Group for Research in Decision Analysis


Combining heuristics and integer linear programming to compute a solution to the Robotic Process Automation (RPA) problem

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Robotic process automation (RPA) is used in various fields of human activity in order to implement faster and more secure processes through a reduction in the risks or errors but also an increase in the productivity rates. The increase of its use and importance calls for evermore efficient solution methods for this problem. In this paper, the RPA is addressed in the context of a financial institution. The problem consists in assigning transactions to software robots, whereas each type of transaction has a different clearance day and processing time. First, four types of heuristics are used to compute an upper bound on the number of required software robots. Then, this bound is given as a parameter to an integer linear program, which is used to assign the transactions to the different robots. The quality of the solutions are assessed by an extensive experimental study on a set of 39000 instances. The results show that two heuristics outperform the others and that the LP problem solved with a timeout of 60 seconds allows to find the optimal solution for most of the instances.

, 13 pages