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    New summary measures and datasets for the multi-project scheduling problem

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    Publication type
    Vlerick strategic journal article
    Author
    Van Eynde, Rob
    Vanhoucke, Mario
    Publication Year
    2022
    Journal
    European Journal of Operational Research
    Publication Volume
    299
    Publication Issue
    3
    Publication Begin page
    853
    Publication End page
    868
    
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    Abstract
    In recent years, more researchers have devoted their attention to the resource-constrained multi-project scheduling problem, resulting in a growing body of knowledge on solution procedures. A key factor in the comparison of these procedures is the availability of benchmark datasets that cover a large part of the feature space. Otherwise, one risks that the conclusions from experiments on these sets do not hold when they are repeated on a different set. In this paper we propose new multi-project datasets that contain instances with a wide variety of characteristics. We first develop several new summary measures that describe three types of portfolio characteristics, two of the three types are not present in any of the existing datasets. Second, an algorithm is developed that can generate instances with the desired parameter values in a controlled manner. With this procedure, we create three datasets that each focus on one of the characteristics and a fourth dataset that contains all combinations. The computational results show (a) that these sets cover a significantly larger part of the feature space than existing benchmark libraries and (b) that they are more challenging for advanced algorithms.
    Keyword
    Project Scheduling, Multi-project Scheduling, Summary Measures, Data Generation, Benchmark Data
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.1016/j.ejor.2021.10.006
    URI
    http://hdl.handle.net/20.500.12127/7008
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ejor.2021.10.006
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