Scheduling and optimisation of batch plants: model development and comparison of approaches

Abstract

The application of parallel machines and storage facilities provides flexibility but raises challenges for batch plants. This research proposes a scheduling model in batch plants, considering complex real-world constraints that were seldom addressed together. Two optimisation approaches, genetic algorithm GA and constraint programming CP, are applied to solve the complex batch plant scheduling problem. A case study and scalability tests are conducted to investigate different performance of GA and CP in the problem to prepare for further research application. It is found that the CP approach has a better performance in solving batch plant scheduling problems with complex constraints although it needs longer time. The 'restart' search strategy is better than two other search strategies for the CP approach.