Chemical production scheduling problems are computationally challenging, due to the presence of a large number of binary decisions, and at the same time are quite diverse as they arise in very different production environments. Accordingly, to address the computational challenge, the group has been developing models for different types of problems (Lee and Maravelias, 2017; Velez and Maravelias, 2013b; Sundaramoorthy and Maravelias, 2011; Velez and Maravelias, 2013a), as well as problems with different processing features, such as time-varying utility price and availability (Velez and Maravelias, 2015), sequence-dependent changeovers (Velez et al., 2017), and periodic production (Wu and Maravelias, 2020). The methods developed in the group allow us to address real-world problems (Figure 1). The result of these methods are detailed schedules, often represented as Gantt charts (Figure 2).

Heineken Facility

Figure 1. Schematic of industrial production environment


Gantt Chart

Figure 2. Gantt chart of four-week schedule for industrial facility consisting of four stages: brewing, fermentation, maturation, and packaging


Lee H, Maravelias CT. Mixed-integer programming models for simultaneous batching and scheduling in multipurpose batch plants. Computers and Chemical Engineering, 106, 621–644, 2017.

Sundaramoorthy A, Maravelias CT. A General Framework for Process Scheduling. 57(3), 695–710, 2011.

Velez S, Dong Y, Maravelias CT. Changeover formulations for discrete-time mixed-integer programming scheduling models. European Journal of Operational Research, 260(3), 949–963, 2017.

Velez S, Maravelias CT. Theoretical framework for formulating MIP scheduling models with multiple and non-uniform discrete-time grids. Computers and Chemical Engineering, 72, 233–254, 2015.

Velez S, Maravelias CT. Mixed-integer programming model and tightening methods for scheduling in general chemical production environments. Industrial and Engineering Chemistry Research, 52(9), 3407–3423, 2013a.

Velez S, Maravelias CT. Multiple and nonuniform time grids in discrete-time MIP models for chemical production scheduling. Computers and Chemical Engineering, 53, 70–85, 2013b.

Wu Y, Maravelias CT. A General Model for Periodic Chemical Production Scheduling. Industrial and Engineering Chemistry Research, 59(6), 2505–2515, 2020.