Minimizing the Total Weighted Completion Time on a Single Machine with Release Dates and Workforce having different skill levels

The aim of the proposed paper is to investigate the scheduling of a single machine managed by workforce having different skill levels and learning ability. The model considers the scheduling of weighted jobs on a single machine with nonzero release times. The processing time of the jobs increases during production due to the deterioration of the machine and the level of maintenance. The workforce ability influences the set-up time and the removal time of each scheduled job. The objective of the scheduling is to minimize the total weighted completion time. The operators are not a critical resource for the part scheduling. Conversely, the machine set-up and the unloading of each job from the machine are performed by an operator selected out from the currently available crew of workers with different skill levels and learning ability. The aim of this paper is to propose a mathematical model allowing efficient solutions to be found for the scheduling of jobs, which includes the cost of each configuration of workers skill, learning ability and maintenance level. The cost of each configuration and the total weighted completion time are conflicting objectives: in this paper a tool to perform decision making is developed by implementing Pareto analysis.