Abstract
The selection of subcontractors is a significant aspect of any project since it can have a meaningful impact on the success of the project. In other words, choosing the wrong subcontractor can lead to delays and cost overruns. Thus, selecting the best subcontractors is crucial for every general contractor. In this study, imperialist competitive algorithm (ICA) is presented to solve the subcontractor selection problem (SSP) in multiple project environments by minimizing the general contractor's cost as the objective subject to resource and precedence constraints under two different circumstances. In the first circumstance, the project deadline can be postponed by paying a penalty; while the deadline cannot be deferred in the second one. The random key (RK) and the subcontractor list representation schemes are employed as encoding procedures, and the serial schedule generation scheme (SSGS) is utilized as a decoding scheme. Comparing the results of applying the presented ICA in a case study with the results obtained using an exact method and genetic algorithm (GA) validates the effectiveness of the proposed algorithm to solve SSP in multiple project environments. The outcomes demonstrate that the proposed ICA is more efficient in cases where the project deadline is restricted.
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Data availability
The datasets analyzed in this research are available in Afshar et al. (2022b).
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All authors contributed to the study’s conception and design. MRA performed the material preparation, data collection, and analysis. MZ wrote the manuscript, and all authors commented on the previous versions of the manuscript. All authors read and approved the final manuscript.
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Afshar, M.R., Zavari, M. Imperialist competitive algorithm for subcontractor selection in multiple project environments. Soft Comput 28, 2107–2124 (2024). https://doi.org/10.1007/s00500-023-09180-y
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DOI: https://doi.org/10.1007/s00500-023-09180-y