NDLI: Procurement auction using actor-critic type learning algorithm

Content Provider	IEEE Xplore Digital Library
Author	Raju, C.V.L. Narahari, Y. Shah, S.
Copyright Year	2003
Description	Author affiliation: Dept. of CSA, Indian Inst. of Sci., Bangalore, India (Raju, C.V.L.; Narahari, Y.)
Abstract	Procurement, the process of obtaining materials or services, is a critical process for any organization. While procuring a set of items from different suppliers who may sell only a subset (bundle) of a desired set of items, it will be required to select an optimal set of suppliers who can supply the desired set of items. This is the optimal vendor selection problem. Bundling in procurement has benefits such as demand aggregation, supplier aggregation, and lead-time reduction. The NP-hardness of the vendor selection problem motivates us to formulate a compatible linear programming problem by relaxing the integer constraints and imposing additional constraints. The newly formulated problem can be solved by a novel iterative algorithm proposed recently in the literature. In this paper, we show that the application of this iterative algorithm leads to an iterative procurement auction that improves the efficiency of the procurement process. By using reinforcement learning to orchestrate the iterations of the algorithm, we show impressive gains in computational efficiency of the algorithm.
Sponsorship	Syst., Man & Cybernetics Soc. IEEE
Starting Page	4588
Ending Page	4594
File Size	452459
Page Count	7
File Format	PDF
ISBN	0780379527
ISSN	1062922X
DOI	10.1109/ICSMC.2003.1245707
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Procurement Iterative algorithms Linear programming Learning Supply chains Companies Computational complexity Lead time reduction Stochastic processes Dynamic programming
Content Type	Text
Resource Type	Article

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