Crowdsourcing Task Assignment Mechanism Based on Employer Net Profit and Employee Satisfaction

Juan Li, Yu Zhang
{"title":"Crowdsourcing Task Assignment Mechanism Based on Employer Net Profit and Employee Satisfaction","authors":"Juan Li, Yu Zhang","doi":"10.21078/JSSI-2021-440-15","DOIUrl":null,"url":null,"abstract":"Abstract Crowdsourcing task assignment has become an important task assignment model in the Internet economy era. In this paper, we study the crowdsourcing task assignment problem based on employer net profit and employee satisfaction. First, the reliability and interest of employees are modeled, based on which the mathematical expressions for employer net profit and employee satisfaction are given. Then, a multi-objective optimization problem is formulated to maximize employer net profit and employee satisfaction by jointly optimizing the task assignment matrix and task offer vector. Since the considered problem contains discrete variables, it cannot be solved directly by traditional optimization methods. Therefore, two low-complexity high-performance algorithms are proposed. The first algorithm is based on a fast non-dominated ranking genetic algorithm with an elite, which is able to explore the Pareto bound of the considered problem. The second algorithm is based on a reinforcement learning framework, which is able to maximize the weighted sum of employer net profit and employee satisfaction. Numerical results show that the number of tasks assigned to employees affects both employee satisfaction and employer net profit. The Pareto bounds and Pareto optimal solutions based on the solutions of the two proposed algorithms are also presented numerically, which quantitatively characterize the tradeoff between employer net profit and employee satisfaction.","PeriodicalId":258223,"journal":{"name":"Journal of Systems Science and Information","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems Science and Information","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21078/JSSI-2021-440-15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract Crowdsourcing task assignment has become an important task assignment model in the Internet economy era. In this paper, we study the crowdsourcing task assignment problem based on employer net profit and employee satisfaction. First, the reliability and interest of employees are modeled, based on which the mathematical expressions for employer net profit and employee satisfaction are given. Then, a multi-objective optimization problem is formulated to maximize employer net profit and employee satisfaction by jointly optimizing the task assignment matrix and task offer vector. Since the considered problem contains discrete variables, it cannot be solved directly by traditional optimization methods. Therefore, two low-complexity high-performance algorithms are proposed. The first algorithm is based on a fast non-dominated ranking genetic algorithm with an elite, which is able to explore the Pareto bound of the considered problem. The second algorithm is based on a reinforcement learning framework, which is able to maximize the weighted sum of employer net profit and employee satisfaction. Numerical results show that the number of tasks assigned to employees affects both employee satisfaction and employer net profit. The Pareto bounds and Pareto optimal solutions based on the solutions of the two proposed algorithms are also presented numerically, which quantitatively characterize the tradeoff between employer net profit and employee satisfaction.
基于雇主净利润和员工满意度的众包任务分配机制
众包任务分配已成为互联网经济时代一种重要的任务分配模式。本文研究了基于雇主净利润和员工满意度的众包任务分配问题。首先,建立了员工的可靠性和兴趣模型,在此基础上给出了雇主净利润和员工满意度的数学表达式。然后,通过对任务分配矩阵和任务提供向量的联合优化,构建了雇主净利润和员工满意度最大化的多目标优化问题。由于所考虑的问题包含离散变量,传统的优化方法无法直接求解。因此,提出了两种低复杂度的高性能算法。第一种算法是基于一种带有精英的快速非支配排序遗传算法,该算法能够探索所考虑问题的Pareto界。第二种算法基于强化学习框架,能够最大化雇主净利润和员工满意度的加权和。数值结果表明,分配给员工的任务数量对员工满意度和雇主净利润都有影响。基于这两种算法的解,给出了Pareto界和Pareto最优解,定量表征了雇主净利润和员工满意度之间的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信