A classification and new benchmark instances for the multi-skilled resource-constrained project scheduling problem

This paper studies and analyses the multi-skilled resource-constrained project scheduling problem (MSRCPSP). We present a new classification scheme based on an existing for problems. allows researchers to classify all problems its extensions. Furthermore, we propose data generation procedure MSRCPSP introduce multiple artificial datasets varying research purposes. The are generated resource parameters compared benchmark in literature. A set of 7 empirical instances from software railway construction companies collected order validate quality datasets. Solutions obtained through genetic algorithm by solving mixed-integer linear programming formulation with CPLEX 12.6. hardness is investigated computational experiments. An experimental analysis impact skill availability, workforce size multi-skilling makespan project.