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The orienteering problem is a route optimization problem which consists infinding a simple cycle that maximizes the total collected profit subject to amaximum distance limitation. In the last few decades, the occurrence of thisproblem in real-life applications has boosted the development of many heuristicalgorithms to solve it. However, during the same period, not much research hasbeen devoted to the field of exact algorithms for the orienteering problem. Theaim of this work is to develop an exact method which is able to obtainoptimality certification in a wider set of instances than with previousmethods, or to improve the lower and upper bounds in its disability.  We propose a revisited version of the branch-and-cut algorithm for theorienteering problem which includes new contributions in the separationalgorithms of inequalities stemming from the cycle problem, in the separationloop, in the variables pricing, and in the calculation of the lower and upperbounds of the problem. Our proposal is compared to three state-of-the-artalgorithms on 258 benchmark instances with up to 7397 nodes. The computationalexperiments show the relevance of the designed components where 18 new optima,76 new best-known solutions and 85 new upper-bound values were obtained.

A revisited branch-and-cut algorithm for large-scale orienteering problems