Based on Amazon's successful business experience, the flywheel effect has proven to be an effective method for guiding companies across the S-curve. More scholars have investigated the flywheel model of business growth, which uses the flywheel effect to help companies achieve leapfrogging growth. More research is needed to determine whether the structured business growth model is universally applicable to different industries and stages of enterprise development. According to this study, in the VUCA era, businesses are forced to accelerate their transformation due to rapid changes in the competitive environment. A more agile approach to growth model optimization is required there. As a result, this study takes a traditional theory approach, and this research builds a flywheel model of enterprise growth on the original flywheel effect theory. The three-step method of producing the corporate growth flywheel model proposed in this study is validated by the empirical results of the case study, and the universality and operability of the structured business growth flywheel model are verified by the case study of the leading real estate intermediary company, Lianjia.

Investment extortion in the stock market is a crucial aspect considered by the investors. Therefore, investors implemented different strategies. This study was intended at constructing an investment portfolio (IP) of stocks within the NSE 100 listed companies of Non-parametric nature, fulfilling the basic premise of portfolio making that is, reducing risks while yielding an attractive return higher than any other instrument for the investors. Using DP omnibus test, the desired sample of companies following the non-normal distribution was achieved. Using financial beta, we have selected the outcome based on the nature of their ‘return’ and ‘risk'. We introduce TOPSIS (Technique for order of performance by similarity to ideal solution), a multi-criteria decision-making process (MCDM) to study the profitability of stocks, rank wise for each year, and finally, the Bayes portfolio model help to select the overall profitability associate with low risk for the construction of the portfolio.

In this paper, we formulate and solve an Imprecise Covering Ring Star Problem (ICRSP), which is a generalization of the Ring Star Problem (RSP). Here the objective of this problem is to find a subset of nodes in a network to minimize the sum of routing costs of interconnecting cycle and assignment costs of the nodes which are out of cycle, to their nearest concentrators such that no assigned node exceeds a predetermined distance (say, covering distance) from the concentrators. The covering distance, as well as the routing and assignments costs, are considered as fuzzy in the proposed ICRSP. A Modified Genetic Algorithm (MGA) is developed and used to solve this model for different confidence levels depending on the corresponding imprecise parameters, reducing it to deterministic form with fuzzy possibility and necessity approaches. Some comparisons with existing benchmark problems are made to justify the performance of the algorithm. As individual cases, some practical ICRSPs are also solved and presented numerically.