Genetic local search for resource-constrained project scheduling under uncertainty

Global manufacturing can be viewed as a project-oriented environment, where an effective project baseline schedule can serve as a basis for planning external activities, such as material procurement, preventive maintenance and commitment to shipping dates to customers. However, real life project scheduling often encounters imprecise activity durations and resource-constraints. The fuzzy set theory provides natural modeling tools for dealing with imprecise activity durations. In this paper, based on the fuzzy set theory, a specific genetic local search (GLS) algorithm is designed to solve fuzzy resource-constrained project scheduling problems. A precedence feasible activity list is applied as a solution representation, and specially designed recombination operators and local search processes are used in our algorithm. The roulette wheel selection and the elite retaining model are incorporated to generate a new population for the next generation. A practical project schedule with different resource availability levels is used in computational experiments. Computational results show that the GLS algorithm is effective for solving this kind of problem.