An approximate dynamic programming approach to project scheduling with uncertain resource availabilities

We study the stochastic resource-constrained project scheduling problem with uncertain resource availability, called SRCPSP-URA, and model it as a sequential decision problem. A new Markov decision process (MDP) model is developed for the SRCPSP-URA. It dynamically and adaptively determines not only...

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Bibliographic Details
Published in:Applied Mathematical Modelling Vol. 97; p. 226
Main Authors: Xie, Fang, Li, Haitao, Xu, Zhe
Format: Journal Article
Language:English
Published: New York Elsevier BV 01.09.2021
Subjects:
Algorithms
Computing time
Dynamic programming
Exact solutions
Markov processes
Project management
Resource scheduling
ISSN:1088-8691, 0307-904X
Online Access:Get full text
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Summary:We study the stochastic resource-constrained project scheduling problem with uncertain resource availability, called SRCPSP-URA, and model it as a sequential decision problem. A new Markov decision process (MDP) model is developed for the SRCPSP-URA. It dynamically and adaptively determines not only which activity to start at a stage, but also which to interrupt and delay when there is not sufficient resource capacity. To tackle the curse-of-dimensionality of an exact solution approach, we devise and implement a rollout-based approximate dynamic programming (ADP) algorithm with priority-rule heuristic as the base policy, for which theoretical sequential improvement property is proved. Computational results show that with moderately more computational time, our ADP algorithm significantly outperforms the priority-rule heuristics for test instances up to 120 activities.
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ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2021.03.048