A hyper-heuristic refers to a search method or a learning mechanism for selecting or generating heuristics to solve computational search problems. Operating at a level of abstraction above that of a metaheuristic, it can be seen as an algorithm that tries to find an appropriate solution method at a given decision point rather than a solution. In this talk, a new hyper-heuristic that combines elements from reinforcement learning and tabu search is presented. It is applied to solve a complex real-world scheduling problem, namely the stochastic open-pit mine production scheduling problem with metal uncertainty (SOPMPSP). The performance of the new hyper-heuristic is assessed by comparing it to several solution methods from the literature: problem-specific algorithms tailored for the SOPMPSP and general hyper-heuristics, which use only limited problem-specific information.
Du café et des biscuits seront offerts au début du séminaire.
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