Assembly line balancing (ALB) allocates tasks to workstations by satisfying the precedence constraints of tasks determined by technology, organizational issues, safety constraints, etc. Several objective functions are used when the optimal allocation of tasks is determined, like station time minimization, capacity maximization, cost reduction or the optimization of some agronomical or physiological indicator.
In our earlier research, simple assembly line balancing models were completed with constraints related to worker's skills. These results can be extended for cases when robots work at the assembly line. When a robot is used as an independent workstation, then it can be considered as a worker with special skills. If however a robot works in collaboration with a worker at a workstation, then the task assignment problem must be completed with scheduling problems. The objective of the research group is the development of such models for robot and worker collaboration, which complete traditional simple assembly line balancing models with conditions pertaining to the joint application of robots and workers.
Another important direction of our ALB model development is the consideration of learning effect when tasks are assigned to workstations. In the early stages of the operation of an assembly line, the task time of workers may decrease as a consequence of learning. The consideration of learning effect may decrease the required number of workstations and may lead to significant cost savings.
The model development is based on mathematical programming and on constraint programming. The applied computational platform is the AIMMS mathematical programming system.
