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Our model permits the vendor to cancel at any time any reservation made earlier, by which case the holder of the reservation incurs a utility loss amounting to a fraction of her value for the reservation and may receive a cancellation payment from the seller.
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To forestall error accumulation brought on by modeling two subtasks independently, we propose to jointly mannequin both subtasks in an end-to-finish neural network. We propose and examine a simple mannequin for auctioning such ad slot reservations upfront. An extensive computational study show the efficacy of the proposed approach and gives insights in to the benefits of strategic time slot management.
We propose a 2-stage stochastic programming formulation for the design of a priori supply routes and time slot assignments and a pattern common approximation algorithm for its answer.

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