Research on incentive strategy based on service quality in spatial crowdsourcing task allocation

Article type: Research Article

Authors: Peng, Peng^{a; b; c} | Ni, Zhiwei^{a; c; *} | Wu, Zhangjun^{a; c} | Zhu, Xuhui^{a; c} | Xia, Pingfan^{a; c}

Affiliations: [a] School of Management, Hefei University of Technology, Hefei, China | [b] North Minzu University, Yinchuan, China | [c] Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, China

Correspondence: [*] Corresponding author. Zhiwei Ni, E-mail: [email protected].

Abstract: In order to further improve the enthusiasm of spatial crowdsourcing workers, considering the service quality of workers, different incentive strategies are proposed and tasks are assigned. Firstly, the incentive model is constructed from the unit time revenue of task and online idle time, and the evaluation function of the evaluation model is constructed; Secondly, the task allocation is transformed into a combinatorial optimization problem by delay matching, and an improved glowworm swarm algorithm is proposed to solve the problem by discrete coding, introducing six kinds of mobile modes, adaptive probability matching and infeasible solution processing; Finally, the algorithm is used to solve the task allocation. The experimental results show that compared with the travel cost minimization strategy and random allocation strategy, the positive incentive index of the proposed strategy is improved by 11.79% and 14.60% respectively, and the fair incentive index is improved by 0.83% and 0.22% respectively, which can effectively improve the positive incentive range and incentive fairness of workers.

Keywords: Spatial crowdsourcing, service quality, task assignment, glowworm swarm algorithm

DOI: 10.3233/JIFS-212531

Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5551-5566, 2022