Solving resource constrained project scheduling problems with improved genetic algorithm

A novel meta-heuristic is developed for solving resource-constrained project scheduling problems (RCPSP). RCPSP deals with the activities of a project to be scheduled with the objective of the makespan minimization subject to both temporal and resource constraints. The proposed improved genetic algorithm (IGA) is based on the mechanics of natural selection and natural genetics. IGA is different from the traditional paradigm in its initialization and mutation mechanism. Initialization in IGA is conducted by using chaotic generator (Logistic, Tent, and Sinusoidal) instead of random generation. And mutation is performed by parallel mutation (PM) operator rather than point mutation. Parallel mutation consists of two mutation strategies viz. Gaussian and Cauchy. Gaussian strategy is utilized for small step mutation and Cauchy strategy for large step mutation. Patterson's test suites are carried out in order to demonstrate the efficacy of the proposed algorithm on RCPSP.