Production scheduling plays an important role in the intelligent decision support system and intelligent optimization decision technology. In the context of the globalization trend, the current production and management may extend from a single factory to a distributed production network. In this paper, we study the distributed blocking flowshop scheduling problem (DBFSP) that is an important generalization of the traditional blocking flowshop scheduling problem in the distributed environment. Six constructive heuristics and an iterated greedy (IG) algorithm are proposed to minimize the makespan, which provides procedures for obtaining efficient and effective solutions to make decision-making sounder. The first five heuristics are developed based on the well-known NEH2 heuristic [B. Naderi, R. Ruiz, The distributed permutation flowshop scheduling problem, Computers & Operations Research, 37 (4) (2010) 754–768.] and the last heuristic is presented by extending the PW heuristic [Q.K. Pan, L. Wang, Effective heuristics for the blocking flowshop scheduling problem with makespan minimization, Omega, 40 (2) (2012) 218–229.] to DBFSP in an effective way. The composite heuristics that combining constructive heuristics and local searches are also studied. The proposed composite heuristics are chosen to generate an initial solution with a high level of quality. Keeping the simplicity of the IG algorithm, three local search procedures, two destruction procedures, an improved reconstruction procedure, and a simulated annealing-like acceptance criterion are well designed based on the problem-specific knowledge to enhance the IG algorithm. The computational experiments are carried out based on the 720 benchmark instances from the literature. The results show that the proposed heuristics are very effective for solving the problem under consideration and the presented IG algorithm performs significantly better than the other state-of-the-art metaheuristics from the literature.

生产调度在智能决策支持系统和智能优化决策技术中起着重要作用。在全球化趋势的背景下，当前的生产和管理可能会从单个工厂扩展到分布式生产网络。在本文中，我们研究了分布式阻塞流水车间调度问题（DBFSP），它是对分布式环境中传统阻塞流水车间调度问题的重要概括。提出了六个建设性启发式算法和迭代贪婪（IG）算法以最小化有效期，从而提供了获得有效且有效的解决方案以使决策更合理的过程。前五种启发式算法是基于著名的NEH2启发式算法开发的[B. Naderi，R.Ruiz，分布式置换流水车间调度问题，计算机与运筹学，37（4）（2010）754–768。]和最后的启发式方法是通过扩展PW启发式方法[QK Pan，L. Wang，阻止流式调度的有效启发式方法最小化制造跨度的问题，Omega，40（2）（2012）218–229。]有效地提交给DBFSP。还研究了将构造性启发式方法和本地搜索相结合的组合启发式方法。选择拟议的组合试探法以生成具有高水平质量的初始解决方案。在保持IG算法简单性的基础上，基于特定问题的知识，精心设计了三个局部搜索过程，两个破坏过程，一个改进的重建过程以及一个模拟的类似退火的接受准则，以增强IG算法。计算实验是基于文献中的720个基准实例进行的。结果表明，所提出的启发式方法对于解决所考虑的问题非常有效，并且所提出的IG算法的性能明显优于文献中其他最新的元启发式方法。