Resource-constrained multi-project scheduling: Benchmark datasets and decoupled scheduling
Publication typeJournal article with impact factor
JournalJournal of Scheduling
Publication Begin page301
Publication End page325
MetadataShow full item record
AbstractIn this paper, we propose a new dataset for the resource-constrained multi-project scheduling problem and evaluate the performance of multi-project extensions of the single-project schedule generation schemes. This manuscript contributes to the existing research in three ways. First, we provide an overview of existing benchmark datasets and classify the multi-project literature based on the type of datasets that are used in these studies. Furthermore, we evaluate the existing summary measures that are used to classify instances and provide adaptations to the data generation procedure of Browning and Yassine (J Scheduling 13(2):143-161, 2010a). With this adapted generator we propose a new dataset that is complimentary to the existing ones. Second, we propose decoupled versions of the single-project scheduling schemes, building on insights from the existing literature. A computational experiment shows that the decoupled variants outperform the existing priority rule heuristics and that the best priority rules differ for the two objective functions under study. Furthermore, we analyse the effect of the different parameters on the performance of the heuristics. Third, we implement a genetic algorithm that incorporates specific multi-project operators and test it on all datasets. The experiment shows that the new datasets are challenging and provide opportunities for future research.
KeywordMulti-project Scheduling, Portfolio Scheduling, Summary Measures, Decoupled Scheduling, Benchmark Data
Knowledge Domain/IndustryOperations & Supply Chain Management
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