Hybrid Local Search Methods in Solving Resource Constrained Project Scheduling Problem

Abstract

Now-a-days different meta-heuristic approaches, their variants and hybrids are being applied for solving Combinatorial Optimization Problems (COP). In this paper Resource Constrained Project Scheduling Problem (RCPSP) has been presented as a COP. This is a common problem for many construction projects. It is highly constrained and is categorized as a NP-hard problem. In our earlier work Simulated Annealing (SA_RCP) outperformed other meta-heuristics, like, Genetic Algorithm, Tabu Search, Particle Swarm Optimization and its variant in solving benchmark instances of this problem. Having been inspired by this result we have further developed new hybrids of Simulated Annealing and Tabu Search. In this work, we have proposed five more methods developed by combining Simulated Annealing and Tabu Search and applied them for solving a benchmark instance of this problem. The results show that Simulated Annealing incorporated with Tabu List, Greedy Selection Heuristic and aspiration criteria (GTSA_AC_RCP) outperforms other methods in getting optimal results with maximum hit and minimum fluctuations.