Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling

Abstract

In this paper, we develop a hybrid genetic algorithm (hGA) with fuzzy logic controller (FLC) to solve the resource-constrained project scheduling problem (rcPSP) which is a well-known NP-hard problem. Our new approach is based on the design of genetic operators with FLC and the initialization with the serial method, which has been shown superior for large-scale rcPSP problems. For solving these rcPSP problems, we firstly demonstrate that our hGA with FLC (flc-hGA) yields better results than several heuristic procedures presented in the literature. Then we evaluate several genetic operators, which include compounded partially mapped crossover (PMX), position-based crossover (PBC), swap mutation (SM), and local search-based mutation (LSM) in order to construct the flc-hGA which have the better optimal makespan and several alternative schedules with optimal makespan.