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Solving Resource-Constrained Project Scheduling Problem via Genetic Algorithm
The resource-constrained project scheduling problem (RCPSP) is an important and challenging problem in the field of construction management. This paper presents a genetic algorithm (GA) for the RCPSP. The proposed algorithm introduces several changes in the genetic algorithm paradigm, such as a new selection operator to select parents to recombine; a modified two-point crossover operator with a specific crossover order; and a linearly decreasing probability-based mutation operator. The proposed algorithm was tested using standard benchmark problems of size J30, J60, and J120 from Project Scheduling Problem Library (PSPLIB) and compared with 19 state-of-the-art metaheuristics in the literature. The computational results validate that the proposed algorithm is a competitive algorithm for solving the RCPSP.
Solving Resource-Constrained Project Scheduling Problem via Genetic Algorithm
The resource-constrained project scheduling problem (RCPSP) is an important and challenging problem in the field of construction management. This paper presents a genetic algorithm (GA) for the RCPSP. The proposed algorithm introduces several changes in the genetic algorithm paradigm, such as a new selection operator to select parents to recombine; a modified two-point crossover operator with a specific crossover order; and a linearly decreasing probability-based mutation operator. The proposed algorithm was tested using standard benchmark problems of size J30, J60, and J120 from Project Scheduling Problem Library (PSPLIB) and compared with 19 state-of-the-art metaheuristics in the literature. The computational results validate that the proposed algorithm is a competitive algorithm for solving the RCPSP.
Solving Resource-Constrained Project Scheduling Problem via Genetic Algorithm
Liu, Jia (Autor:in) / Liu, Yisheng (Autor:in) / Shi, Ying (Autor:in) / Li, Jian (Autor:in)
12.12.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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