阻塞作业车间调度问题的新型迭代构造方法
Novel Iterative Construction Methods for the Blocking Job Shop Scheduling Problem
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中文总结 AI 辅助
针对阻塞作业车间调度问题，提出三种基于波束搜索的迭代构造启发式算法，其中GPU加速变体G-PMS-BS实现44倍加速，并在多个标准基准上取得新的最佳结果。
中文摘要 AI 辅助
阻塞作业车间调度问题（BJSSP）出现在现代复杂的制造、生产、物流和服务中，其中连续操作之间不允许有中间存储。这给元启发式算法带来了重大挑战，因为在求解该问题时，可行解与探索解的比例较低。为了高效解决这一问题，我们提出了三种新的基于波束搜索的启发式算法：波束搜索迭代构造启发式算法（BS-ICH）、其CPU并行扩展并行多策略波束搜索（PMS-BS），以及GPU加速变体G-PMS-BS。BS-ICH通过迭代扩展部分解来构造可行调度，同时保持宽度为k的波束以保留多个高质量的部分调度。PMS-BS运行数百个具有机器偏置多样性的并行BS-ICH实例，以扩大搜索空间并逃离局部最优。G-PMS-BS利用两阶段内核架构将波束扩展卸载到大规模并行GPU硬件上，该架构将轻量级评分与目标状态重建分离，从而能够扩展到具有2,000个操作的实例。混合CPU+GPU模式进一步利用空闲主机核心进行并发探索，使用负载均衡策略最小化同步开销。G-PMS-BS相比CPU基线实现了44倍加速。在所有标准Lawrence和Taillard实例上的实验表明，G-PMS-BS为22个Lawrence基准和77个Taillard实例建立了新的最佳已知结果，在最大的100x20实例上，完工时间最多减少了13%。
英文摘要
The Blocking Job-Shop Scheduling Problem (BJSSP) arises in modern and complex manufacturing, production, logistics, and service where no intermediate storage is allowed between consecutive operations. This creates a significant challenge for meta-heuristics due to the low ratio of feasible to explored solutions when solving the problem. To address this problem efficiently, we propose three new beam-search-based heuristics: the Beam Search Iterative Construction Heuristic (BS-ICH), its CPU-parallel extension Parallel Multi-Strategy Beam Search (PMS-BS), and a GPU-accelerated variants G-PMS-BS. BS-ICH constructs feasible schedules by iteratively extending partial solutions, while maintaining a beam of width k to preserve multiple high-quality partial schedules. PMS-BS runs hundreds of parallel BS-ICH instances with machine-biased diversity to expand the search space and escape local optima. G-PMS-BS offloads the beam expansion onto massively parallel GPU hardware using a two-phase kernel architecture that separates lightweight scoring from targeted state reconstruction, enabling scaling to instances with 2,000 operations. A hybrid CPU+GPU mode further exploits idle host cores for concurrent exploration, using load-balancing strategy to minimize synchronization overhead. G-PMS-BS achieves a 44x speedup over the CPU baseline. Experiments on all standard Lawrence and Taillard instances demonstrate that G-PMS-BS establishes new best-known results for 22 Lawrence benchmarks and 77 Taillard instances, with makespan reductions of up to 13% on the largest 100x20 instances.
发表机构
- University of Sciences and Technology Houari Boumediene (USTHB)（乌阿里·布梅迪恩科学与技术大学）
机构由 AI 辅助整理，请以论文原文为准。
