Resource allocation using metaheuristic search

TL;DR

This paper evaluates the effectiveness of simulated annealing, tabu search, and genetic algorithms in solving resource allocation and scheduling problems in software project management, finding genetic algorithms generally perform best.

Contribution

It provides a comparative analysis of three metaheuristic algorithms applied to resource allocation in software projects, highlighting the superior performance of genetic algorithms.

Findings

Genetic algorithms outperformed simulated annealing and tabu search.

All three metaheuristics effectively solved resource allocation problems.

Experimental results support the use of metaheuristics in software project scheduling.

Abstract

This research is focused on solving problems in the area of software project management using metaheuristic search algorithms and as such is research in the field of search based software engineering. The main aim of this research is to evaluate the performance of different metaheuristic search techniques in resource allocation and scheduling problems that would be typical of software development projects. This paper reports a set of experiments which evaluate the performance of three algorithms, namely simulated annealing, tabu search and genetic algorithms. The experimental results indicate that all of the metaheuristics search techniques can be used to solve problems in resource allocation and scheduling within a software project. Finally, a comparative analysis suggests that overall the genetic algorithm had performed better than simulated annealing and tabu search.