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Besides the "obvious" mariage of ML and optimization (namely, use ML to prepare the input for optimization), there can be combinations that make use of the respective strengths: ML is good for decisions that are repetitive, ill-structured, simple in their solution; opt is good for well-structured situations, complex in their solution. Thus, a combination can work well eg to make a base plan with optimization and react to disturbances with ML. A preprint that employs ML and optimization to deal with different information statii for tactical and strategicoperational decisions is by Larsen et al. on Predicting Tactical Solutions to Operational PlanningProblemsPlanning Problems under Imperfect Information.

Besides the "obvious" mariage of ML and optimization (namely, use ML to prepare the input for optimization), there can be combinations that make use of the respective strengths: ML is good for decisions that are repetitive, ill-structured, simple in their solution; opt is good for well-structured situations, complex in their solution. Thus, a combination can work well eg to make a base plan with optimization and react to disturbances with ML. A preprint that employs ML and optimization to deal with different information statii for tactical and strategic decisions is by Larsen et al. on Predicting Tactical Solutions to Operational PlanningProblems under Imperfect Information.

Besides the "obvious" mariage of ML and optimization (namely, use ML to prepare the input for optimization), there can be combinations that make use of the respective strengths: ML is good for decisions that are repetitive, ill-structured, simple in their solution; opt is good for well-structured situations, complex in their solution. Thus, a combination can work well eg to make a base plan with optimization and react to disturbances with ML. A preprint that employs ML and optimization to deal with different information statii for tactical and operational decisions is by Larsen et al. on Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information.

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source | link

Besides the "obvious" mariage of ML and optimization (namely, use ML to prepare the input for optimization), there can be combinations that make use of the respective strengths: ML is good for decisions that are repetitive, ill-structured, simple in their solution; opt is good for well-structured situations, complex in their solution. Thus, a combination can work well eg to make a base plan with optimization and react to disturbances with ML. A preprint that employs ML and optimization to deal with different information statii for tactical and strategic decisions is by Larsen et al. on Predicting Tactical Solutions to Operational PlanningProblems under Imperfect Information.