Production planning and scheduling are two core decision layers, constrained and affected by one another in manufacturing systems. Owing to different time scales and objectives, planning and scheduling are often separately handled in a sequential way, which frequently results in infeasible or suboptimal solutions. Moreover, uncertain issues, e.g. the fuzzy startup time of a machine and the fuzzy processing time for a task, are inherent to manufacturing systems due to mechanized and/or man-made factors. Motivated by these challenges, this paper aims to develop fuzzy bi-level decision-making techniques to handle integrated planning and scheduling problems in the fuzzy manufacturing system. First, the integrated problem is formulated into a fuzzy bi-level decision model in which solving the higher-level planning problem has to take into account lower-level implicit scheduling reactions in advance. Second, a hybrid solution method is developed to solve the resulting bi-level decision model, in which a particle swarm optimization (PSO) algorithm is applied to update planning decisions, and then, in view of each given planning decision, a heuristic algorithm is presented to find an optimal schedule under fuzzy manufacturing conditions. Lastly, a set of computational study is constructed to demonstrate the effectiveness of the proposed fuzzy bi-level decision-making techniques. Compared with existing works, they can find better planning decisions fulfilled by schedules and perform much better in terms of computational efficiency.

生产计划和调度是两个核心决策层，在制造系统中相互制约和影响。由于时间尺度和目标的不同，通常以顺序的方式分别处理计划和调度，这通常导致不可行或次优的解决方案。此外，由于机械化和/或人为因素，不确定性问题，例如机器的模糊启动时间和任务的模糊处理时间，是制造系统固有的。受这些挑战的驱使，本文旨在开发模糊的双层决策技术，以处理模糊制造系统中的集成计划和调度问题。第一，集成的问题被公式化为模糊的双层决策模型，在该模型中，解决高层计划问题必须事先考虑到较低层的隐式调度反应。其次，开发了一种混合解决方案方法来解决由此产生的双层决策模型，其中应用了粒子群算法（PSO）更新计划决策，然后针对每个给定的计划决策，采用启发式算法。提出以在模糊制造条件下找到最佳计划。最后，构建了一组计算研究来证明所提出的模糊双层决策技术的有效性。与现有工作相比，他们可以找到按计划执行的更好的计划决策，并且在计算效率方面要好得多。