A novel approach of two-stage three-way co-opetition decision for crowdsourcing task allocation scheme

In the crowdsourcing task allocation scheme, there is an emerging and realistic co-opetition phenomenon. To availably solve the crowdsourcing task allocation problem with co-opetition, this paper designs a two-stage co-opetition model by constructing novel three-way decision (TWD), including a competition-optimization model and a negotiation-cooperation model. Unlike the other studies, the two-stage co-opetition model with TWD can not only protect the profits of the task candidates, but also optimize the overall benefits. Specifically speaking, in the competition-optimization model, we construct an optimization model based on the data envelopment analysis (DEA) method in advance, which maximizes the personal benefit. By integrating information system and the loss function matrix, we consider the linkage of evaluation information and risk information and then improve the original TWD to make an initial allocation. In the negotiation-cooperation model, considering that the relationship among the candidates may influence the task performance, the fuzzy measure is introduced to describe a broader partnership. Meanwhile, we also design two different schemes to coordinate and optimize the best task allocations based on the initial allocation. In order to choose the best scheme, the selection strategy between schemes is further investigated under the guidance of the utility and the loss. Finally, we give an example of a medical supply chain crowdsourcing problem to illustrate and verify our proposed approach. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a two-stage co-opetition model for solving crowdsourcing task allocation problems, protecting task candidates' profits and optimizing overall benefits. The model integrates data envelopment analysis and fuzzy measure, and designs two schemes for task allocation.