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K-Most Demanding Products Discovery with Maximum Expected Customers

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IJCA Proceedings on National Conference on Advancements in Computer & Information Technology
© 2016 by IJCA Journal
NCACIT 2016 - Number 6
Year of Publication: 2016
Authors:
Sofiya S. Mujawar
Dhanashree Kulkarni

Sofiya S Mujawar and Dhanashree Kulkarni. Article: K-Most Demanding Products Discovery with Maximum Expected Customers. IJCA Proceedings on National Conference on Advancements in Computer & Information Technology NCACIT 2016(6):1-4, May 2016. Full text available. BibTeX

@article{key:article,
	author = {Sofiya S. Mujawar and Dhanashree Kulkarni},
	title = {Article: K-Most Demanding Products Discovery with Maximum Expected Customers},
	journal = {IJCA Proceedings on National Conference on Advancements in Computer & Information Technology},
	year = {2016},
	volume = {NCACIT 2016},
	number = {6},
	pages = {1-4},
	month = {May},
	note = {Full text available}
}

Abstract

Paper originates a retardant for production organizes as k-most demanding product (k-MDP). Specified a group of customers requiring a specific variety of product with multiple options, a group of current product of the class, a group of candidate product that company is able to supply, and a positive number k, it is helpful to the corporate to select k product from the candidate product such the projected variety of the whole customers for the k product is maximized. One greedy algorithmic rule is implemented to look inexact resolution for the difficulty conferred during this paper is NP-hard once the amount of standards explains or options is three or quite three. This paper dis. cover specific solution for this issue, Apriori-Based (APR) algorithmic rule and Boundary Pruning (UBP) algorithmic rule area unit projected. Boundary of expected figures of total customers is additionally enforced to look for optimum resolution of the matter. Additionally to it, for computing least demanding product, AN algorithmic rule is calculated to search the k-least demanding product. This may be even helpful for production plans generation.

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