Refinery Scheduling Optimization using Genetic Algorithms and Cooperative Coevolution

Oil refineries are one of the most important examples of multiproduct continuous plants, that is, a continuous processing system that generates a number of products simultaneously. A refinery processes various crude oil types and produces a wide range of products. It is a complex optimization problem, mainly due to the number of different tasks involved and different objective criteria. In addition, some of the tasks have precedence constraints that require other tasks to be scheduled first. In this paper the refinery scheduling problem is addressed using genetic algorithms and cooperative coevolution. A simple refinery, with commonly found types of equipments, tasks and constraints of a real refinery, was created. Three test scenarios were designed with different sizes, demands and constraints. In all of them, the results obtained were far better than the ones obtained through random search