Modeling and solving method for multi task engineering project scheduling considering nonlinear time-varying constraints

In response to the widespread problems of resource dynamic fluctuations, uncertain task duration, and time-varying costs in practical engineering projects, the author proposes a multi task engineering project scheduling model that considers nonlinear time-varying constraints, and designs a hybrid intelligent algorithm (IMHA) that integrates genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) for solution. This model covers multi-objective function construction, resource/time/risk constraint modeling, and fuzzy uncertainty handling. Through 35 sets of test cases with different scales and industry backgrounds, the experimental results show that IMHA is significantly better than GA, PSO, and SA in terms of project completion time, resource utilization, and algorithm stability, with an average reduction of about 11.5% in project duration and a 9.6% improvement in resource utilization. In addition, in the large-scale scheduling problem of 200 tasks, the running time is controlled within 15.6 minutes, demonstrating excellent scheduling efficiency and robustness.