A genetic algorithm for the robust resource leveling problem
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The resource leveling problem (RLP) involves the determination of a project baseline schedule that specifies the planned activity starting times while satisfying both the precedence constraints and the project deadline constraint under the objective of minimizing the variation in the resource utilization. However, uncertainty is inevitable during project execution. The baseline schedule generated by the deterministic RLP model tends to fail to achieve the desired objective when durations are uncertain. We study the robust resource leveling problem in which the activity durations are stochastic and the objective is to obtain a robust baseline schedule that minimizes the expected positive deviation of both resource utilizations and activity starting times. We present a genetic algorithm for the robust RLP. In order to demonstrate the effectiveness of our genetic algorithm, we conduct extensive computational experiments on a large number of randomly generated test instances and investigate the impact of different factors (the marginal cost of resource usage deviations, the marginal cost of activity starting time deviations, the activity duration variability, the due date, the order strength, the resource factor and the resource constrainedness).
KeywordsProject scheduling Robust resource leveling Stochastic activity durations Genetic algorithm
The authors thank the reviewers for providing valuable suggestions that have improved the quality of this paper. The research of Hongbo Li is supported by the Research Center for Operations Management of the KU Leuven, the National Natural Science Foundation of China under Grant No. 71271019, the China Postdoctoral Science Foundation under Grant No. 2015M571542, the Humanities and Social Sciences Foundation of the Ministry of Education of China under grant 15YJCZH077 and a scholarship from the China Scholarship Council.
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