Logical and Generalized Disjunctive Programming for Supplier and Contract Selection under Provision Uncertainty

Supply-chain optimization is a key issue in gainnig competitiveness in today's economies where buyers and suppliers develop long-term relationships. Final agreements are formalized by signing contracts involving the purchasing of large amounts of materials, taking advantage of economies of scale. Although establishing agreements with suppliers is definitely an important step in reducing uncertainty, uncertainty never completely disappears. For that reason, the problem addressed in this article is the delivery and purchase optimization in a supply chain under provision uncertainty. Several decisions are presented in this problem that can naturally be posed in terms of of discrete decision models and generalized disjunctive programming. Uncertainty is modeled through a new approach that avoids the drawbacks of traditional methods. The goal pursued in this article is to contribute to better raw-material usage and tactical decisions to face an uncertain provision process in the supply chain.