In this paper we address the long-term scheduling of a real world multi-product single stage continuous process for manufacturing glass. 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 developed. The models 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, including minimum run lengths and changeovers across due dates, are proposed. The performance and characteristics of the proposed rolling horizon algorithms are discussed for one industrial example. (c) 2010 Elsevier Ltd. All rights reserved.