A hybrid heuristic algorithm for the resource-constrained project scheduling problem

TL;DR

This paper introduces a hybrid metaheuristic combining genetic algorithms and neighborhood search strategies to effectively solve the resource-constrained project scheduling problem, minimizing project duration.

Contribution

The study develops a novel hybrid algorithm integrating GA and neighborhood search with resource ranking heuristics, achieving improved solutions for RCPSP.

Findings

Achieved the best average deviations from the critical path lower bound.

Updated heuristic solutions for several PCPLIB instances.

Validated effectiveness through computational experiments.

Abstract

This study presents a hybrid metaheuristic for the resource-constrained project scheduling problem (RCPSP), which integrates a genetic algorithm (GA) and a neighborhood search strategy (NS). The RCPSP consists of a set of activities that follow precedence relationship and consume resources. The resources are renewable, and the amount of the resources is limited. The objective of RCPSP is to find a schedule of the activities to minimize the project makespan. The algorithm uses two crossovers in the GA and two neighborhoods in the NS, as well as a resource ranking heuristic. The computational results with instances from the PCPLIB library validate the effectiveness of the proposed algorithm. We have obtained some of the best average deviations of the solutions from the critical path lower bound. The best heuristic solutions have been updated for some instances from PCPLIB.