Modeling and Solving the Stochastic Multi-mode Resource Leveling Problem
CHU Zihao
XU Zhe
LI Ming
GU Kun
Abstract：Multi-mode execution as well as uncertain task durations is common in project scheduling area. Reducing the fluctuations in the pattern of expensive renewable resource usage over time is an important goal project pursues. For the stochastic multi-mode resource leveling problem, the Markov decision process model is set up under the objective of minimizing the variation in the renewable resource utilization. An approximate dynamic programming algorithm based on the rollout policy is developed. Meanwhile, a revised genetic algorithm is employed as the base policy to enhance performance of the rollout algorithm. And the virtual upper bound of resource is proposed in the decision stage to assess candidate activities. The model and algorithm are demonstrated using an example project. The effect of related parameters on resource leveling is tested through full factorial design. Computational results show that the algorithm solves the stochastic multi-mode resource leveling problem effectively and the resource usage benefits from a simple network structure, ample resources, small variation coefficient and symmetric distribution of activity duration.
Keywords：stochastic schedulingresource-constrainedmulti-modeuncertain task durationsresource levelingapproximate dynamic programming
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 95-105 )