As the impact of faultlines is still without a consensus, to figure out how faultlines will hurt or promote the entrepreneurial performance can help the new generation of Chinese migrant workers to start their businesses successfully under the Rural Revitalization Strategy. This study addressed a fuzzy-set qualitative comparative analysis (fsQCA) based on 32 returning entrepreneurial teams from a complexity perspective. We firstly introduced three faultline categories for migrant workers and selected five of the faultlines with high factor loads in each category for further analysis. Then a scale was developed to measure the team performance. By conducting fsQCA, four types of faultline configurations were found: (1) background-experience actuation; (2) guidance-balance lacking; (3) role-cognition conflict; and (4) information-decision polarization. The “background-experience actuation” type will promote the entrepreneurial performance while the other types will hurt the performance. Theoretically, breaking through the limitations of traditional regressions in previous studies, fsQCA is used to explore the complex interactions and integrated effects among different categories of faultlines, demonstrates that the unstable impact is just a one-sided representation of the overall effect, and fills the general faultline theory with Chinese specific scenario and small-sized entrepreneurship. Practically, several implications are proposed to optimize the heterogeneity of the returning migrant workers’ entrepreneurial teams and increase their performances, such as constructing the “balance” and “guidance” mechanism, enriching the background diversity of the members and solving the information-decision faultlines into individual diversity, etc., which can also be utilized by migrant worker entrepreneurs in other developing areas in the world.
The mixed integer linear programming (MILP) has been widely applied in many fields such as supply chain management and robot control, while how to develop a more efficient algorithm to solve large-scale MILP is still in discussion. This study addresses a hybrid algorithm of the ant colony and Benders decomposition to improve the efficiency. We firstly introduce the design of our algorithm, in which the Benders algorithm decomposes the MILP into a master problem and a slack problem, the ant colony algorithm generates initial solutions for the master problem, and heuristic rules obtain feasible solutions for the slack problem. Then, the computational experiments are carried out to verify efficiency, with a benchmark test and some medium-large scale examples. Compared with other algorithms like CPLEX, GUROBI, and traditional ACA, our algorithm shows a better performance with a 0.3%–4.0% optimality gap, as well as a significant decrease of 54.3% and 33.6% on average in the CPU time and iterations, respectively. Our contribution is to provide a low-workload, time-saving, and high-accuracy hybrid algorithm to solve MILP problems with a large amount of variables, which can be widely used in more commercial solvers and promote the utilization of the artificial intelligence.
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