An Effective Two-Phase Genetic Algorithm for Solving the Resource Constrained Project Scheduling Problem (RCPSP)

TL;DR

This paper introduces a novel two-phase genetic algorithm for RCPSP that alternates between intensification and diversification to improve solution quality, demonstrating effectiveness on benchmark problems.

Contribution

The paper proposes a new two-phase GA approach that enhances solution search by alternating parent selection strategies, improving results on standard RCPSP benchmarks.

Findings

Effective in improving heuristic solutions

Outperforms some existing methods on benchmarks

Balances exploration and exploitation successfully

Abstract

This note presents a simple and effective variation of genetic algorithm (GA) for solving RCPSP, denoted as 2-Phase Genetic Algorithm (2PGA). The 2PGA implements GA parent selection in two phases: Phase-1 includes the best current solutions in the parent pool, and Phase-2 excludes the best current solutions from the parent pool. The 2PGA carries out the GA evolution by alternating the two phases iteratively. In exploring a solution space, the Phase-1 emphasizes intensification in current neighborhood, while the Phase-2 emphasizes diversification to escape local traps. The 2PGA was tested on the standard benchmark problems in PSPLIB, the results have shown that the algorithm is effective and has improved some of the best heuristic solutions.