Abstract This paper presents two well-known meta-heuristics which are Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) to solve the dynamic integrated process planning, scheduling and due date assignment problem (DIPPSDDA) in which jobs arrive to the shop floor randomly. In this study, it is aimed to find the best combination of dispatching rule, due date assignment rule and route of all job with the objective of minimizing earliness, tardiness and due-dates of each jobs. 8 different size shop floors for the comparison of the GA and ACO algorithms performances have been developed. As a result of the experimental study, it was concluded that ACO algorithm outperformed GA algorithm. In addition, it has been suggested that integrated approaches can provide more global manufacturing efficiency than individual approaches.

中文翻译：

---

使用蚁群优化的动态集成流程规划、调度和截止日期分配

摘要 本文提出了两种著名的元启发式算法，即遗传算法 (GA) 和蚁群优化算法 (ACO)，以解决作业到达车间的动态集成流程计划、调度和截止日期分配问题 (DIPPSDDA)。地板随机。本研究旨在寻找所有作业的调度规则、截止日期分配规则和路线的最佳组合，以最小化每个作业的提前、迟到和截止日期。已经开发了 8 个不同规模的车间，用于比较 GA 和 ACO 算法的性能。实验研究结果表明，ACO算法优于GA算法。此外，有人建议集成方法可以提供比单独方法更高的全球制造效率。