4,253 research outputs found
A methodology for demand learning with an application to the optimal pricing of seasonal products
Cover title.Includes bibliographical references (p. 31-32).by Gabriel R. Bitran, Hitendra K. Wadhwa
The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems
Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three âpillarsâ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a âboomerangâ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
This paper investigates the impact of pre-existing offline data on online
learning, in the context of dynamic pricing. We study a single-product dynamic
pricing problem over a selling horizon of periods. The demand in each
period is determined by the price of the product according to a linear demand
model with unknown parameters. We assume that before the start of the selling
horizon, the seller already has some pre-existing offline data. The offline
data set contains samples, each of which is an input-output pair consisting
of a historical price and an associated demand observation. The seller wants to
utilize both the pre-existing offline data and the sequential online data to
minimize the regret of the online learning process.
We characterize the joint effect of the size, location and dispersion of the
offline data on the optimal regret of the online learning process.
Specifically, the size, location and dispersion of the offline data are
measured by the number of historical samples , the distance between the
average historical price and the optimal price , and the standard
deviation of the historical prices , respectively. We show that the
optimal regret is , and design a learning algorithm based on the
"optimism in the face of uncertainty" principle, whose regret is optimal up to
a logarithmic factor. Our results reveal surprising transformations of the
optimal regret rate with respect to the size of the offline data, which we
refer to as phase transitions. In addition, our results demonstrate that the
location and dispersion of the offline data also have an intrinsic effect on
the optimal regret, and we quantify this effect via the inverse-square law.Comment: Forthcoming in Management Scienc
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