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This paper presents a novel randomized dynamic pricing mechanism for the online k-selection problem with diseconomies of scale (OSDoS), aimed at maximizing social welfare while accounting for increasing marginal production costs. The proposed mechanism, r-Dynamic, outperforms existing deterministic and static pricing approaches, providing a tighter lower bound on the competitive ratio in both small and large inventory settings. The study addresses unresolved questions in the field by deriving a new tight lo

Author
mashitianxia1
Language
EN