Name
Abstract | ||
|---|---|---|
It is shown in the paper that Developmental Genetic Programming is an efficient tool for evolutionary development of intelligent supervisors that solve an extension of Resource-Constrained Project Scheduling Problem. The extension assumes that resources are only partially available. It also assumes that renewable resources affect the project cost. The cost should be as low as possible and a deadline of the project must be met. This is apparent with regard to software houses and building enterprises. Computational experiments showed that supervisors find solutions of the problem much faster than other genetic approaches. A specific property of the supervisor is that it has various strategies of allocating the resources to the tasks. The supervisor uses the strategies in order to develop a procedure for producing the best schedule for the whole project. The analysis of the evolutionary process was performed and experimental results were compared with the optimal ones obtained by means of the exhaustive search method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10489-016-0791-1 | Appl. Intell. |
Keywords | Field | DocType |
Project scheduling,Resource allocation,Global optimization,Evolutionary computations,Developmental genetic programming | Supervisor,Project scheduling problem,Schedule (project management),Brute-force search,Global optimization,Computer science,Genetic programming,Resource allocation,Software,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
45 | 3 | 0924-669X |
Citations | PageRank | References |
1 | 0.36 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Grzegorz Pawinski | 1 | 4 | 1.77 |
| Krzysztof Sapiecha | 2 | 47 | 14.96 |