Date of Original Version
Abstract or Description
In this paper the long-term scheduling of a real world multi-product single stage continuous process for manufacturing glass is studied. This process features long minimum run lengths, and sequence dependent changeovers of the order of days, with high transition costs. The long-term scheduling involves extended time horizons that lead to large scale mixed integer linear programming (MILP) scheduling models. In order to address the difficulties posed by the size of the models, three different rolling horizon algorithms based on different models and time aggregation techniques are studied. The models used are based on the continuous time slot MILP model, and on the traveling salesman model proposed by Erdirik-Dogan and Grossmann (2008). Due to the particular characteristics of the process under study, several new features are proposed, which include: a) carry-over changeovers across the due dates; b) minimum run lengths across the due dates; c) a rigorous aggregation of the products based on the type of changeovers; d) definition of minimum inventory levels at the end of the time horizon. Several case studies are formulated in order to compare different scenarios, and assess the proposed rolling horizon algorithms.