Collaboration–Competition Estimation of Distribution Algorithm for Flexible Job Shop Co-Scheduling With Multiload AGVs
针对多载AGV的柔性作业车间协同调度问题，提出一种协作竞争分布估计算法，通过混合初始化、多概率模型和变邻域搜索，显著优于现有算法，并验证了增加AGV载重可有效缩短完工时间。
Flexible job shop co-scheduling problems (FJSCSPs) normally adopt single-load automated guided vehicles (AGVs) for transportation, possibly causing the waste of load capacity. To enhance the transportation efficiency, a multiload AGVs (MAGVs) that carry more than one job simultaneously within its load capacity come into use in flexible manufacturing systems (FMSs). In this scenario, transit throughput can achieve the obvious improvement without increasing the vehicle fleet size, having become more prevalent gradually. However, co-scheduling machines and MAGVs is seldom investigated, which is crucial for maximizing production efficiency due to the inherent interdependence between transporting and processing. Considering constraints on load capacity, task assignment, and transportation sequence, this co-scheduling problem is formulated by minimizing the makespan as an optimization objective. Subsequently, a collaboration–competition estimation of distribution algorithm (CCEDA) is put forward to solve the difficulties caused by the flexible sequence for pickup and delivery tasks of MAGVs. In particular, two problem-related heuristic rules for selecting AGVs and machines are designed, and then a hybrid initialization strategy is developed to produce high-quality initial individuals. To comprehensively describe the landscape of the problem, multiple probability models are established by learning the elite solutions, and then a collaboration–competition mechanism adaptively samples using different models to maintain the high-efficiency exploration. Furthermore, a local search based on variable neighborhood is introduced to enhance the exploitation in promising regions. The experimental results on 30 instances expose that the proposed algorithm outperforms the other state-of-the-art algorithms significantly. Also, the analysis on the impact of AGV load capacity on production confirms that its increase effectively reduces the makespan, thereby demonstrating the practical value of MAGVs.