear programming (F-MOLP) (Erginel and Gecer, 2016;Nazari-Shirkouhi et al, 2013), MOMILP Toffano et al, 2021), bi-objective DEA (Goswami and Ghadge, 2020), and PSO (Assadipour and Razmi, 2013). Furthermore, several studies considered multi-objective (goal) programming with non-linear cost functions.…”

Hybrid MCDM and simulation-optimization for strategic supplier selection

“…Recent literature has focused on strategic supplier selection integrating order allocation. Singh (2014) and Toffano et al (2022) determined supplier selection and order allocation by considering quality, price, delivery, and consistency. Ayhan and Kilic (2015) evaluated suppliers according to quality, price, delivery, and after-sales performance.…”

Minimality and comparison of sets of multi-attribute vectors

“…Parameterised preference models are commonly used with interactive preference elicitation approaches (see, e.g., [9,30,38,54,57]). In this context, the purpose is to explore the alternatives based on different interactions with the decision-maker and without listing all the available alternatives.…”

“…This framework is fairly general; for instance, the utility function may be based on a decomposition of utility, using, for example, an additive representation for a combinatorial problem (e.g., [ 37 , 42 , 58 , 62 ]). Also, could represent the expected utility of alternative given that is the correct user model, based on a probabilistic model with parameter , for example in a multi-objective influence diagram [ 22 , 41 , 43 ], with corresponding to a policy.…”