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We study a multiperiod network revenue management problem where a seller sells multiple products made from multiple resources with finite capacity in an environment where the underlying demand function is a priori unknown (in the nonparametric sense). The objective of the seller is to simultaneously learn the unknown demand function and dynamically price the products to minimize the expected revenue loss. For the problem where the number of selling periods and initial capacity are scaled by k > 0, it is known that the expected revenue loss of any non-anticipating pricing policy is [OMEGA]([square root of k]). However, there is a considerable gap between this theoretical lower bound and the performance bound of the best-known heuristic control in the literature. In this paper, we propose a nonparametric self-adjusting control and show that its expected revenue loss is O([k.sup.1/2+[member of]] log k) for any arbitrarily small [member of] > 0, provided that the underlying demand function is sufficiently smooth. This is the tightest bound of its kind for the problem setting that we consider in this paper, and it significantly improves the performance bound of existing heuristic controls in the literature. In addition, our intermediate results on the large deviation bounds for spline estimation and nonparametric stability analysis of constrained optimization are of independent interest and are potentially useful for other applications. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2018.0937. Keywords: revenue management * learning * self-adjusting control * spline approximation * asymptotic analysis, 1. Introduction Revenue management (RM), which was first implemented in the 1960s by legacy airline companies to maintain their edge in the competitive airline market, has recently become widespread in [...]

Abstract. We investigate when a buyer seeking to procure multiple units of an input may find it advantageous to run a 'test auction' in which she has incumbent suppliers bid [...]