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  • 1
    Online Resource
    Online Resource
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2021
    In:  Management Science Vol. 67, No. 10 ( 2021-10), p. 6089-6115
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 67, No. 10 ( 2021-10), p. 6089-6115
    Abstract: We consider a periodic-review single-product inventory system with fixed cost under censored demand. Under full demand distributional information, it is well known that the celebrated (s, S) policy is optimal. In this paper, we assume the firm does not know the demand distribution a priori and makes adaptive inventory ordering decisions in each period based only on the past sales (a.k.a. censored demand). Our performance measure is regret, which is the cost difference between a feasible learning algorithm and the clairvoyant (full-information) benchmark. Compared with prior literature, the key difficulty of this problem lies in the loss of joint convexity of the objective function as a result of the presence of fixed cost. We develop the first learning algorithm, termed the [Formula: see text] policy, that combines the power of stochastic gradient descent, bandit controls, and simulation-based methods in a seamless and nontrivial fashion. We prove that the cumulative regret is [Formula: see text] , which is provably tight up to a logarithmic factor. We also develop several technical results that are of independent interest. We believe that the developed framework could be widely applied to learning other important stochastic systems with partial convexity in the objectives. This paper was accepted by Chung Piaw Teo, optimization.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    RVK:
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2021
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 2
    Online Resource
    Online Resource
    Elsevier BV ; 2020
    In:  Research in International Business and Finance Vol. 52 ( 2020-04), p. 101128-
    In: Research in International Business and Finance, Elsevier BV, Vol. 52 ( 2020-04), p. 101128-
    Type of Medium: Online Resource
    ISSN: 0275-5319
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2165501-7
    detail.hit.zdb_id: 424514-3
    SSG: 3,2
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  • 3
    Online Resource
    Online Resource
    Informa UK Limited ; 2022
    In:  Journal of Environmental Planning and Management Vol. 65, No. 3 ( 2022-02-23), p. 514-535
    In: Journal of Environmental Planning and Management, Informa UK Limited, Vol. 65, No. 3 ( 2022-02-23), p. 514-535
    Type of Medium: Online Resource
    ISSN: 0964-0568 , 1360-0559
    RVK:
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2000921-5
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  • 4
    Online Resource
    Online Resource
    Wiley ; 2022
    In:  Production and Operations Management Vol. 31, No. 6 ( 2022-06), p. 2571-2587
    In: Production and Operations Management, Wiley, Vol. 31, No. 6 ( 2022-06), p. 2571-2587
    Abstract: Sellers are conventionally generous with their return policies for valuation‐uncertain products, such as experience products and new products. However, with the development of online review platforms, an increasing number of consumers are engaging in social learning by referring to others' reviews to reduce valuation uncertainty. In this study, we investigate how social learning interacts with sellers' return policies. There are three main conclusions. First, when sellers have a relatively higher expectation of product quality (or simply the product quality is high), social learning makes the sellers offering either no‐refund policies or partial‐refund policies better off in terms of the increased profit. It will cause the no‐refund sellers to choose higher prices and inventory, and the partial‐refund sellers to set lower prices and refund amounts. Second, under social learning, the partial‐refund policy tends to be more beneficial to sellers than both full‐refund and no‐refund policies; although, when the product quality is high, the no‐refund policy tends to bring more benefits to sellers than the full‐refund policy. Hence, sellers may finally switch to the partial‐refund policy. Third, for partial‐refund policies, more often than not, social learning increases social welfare when the product quality is high; specifically, in many cases, it increases not only the profit of the seller but also the welfare of consumers.
    Type of Medium: Online Resource
    ISSN: 1059-1478 , 1937-5956
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2151364-8
    detail.hit.zdb_id: 1108460-1
    SSG: 3,2
    Location Call Number Limitation Availability
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