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K-Most Demanding Products Discovery with Maximum Expected Customers

Published on May 2016 by Sofiya S. Mujawar, Dhanashree Kulkarni
National Conference on Advancements in Computer & Information Technology
Foundation of Computer Science USA
NCACIT2016 - Number 6
May 2016
Authors: Sofiya S. Mujawar, Dhanashree Kulkarni
3b465efc-0cc6-47f6-8cc2-3614ad0b57e9

Sofiya S. Mujawar, Dhanashree Kulkarni . K-Most Demanding Products Discovery with Maximum Expected Customers. National Conference on Advancements in Computer & Information Technology. NCACIT2016, 6 (May 2016), 1-4.

@article{
author = { Sofiya S. Mujawar, Dhanashree Kulkarni },
title = { K-Most Demanding Products Discovery with Maximum Expected Customers },
journal = { National Conference on Advancements in Computer & Information Technology },
issue_date = { May 2016 },
volume = { NCACIT2016 },
number = { 6 },
month = { May },
year = { 2016 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ncacit2016/number6/24730-3083/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancements in Computer & Information Technology
%A Sofiya S. Mujawar
%A Dhanashree Kulkarni
%T K-Most Demanding Products Discovery with Maximum Expected Customers
%J National Conference on Advancements in Computer & Information Technology
%@ 0975-8887
%V NCACIT2016
%N 6
%P 1-4
%D 2016
%I International Journal of Computer Applications
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.

References
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Index Terms

Computer Science
Information Sciences

Keywords

K-mdp Decision Support Production Plan Product Discovery.