An Integrated System for Production Scheduling in Steelmaking and Casting Plants

• Published in: IEEE transactions on automation science and engineering 2016-04, Vol.13 (2), p.1112-1128
• Main Authors: Fanti, Maria Pia, Rotunno, Giuliana, Stecco, Gabriella, Ukovich, Walter, Mininel, Stefano
• Format: Article
• Language: English
• Subjects: Casting; Industrial plants; Integer programming; Job shop scheduling; Optimization; Production scheduling; Schedules; simulation; Steel; Steel production; steelmaking and casting scheduling; Systems integration; Unified modeling language
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2015.2477362

This paper presents an integrated system (IS) devoted to solve the complex scheduling problems in steel-making and casting (SMC) plants. The presented IS is composed of four modules: data base, optimization, simulation modules, and the user interface. In particular, we develop the two main components of the IS: the optimization and simulation modules. The optimization module is based on a mixed-integer linear programming formulation with the objective of minimizing the makespan, i.e., the completion time of the last job. Moreover, a discrete-event simulation module is used to validate and assess the proposed schedules. By designing the IS for a real case study, we show how it can be applied off-line to schedule the daily operations of the SMC, as well as online in order to face unpredictable events such as failures and blocks of the machines. Note to Practitioners-This paper is motivated by the necessity of solving the complex scheduling problems in steel-making and casting (SMC) plants. To this aim, the paper presents an integrated system (IS) that is devoted to help decision makers in selecting the optimal schedules of the operations also in the cases in which unpredictable events may occur. The main modules of the presented IS are an optimization module, based on a mixed-integer linear programming model, and a simulation module, able to verify and validate the proposed schedules by what if analyses and stochastic input parameters. Because of the complexity of the problem, some approximations referring to the transportation system planning are considered. However, the results show the efficiency of the proposed scheduling approach for real industrial applications. Future research aims at investigating about more efficient mathematical programming models in order to solve larger problems involving the planning of one or more weeks and a more detailed description of the plant.