Authors:
José António Oliveira
;
Luís Dias
and
Guilherme Pereira
Affiliation:
University of Minho, Portugal
Keyword(s):
Optimization, Project Management, Scheduling, RCPSP, Metaheuristics, Genetic Algorithm, Random Keys.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Network Optimization
;
Operational Research
;
Optimization
;
Pattern Recognition
;
Project Management
;
Scheduling
;
Software Engineering
;
Symbolic Systems
Abstract:
The Resource Constrained Project Scheduling Problem (RCPSP) is NP-hard thus justifying the use meta-heuristics for its solution. This paper presents an evolutionary algorithm developed for the RCPSP problem. This evolutionary algorithm uses an alphabet based on random keys that makes easier its implementation while solving combinatorial optimization problems. Random keys allow the use of conventional genetic operators, what makes easier the adaptation of the evolutionary algorithm to new problems. To improve the method's performance, this evolutionary algorithm uses an initial population that is generated considering the information available for the instance. This paper studies the impact of using that information in the initial population. The computational experiments presented compare two types of initial population - the conventional one (generated randomly) and this new approach that considers the information of the instance.