多联动可重入间歇生产调度的规则构建与仿真优化

    Rule Construction and Simulation-optimization for Reentrant Batch Process Scheduling with Multi-device Joint Action

    • 摘要: 在多联动可重入多目的间歇生产过程中,堵塞、抢占和死锁极易发生,造成协同生产难度大、多品种换型成本高,单纯采用智能优化算法将在编解码层面遭遇极大困难,且无法给出细节真实可用的调度方案。为此,以最小化最大完工时间为目标,提出融合启发式规则与高保真仿真的智能仿真优化框架。其中,基于生产逻辑,建立集成推拉结合、重相拼罐、重入优先三规则的生产系统仿真运行框架,保证高效连续生产;基于遗传规划,挖掘不同订单组合下的设备分配规则,保障优质解码;设计嵌入混合初始化和全可达变异的遗传算法,优化批量分配和投产排序。针对28个品种1000组订单的实例实验表明,与基于工业仿真平台的高保真仿真优化方法相比,所建立的优化框架在稳定性、有效性和计算效率上表现优异。

       

      Abstract: During the reentrant multi-purpose batch process with multi-device joint action, blockage, preemption and deadlock occur frequently, which significantly complicates collaborative production and increases the cost of changing production types. The direct application of intelligent optimization algorithms faces substantial challenges at both the encoding and decoding stages and fails to provide detailed and actionable scheduling plans. Therefore, an intelligent optimization framework integrating heuristic rules and high-fidelity simulation is proposed with the objective of minimizing the maximum completion time (makespan). Specifically, a production system simulation operation framework is established based on production logic, incorporating three rules—push-pull combination, heavy phase blending, and re-entrant priority—to ensure efficient and continuous production. Furthermore, equipment allocation rules under various order combinations are explored using genetic programming to guarantee high-quality decoding. Last, a genetic algorithm embedded with hybrid initialization and full-reachability mutation strategies is designed to optimize batch allocation and production sequencing. Experimental results from 1000 sets of orders across 28 varieties demonstrate that the proposed optimization framework surpasses high-fidelity simulation optimization methods based on industrial simulation platforms in terms of stability, effectiveness, and computational efficiency.

       

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