Optimal Design of Crowdsourcing Project Pricing

DOI

10.2991/amms-17.2017.95How to use a DOI?

Keywords

component; crowdsourcing; multi-objectienve nonlinear programming; Entropy method; Genetic Algorithm; Logistic regression

Abstract

Aiming at the problem of the pricing and completion of crowdsourcing project,. we set up a project task pricing design model and solve this model by using genetic algorithm and logistic regression. Meanwhile, we use the differential equation to improve this model. The project task pricing design model is a multi-objective nonlinear programming model. We introduce the concept of company profit to limit the price and regard the profit as our target function. In addition, we consider the members' credit value, the distance between the members to the mission point, the number of members within the mission area and the possibility of members performing tasks to limit target function. In consideration of the possibility of the member performing the task, we used the logistic method to fit it. When considering the integrated reputation of members, we use the entropy weight method to determine the coefficients of each index. Finally, we get the target function values in different cases by using Genetic Algorithm. Meanwhile, we use the differential equation method to set up the price floating model to optimize our existing model. Through the model we established, we can ensure that the tasks issued by the company can be carried out with a higher completion rate in practice, and obtain better profit.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lizhi Yu
AU  - Dongkai Chen
AU  - Huangding Zhu
AU  - Zhe Xuan
AU  - Xusheng Kang
PY  - 2017/11
DA  - 2017/11
TI  - Optimal Design of Crowdsourcing Project Pricing
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
PB  - Atlantis Press
SP  - 439
EP  - 443
SN  - 1951-6851
UR  - https://doi.org/10.2991/amms-17.2017.95
DO  - 10.2991/amms-17.2017.95
ID  - Yu2017/11
ER  -