NDLI: Learning in infinite-horizon inventory competition with total demand observations

Content Provider	IEEE Xplore Digital Library
Author	Zeinalzadeh, A. Alptekinoglu, A. Arslan, G.
Copyright Year	2012
Description	Author affiliation: Edwin L. Cox Sch. of Bus., Southern Methodist Univ., Dallas, TX, USA (Alptekinoglu, A.) \|\| Dept. of Electr. Eng., Univ. of Hawaii at Manoa, Honolulu, HI, USA (Zeinalzadeh, A.; Arslan, G.)
Abstract	We consider single-period and infinite-horizon inventory competition between two firms that replenish their inventories as in the well-known newsvendor model. Normally customers have a preference for shopping in one firm or the other. A fixed percentage of them who encounter a stockout in the firm of their first choice, though, visits the other firm. This substitution behavior makes the firm's replenishment decisions strategically related. Our main contribution is to introduce a simple learning algorithm to inventory competition. The learning algorithm requires each firm (a) to have the knowledge of its own critical fractile, which the firm can calculate using the values of its own per unit revenue, order cost, and holding cost; and (b) to observe its own total demand realizations. They do not necessarily know their true demand distributions. The firms need not even have any information about each other, beyond the implicit information encoded in their own total demand realizations affected by their competitors' inventory decisions. In fact, the firms need not even be aware that they are engaged in inventory competition. We prove that the inventory decisions generated by the learning algorithm converge, with probability one, to certain threshold values that constitute an equilibrium in pure Markov strategies for an infinite-horizon discounted-reward inventory competition game.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	1382
Ending Page	1387
File Size	193039
Page Count	6
File Format	PDF
ISBN	9781457710957
ISSN	07431619
e-ISBN	9781457710964
e-ISBN	9781467321020
DOI	10.1109/ACC.2012.6315678
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Games Convergence Markov processes USA Councils Educational institutions Vectors Learning systems
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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