Abstract

We study online algorithms for bin packing and bin covering in two different probabilistic settings in which the item sizes are drawn randomly or the items are adversarial but arrive in random order. We prove several results on the expected performance of well-known online algorithms in these settings. In particular, we prove that the simple greedy algorithm Dual Next-Fit for bin covering performs in the random-order setting strictly better than in the worst case, proving a conjecture by Christ et al. (Theoret Comput Sci 556:71–84, 2014).

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