(624g) An Advanced Scheduling Technique for Multipurpose Batch Plants | AIChE

(624g) An Advanced Scheduling Technique for Multipurpose Batch Plants


Seid, E. R. - Presenter, University of Pretoria
Majozi, T. - Presenter, University of Pretoria

1E. Reshid and 1,2T. Majozi

1Department of Chemical Engineering, University of Pretoria, Lynnwood Road, Pretoria, 0002, South Africa

2 Modelling and Digital Science, CSIR, Pretoria, South Africa



Several scheduling techniques exist in literature based on continuous-time representation. The models based on unit specific time points have shown better solution efficiency by reducing the number of time points and problem size. In this presentation novel scheduling techniques based on unit specific time point continuous time representation are presented. The proposed models allow nonsimultaneous material transfer into a unit like in the models of Susarla et al. (2010). This approach gives a better schedule as compared to most published models. The developed scheduling models are based on state sequence network representation that has proven to inherently result in smaller problems in terms of binary variables. The models require a smaller number of time points as compared to single-grid and multi-grid continuous time models. Consequently, they exhibit much better computational performance. Numerical evaluation using literature examples indicate in some of the complex examples, that the proposed models give a better objective value as compared to other scheduling models. From a case study, a new optimal objective value of 53.8 was obtained for makespan minimization, as compared to 56.4 obtained by models in the literature. This indicates that the proposed models are able to reduce the working hours of the plant considerably. An added feature of the proposed models are their ability to exactly handle fixed intermediate storage operational philosophy, which has proven to be a subtle drawback in most published scheduling techniques.

Key words: Scheduling; Multipurpose Batch Plant; Optimization; MILP; Continuous-time