Open pit mine production scheduling for long-term planning is a relevant and required task for any mining project or operation. Mining blocks must be scheduled for extraction over a set of years, and a destination must be assigned to each one of them. The goal is to maximize the Net Present Value of the project, subject to capacity and operational constraints. Traditionally, this task has been performed either with the guidance of nested pits produced by the Lerchs-Grossmann algorithm (LG), considering pre-defined block destinations, or by Direct Block Scheduling (DBS), in which individual blocks are selected (or not) for extraction and destinations are assigned at given periods of time. On the one hand, from the purely theoretical side, DBS methods should be superior to those based on LG, because they are designed to deal with more realistic considerations of the problem (like capacities, multiple products, etc.) while LG approaches are limited to slope constraints and a unique economic value as parameters. On the other hand, the practical one, LG-based methods have been at the advantage, because DBS methods require intensive computational power to be solved. Fortunately, in later years, the availability of new algorithms and technology has made DBS more competitive. New DBS algorithms based on Integer Programming and heuristics have arisen with reasonable processing times, and MineLib, a set of standard datasets for testing, has been published and made available for researchers and software developers. This paper presents two DBS algorithms and show, by means of MineLib, their competitiveness against the state-of-the-art algorithms commercially available. Furthermore, these algorithms are applied to a case study in order to see how the solutions obtained differ and improve on the traditional approach based on LG.