A classification and new benchmark instances for the multi-skilled resource-constrained project scheduling problem
Snauwaert, Jakob Vanhoucke, Mario
Snauwaert, Jakob
Vanhoucke, Mario
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Publication Type
Journal article with impact factor
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Supervisor
Publication Year
2023
Journal
European Journal of Operational Research
Book
Publication Volume
307
Publication Issue
1
Publication Begin page
1
Publication End page
19
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Abstract
This paper studies and analyses the multi-skilled resource-constrained project scheduling problem (MSRCPSP). We present a new classification scheme based on an existing classification scheme for project scheduling problems. This allows researchers to classify all multi-skilled project scheduling problems and its extensions. Furthermore, we propose a new data generation procedure for the MSRCPSP and introduce multiple artificial datasets for varying research purposes. The new datasets are generated based on new multi-skilled resource parameters and are compared to existing benchmark datasets in the literature. A set of 7 empirical multi-skilled project instances from software and railway construction companies are collected in order to validate the quality of the artificial datasets. Solutions are obtained through a genetic algorithm and by solving a mixed-integer linear programming formulation with CPLEX 12.6. The hardness of the multi-skilled project instances is investigated in the computational experiments. An experimental analysis studies the impact of skill availability, workforce size and multi-skilling on the makespan of the project.
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Keywords
Project Scheduling, Resource-Constrained Scheduling, Skills