document: As mentioned earlier, this paper represents ongoing efforts to efficiently address the stochastic MPSP. Future work may consider investigating whether the algorithm would be as successful or not in solving variants of the MPSP that include more operational constraints, such as variable cut-off grade, grade blending, and stockpiling, as it is in solving the “classical” variant considered in this paper. Indeed, it is a general-purpose algorithm and should be applicable to any of these variants. Other research avenues include considering other strategies for updating the penalties within PH and other methods for solving the sub-problems. Finally, another important research direction is the development of other efficient solution approaches. Since it has been observed empirically that the problem formulation often achieves small integrality gaps, one approach could be to solve the linear relaxation of the problem using an efficient algorithm and then to use an LP-rounding procedure to get an integer solution.
doc_bio_tags: O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O B I O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: grade blending mpsp operational constraints ph solve the linear relaxation stockpiling
abstractive_keyphrases: general-purpose algorithm lp-rounding procedure stochastic mpsp variable cut-off grade