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This paper investigates the problem of how to achieve a positive cash flow balance by multimode multiproject scheduling, in which a contractor must implement multiple projects concurrently, and activities can be performed with one of several alternative modes. First, based on formulating cash flows for the projects, we construct an optimization model that can minimize the maximum gap between accumulative cash outflow and cash inflow, thus balancing cash flow positively by arranging optimal execution modes and start times for activities. Then, we prove the NP-hardness of the studied problem and design two metaheuristic algorithms, namely, tabu search (TS) and simulated annealing (SA), which search the desirable solutions in nested and mixed ways, respectively. Finally, taking the multistart iterative improvement (MSII) as comparison algorithm, the performance of the two algorithms developed is evaluated through a computational experiment performed on a data set generated randomly. From the research results, the following conclusions are drawn. The TS and SA are more suitable for solving the smaller and larger problems, respectively, while the nested searching structure could enhance the algorithm's efficiency. With increases in the advance payment proportion, the number of milestone activities, the client's payment proportion, or the project deadline, the contractor's maximal cash flow gap decreases.

This study establishes a model of prefabricated building project risk management system based on the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm and a prediction model of deep learning multilayer feedforward neural network (Backpropagation, BP neural network) to improve the requirements of risk management during the construction of large prefabricated building projects. First, we introduced the BP neural network algorithm based on deep learning. Second, the traditional Teaching-Learning-Based Optimization (TLBO) algorithm was modified by using information entropy, and the modified algorithm was simulated and tested in five test functions. Then, based on the BP neural network and MTLBO algorithm, we established the MTLBO-BP neural network prediction model and tested its performance. Finally, based on the MTLBO-BP neural network prediction model, MATLAB software was used to establish an intelligent model of the risk management system during the construction of prefabricated building projects, and the example verification was performed. In addition, the MTLBO algorithm was verified by test function simulation and established that global searchability is stronger than the TLBO algorithm. Of note, it is not easy to fall into a local optimum. The test results of the MTLBO-BP neural network prediction model revealed that the prediction model converges faster and exerts a better prediction effect. The example verification of the intelligent model of the risk management system during the construction of prefabricated building projects established in this study revealed that the algorithm proposed is more accurate in the reliability and cost prediction of the risk management of prefabricated building projects. Moreover, the algorithm proposed provides theoretical support for intelligent management and decision-making of prefabricated building projects. Overall, this study validates that this algorithm is essential for construction project management, decision-making, and quality assurance.