A Developmental Genetic Approach to the cost/time trade-off in Resource Constrained Project Scheduling
Grzegorz Pawiński, Krzysztof Sapiecha
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 171–179 (2014)
Abstract. In this paper, the use of Developmental Genetic Programming (DGP) for solving a new extension of the Resource- Constrained Project Scheduling Problem (RCPSP) is investigated. We consider a variant of the problem when resources are only partially available and a deadline is given but it is the cost of the project that should be minimized. RCPSP is a well-known NP-hard problem but in its original formulation it does not take into consideration initial resource workload and it minimises the makespan. Unlike other genetic approaches, where genotypes represent solutions, a genotype in DGP is a procedure that constructs a solution to the problem. Genotypes (the search space) and phenotypes (the solution space) are distinguished and a genotype-to-phenotype mapping (GPM) is used. Thus, genotypes are evolved without any restrictions and the whole search space is explored. The goal of the evolution is to find a procedure constructing the best solution of the problem for which the cost of the project is minimal. The paper presents genetic operators as well as GPM specified for the DGP. Experimental results showed that our approach gives significantly better results compared with methods presented in the literature.