This paper proposes a new ant colony optimization (ACO) algorithm suitable for integrated process planning and scheduling (IPPS) that optimizes both process planning and scheduling simultaneously. The IPPS covered in this study, when compared to the conventional IPPS, is more flexible and complicated because sequence-dependent setups and tool-related capacity constraints are additionally considered. Traditional ACOs have limitations in improving the solution quality and computation time for IPPS. The high flexibility and complexity of IPPS requires a large size of repository for pheromone trails and it causes the long computation time for updating them, excessive evaporation of pheromones, and unbalancing between pheromones and desirability. In the proposed ACO, each ant agent improves their own incumbent solution or finds a new solution using the pheromone trails that is composed of the experience information of the colony. Therefore, the proposed ACO conducts individual and cooperative evolving at the same time. Furthermore, we propose a simplified updating rule for pheromone trails and standardization of the transition probability to increase efficiency of the algorithm. Experimental results show that the proposed ACO is superior to recently proposed meta-heuristics for benchmark problems of different sizes in terms of both solution quality and computation time.

中文翻译：

---

进化蚁群系统，用于考虑顺序依赖的建立时间的大型集成过程计划和调度问题

本文提出了一种适用于集成过程计划和调度（IPPS）的新蚁群优化（ACO）算法，该算法可同时优化过程计划和调度。与常规IPPS相比，本研究中涵盖的IPPS更加灵活和复杂，因为还考虑了与序列有关的设置和与工具相关的容量限制。传统的ACO在提高IPPS的解决方案质量和计算时间方面存在局限性。IPPS的高度灵活性和复杂性要求用于信息素路径的存储库很大，这会导致更新它们所需的计算时间较长，信息素的过度蒸发以及信息素与所需信息之间的不平衡。在拟议的ACO中，每个蚂蚁代理程序都会改进自己的现有解决方案，或者使用由殖民地的经验信息组成的信息素线索找到新的解决方案。因此，拟议的ACO同时进行个人和合作发展。此外，我们提出了一种简化的信息素更新规则和过渡概率的标准化规则，以提高算法的效率。实验结果表明，在解决方案质量和计算时间方面，针对不同大小的基准问题，提出的ACO优于最近提出的元启发式算法。我们提出了一种简化的信息素更新规则和过渡概率的标准化规则，以提高算法的效率。实验结果表明，在解决方案质量和计算时间方面，针对不同大小的基准问题，提出的ACO优于最近提出的元启发式算法。我们提出了一种简化的信息素更新规则和过渡概率的标准化规则，以提高算法的效率。实验结果表明，在解决方案质量和计算时间方面，针对不同大小的基准问题，提出的ACO优于最近提出的元启发式算法。