Solving NPV-Based Resource Constrained Project Scheduling Problem Using Genetic Algorithm

This paper discusses the resource-constrained project scheduling problem with discounted cash flows. Building on a rich literature on this topic, this paper proposes a new set of genetic algorithm (GA) operators. All three crossover and mutation operators are constructed to minimize the process idle time. The variable insertion neighborhood search (VINS) is employed as a mutation operator. Moreover, the elitism and roulette strategies are utilized besides diversifying the population by random generation of additional chromosomes before applying the population update to control the required size. Taguchi Design of Experiment (DOE) is employed to determine the suitable parameter values of GA. The proposed GA is tested on well-known 17,280 standard benchmark projects ranging from 25 to 100 activities. The proposed GA implemented in Julia Language outperforms well-known state-of-art standalone meta-heuristic techniques which may outperform existing hybrid systems if our GA is hybridized.