Generalized disjunctive programming models for the truck loading problem: A case study from the non-alcoholic beverages industry

Abstract

A real-world truck loading problem is considered for the major non-alcoholic beverages bottler in Argentina. The previous manual procedure to define the truck loading plan is improved by automatizing the process through a set of optimization models applying Generalized Disjunctive Programming. Most of the operational practices and restrictions are taken into account. Given that there is flexibility to load products in the trucks, a better use of their capacities is obtained and the balance of forces addressed avoids expensive penalties fees. By a set of examples, different metrics are tested and good quality results are obtained for an every-day practice.