Comparing models for lot-sizing and scheduling of single-stage continuous processes: Operations research and process systems engineering approaches

• Published in: Computers & chemical engineering 2013-05, Vol.52, p.177-192
• Main Authors: Amorim, P., Pinto-Varela, T., Almada-Lobo, B., Barbósa-Póvoa, A.P.F.D.
• Format: Article
• Language: English
• Subjects: Applied sciences; Biological and medical sciences; Distribution theory; Exact sciences and technology; Food engineering; Food industries; Fundamental and applied biological sciences. Psychology; General aspects; Inventory control, production control. Distribution; Mathematical programming; Mathematics; Operational research and scientific management; Operational research. Management science; Probability and statistics; Production planning and control; Sciences and techniques of general use; Statistics
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2013.01.006

► We compare models from the OR and PSE communities for lot-sizing and scheduling. ► We consider a very practical setting with complex setup structures. ► Despite apparent similarities, models have a distinct computational performance. ► A new formulation is obtained by using concepts developed in both communities. ► Evidence of the advantages of bringing together these communities is obtained. In the last years, several researchers from two different academic communities, the Operations Research and the Process Systems Engineering, have been developing mathematical formulations for the lot-sizing and scheduling of single-stage continuous processes with complex setup structures. This problem has been intensively studied due to its importance to a wide range of industries where a single-stage approach is suitable for production planning. This is the case of the glass container, beer, and dairy production. Recent works have been performed by both mentioned communities, however, no intense communication between these research efforts has been observed. This work attempts a systematic analysis on recent formulation developments of both communities. Based on the result of this comparison, a reformulation is proposed that outperforms in the majority of the cases the previous existent formulations for a set of systematically generated random instances.