Title: Scenario-based stochastic model for supplier selection and order allocation under disruption risk and quantity discount

Authors: Faiza Hamdi; Faouzi Masmoudi

Addresses: College of Business, University of Jeddah, 21959, P.O. Box: 34, Saudi Arabia ' Laboratoire de recherche de Mécanique, Univesity of Sfax, Modélisation et Production, Sfax 3038, Tunisia

Abstract: In this paper, we develop two stochastic mixed integers linear programming (SMILP) models for supplier selection under disruption risk considering different capacity, failure probability, uncertain demand and quantity discounts. The suppliers are assumed domestic suppliers and global suppliers. The obtained combinatorial stochastic optimisation problem is formulated as a mixed integer program with conditional value-at-risk technique (CVaR). Numerical examples and computational results are presented. The proposed models can optimise the present problem through an estimated value at risk (VaR) and minimised CVaR simultaneously. The computational results reveal that the proposed models allow the decision maker to make an efficient selection of suppliers under disruption risk. Results also show that the decisions are not univocal because they depend on the risk proneness of the decision maker.

Keywords: selection supplier; disruption risk; stochastic mixed integer linear programming; total quantity discount; VaR; value-at-risk technique; CVaR; conditional value-at-risk technique; neutral risk; aversion risk.

DOI: 10.1504/IJRAM.2022.128710

International Journal of Risk Assessment and Management, 2022 Vol.25 No.1/2, pp.84 - 102

Accepted: 03 Mar 2021
Published online: 02 Feb 2023 *