Authors:
Fulvio Antonio Cappadonna
;
Antonio Costa
and
Sergio Fichera
Affiliation:
University of Catania, Italy
Keyword(s):
Scheduling, Parallel Machines, Human Resources, Makespan, Genetic Algorithms.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolution Strategies
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
This paper addresses the unrelated parallel machine scheduling problem with limited and differently-skilled human resources. Firstly, the formulation of a Mixed Integer Linear Programming (MILP) model for solving the problem is provided. Then, three proper Genetic Algorithms (GAs) are presented, aiming to cope with larger sized issues. Numerical experiments put in evidence how all GAs proposed are able to approach the global optimum given by MILP model for small-sized instances. Moreover, a statistical comparison among proposed meta-heuristics algorithms is performed with reference to larger problems.