Metaheuristic Methods

Abstract

Given the $\mathcal{N}\!\mathcal{P}$-hard nature of the Resource Constrained Project Scheduling Problem (RCPSP), obtaining an optimal solution for larger instances of the problem becomes computationally intractable. Metaheuristic approaches are therefore commonly used to provide near-optimal solutions for larger instances of the problem. Over the past two decades, a number of different metaheuristic approaches have been proposed and developed for combinatorial optimization problems in general and for the RCPSP in particular. In this chapter, we review the various metaheuristic approaches such as genetic algorithms, simulated annealing, tabu search, scatter search, ant colonies, the bees algorithm, neural networks etc., that have been applied to the RCPSP. One metaheuristic approach called the NeuroGenetic approach is described in more detail. The NeuroGenetic approach is a hybrid of a neural-network based approach and the genetic algorithms approach. We summarize the best results in the literature for the various metaheuristic approaches on the standard benchmark problems J30, J60, J90, and J120 from PSPLIB (Kolisch and Sprecher, Eur J Oper Res 96:205–216, 1996).