Resources for development projects are often scarce in the real world. Generally, many projects are to be completed that rely on a common pool of resources. Besides resource constraints, there exists data dependency among tasks within each project. A genetic algorithm approach with one-point uniform crossover and a refresh operator is proposed to minimize the overall duration or makespan of multiple projects in a resource constrained multi project scheduling problem (RCMPSP) without violating inter-project resource constraints or intra-project precedence constraints. The proposed GA incorporates stochastic feedback or rework of tasks. It has the capability of capturing the local optimum for each generation and therefore ensuring a global best solution. The proposed Genetic Algorithm, with several variants of GA parameters is tested on sample scheduling problems with and without stochastic feedback. This algorithm demonstrates to provide a quick convergence to a global optimal solution and detect the most likely makespan range for parallel projects of tasks with stochastic feedback.
- Design Engineering Division and Computers and Information in Engineering Division
Task Scheduling of Parallel Development Projects Using Genetic Algorithms
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Zhuang, M, & Yassine, AA. "Task Scheduling of Parallel Development Projects Using Genetic Algorithms." Proceedings of the ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 1: 30th Design Automation Conference. Salt Lake City, Utah, USA. September 28–October 2, 2004. pp. 215-224. ASME. https://doi.org/10.1115/DETC2004-57159
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