In real-life applications, resources in construction projects are always limited. It is of great practical importance to shorten the project duration by using intelligent models (i.e., evolutionary computations such as genetic algorithm (GA) and particle swarm optimization (PSO) to make the construction process reasonable considering the limited resources. However, in the general EC-based model, for example, PSO easily falls into a local optimum when solving the problem of limited resources and the shortest period in scheduling a large network. This paper proposes two PSO-based models, which are resource-constrained adaptive particle swarm optimization (RC-APSO) and an input-adaptive particle swarm optimization (iRC-APSO) to respectively solve the static and dynamic situations of resource-constraint problems. The RC-APSO uses adaptive heuristic particle swarm optimization (AHPSO) to solve the limited resource and shortest duration problem based on the analysis of the constraints of process resources, time limits, and logic. The iRC-APSO method is a combination of AHPSO and network scheduling and is used to solve the proposed dynamic resource minimum duration problem model. From the experimental results, the probability of obtaining the shortest duration of the RC-APSO is higher than that of the genetic PSO and GA models, and the accuracy and stability of the algorithm are significantly improved compared with the other two algorithms, providing a new method for solving the resource-constrained shortest duration problem. In addition, the computational results show that iRC-APSO can obtain the shortest time constraint and the design scheme after each delay, which is more valuable than the static problem for practical project planning.

在实际应用中，建设项目中的资源总是有限的。在资源有限的情况下，利用智能模型（即遗传算法（GA）和粒子群优化（PSO）等进化计算，使施工过程合理化，从而缩短工程工期具有重要的现实意义。以EC模型为例，PSO在解决大型网络调度中资源有限、周期最短等问题时容易陷入局部最优，本文提出了两种基于PSO的模型，即资源受限的自适应粒子群优化（RC-APSO）和输入自适应粒子群优化（iRC-APSO）分别解决资源约束问题的静态和动态情况。RC-APSO在分析过程资源、时间限制和逻辑约束的基础上，采用自适应启发式粒子群优化（AHPSO）解决资源有限和最短持续时间问题。iRC-APSO 方法是 AHPSO 和网络调度的结合，用于解决所提出的动态资源最小持续时间问题模型。从实验结果来看，RC-APSO 获得最短持续时间的概率高于遗传 PSO 和 GA 模型，算法的准确性和稳定性较其他两种算法有显着提高，提供了一种新的解决资源受限最短持续时间问题的方法。此外，