MULTI PROJECT SCHEDULING IN THE CHEMICAL INDUSTRY USING A GENETIC ALGORITHM

Due to impatient customers and competitive threats, it has become increasingly important for the chemical industry to shorten the lead time of development projects. Furthermore, these organizations are faced with the challenge of planning and managing the simultaneous execution of multiple dependent projects under tight time and resource constraints. Within that kind of business environment, effective project management and task scheduling is crucial to organizational performance. A genetic algorithm approach with a novel genotype and GP mapping operation is presented to minimize the overall project duration and budget of multiple projects for a resource constrained multi project scheduling problem (RCMPSP) without violating inter-project resource constraints or intra-project precedence constraints. The C3 representation was used to model the processes due to its compact, generic and easily quantifiable nature. The implemented system is capable of calculating a number of project performance metrics that are focused towards determining the effect of stochastic rework of tasks, variable assignment of actors and the stochastic makespan for a specific task. The proposed Genetic Algorithm is tested on scheduling problems with and without stochastic feedback. This GA demonstrates to provide a quick convergence to a global optimal solution regarding the multi-criteria objectives.