In recent years there has been a renewed interest in multi-product batch process plants. These are particularly useful for the manufacture of high-valueadded products such as pharmaceuticals and semiconductors. Optimum scheduling of a multiproduct batch process plant is essential to improve productivity and reduce costs. This is a difficult task, since such plants, being Discrete Event Dynamic Systems (DEDS), are complex , due to the combinatorial explosion phenomenon. They also have elements of uncertainty and unpredictability and manufacturers have increasingly ambitious projects. In this paper, it will be shown how such a plant can be modelled on a timed Petri net. In this way, the search space for the scheduling algorithm is reduced to the feasible search space (taking into account the production unit constraints, product flow requirements and processing times). A Branch and Bound algorithm is applied to the timed Petri net model in order to search for the optimum schedule of the plant operation. Three tests, performed at each stage of the search, further reduce the search space, resulting in an efficient, intelligent scheduling algorithm. This algorithm generates a set of transitions (events), the firing order of which results in the optimum operation of the plant. Also, an algorithm which simulates the plant operation is developed. The algorithms developed in this paper are valuable tools that can be used in the design stages of a multiproduct batch process plant or as part of an intelligent software package that will totally control the plant in real time. Two multi-product batch process plants are modelled on a Petri net, scheduled and simulated to illustrate the developed techniques.
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