Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance.

The evaluation of entrepreneurs’ uncertainty tolerance (UT) is more meaningful, e.g., predicting their behaviors, making psychological counseling strategies for them, etc. In fact, it is an uncertain problem that simultaneously contains randomness and fuzziness in evaluating entrepreneurs’ UT. Hence, it is difficult to solve it by traditional evaluation methods. This paper presents a cloud model-based comprehensive evaluation method for entrepreneurs’ UT which overcomes the inability of other methods to take into account randomness and fuzziness simultaneously. First, five UT levels are divided. Then, an evaluation index system which contains 14 secondary level indexes and four primary level indexes is constructed, and an analytic hierarchy process (AHP) method is used to obtain the weights for the secondary level indexes. Subsequently, the backward cloud generator (BCG) and virtual cloud model (CM) are used to obtain the CM of evaluation indexes, and cloud chart of evaluation results are generated by forward cloud generator (FCG). Finally, five core start-up founders of a high-tech company in China are chosen for a case study to illustrate our method. Compared with other traditional evaluation methods, our method has been verified to be a more competitive method. Its results are more visualized and low information lost.