New models of supply chain network design by different decision criteria under hybrid uncertainties

Abstract

Supply chain network design is to determine factories and distribution centers to produce and distribute products, in order to satisfy the demand of customers to some extent. Supply chain network design plays an important strategic role in supply chain management and faces kinds of uncertainties, such as uncertain demand and cost. In order to deal with the impact of uncertain factors, this paper considers the supply chain network design under hybrid uncertainties, namely, the objective randomness of retailers’ demand and the subjective uncertainties of operating costs. We construct three models to handle managers’ different needs. They are the expected cost minimization model that minimizes the expected total cost, the $\beta$-cost minimization model that minimizes the $\beta$-cost which means that the chance measure of the actual cost not exceeding the cost is not less than the confidence level $\beta$, and the chance measure maximization model that maximizes the chance measure of the actual cost not exceeding the given cost. This paper then transforms them into deterministic classes by uncertainty theory, and several numerical examples are presented to verify the validity of the models and algorithm.