Novel Iterative Construction Methods for the Blocking Job Shop Scheduling Problem

Abstract

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.