In order to be competitive in today’s rapidly changing business world, enterprises have transformed a centralized to a decentralized structure in many areas of decision. It brings a critical problem that is how to schedule the production resources efficiently among these decentralized production centers. This paper studies a multi-objective distributed hybrid flow shop scheduling problem (MDHFSP) with the objectives of minimizing makespan, total weighted earliness and tardiness, and total workload. In the MDHFSP, a set of jobs have to be assigned to several factories, and each factory contains a hybrid flow shop scheduling problem with several parallel machines in each stage. A multi-objective evolutionary algorithm based on multiple neighborhoods local search (MOEA-LS) is proposed to solve the MDHFSP. In the initialization phase, a weighting mechanism is used to decide which position is the best one for each job when constructing a new sequence. Several multiple neighborhoods local search operators based on the three objectives are designed to generate offsprings. Some worse neighboring solutions are replaced by the solutions in the achieve set with a simulated annealing probability. In order to avoid trapping into local optimum, an adaptive weight updating mechanism is utilized when the achieve set has no change. The comprehensive comparison with other classic multi-objective optimization algorithms shows the proposed algorithm is very efficient for the MDHFSP.

为了在当今瞬息万变的商业世界中保持竞争力，企业在许多决策领域已将集中式结构转变为分散式结构。它带来了一个关键问题，即如何在这些分散的生产中心之间有效地调度生产资源。本文研究了一个多目标分布式混合流水车间调度问题 (MDHFSP)，其目标是最小化完工时间、总加权提前和延迟以及总工作量。在 MDHFSP 中，一组作业必须分配给几个工厂，每个工厂都包含一个混合流水车间调度问题，每个阶段都有几台并行机器。提出了一种基于多邻域局部搜索的多目标进化算法(MOEA-LS)来解决MDHFSP。在初始化阶段，在构建新序列时，使用加权机制来决定哪个职位最适合每个工作。基于三个目标的几个多邻域本地搜索算子被设计来生成后代。一些较差的相邻解决方案被具有模拟退火概率的实现集中的解决方案所取代。为了避免陷入局部最优，当实现集没有变化时，采用自适应权重更新机制。与其他经典多目标优化算法的综合比较表明，该算法对MDHFSP非常有效。基于三个目标的几个多邻域本地搜索算子被设计来生成后代。一些较差的相邻解决方案被具有模拟退火概率的实现集中的解决方案所取代。为了避免陷入局部最优，当实现集没有变化时，采用自适应权重更新机制。与其他经典多目标优化算法的综合比较表明，该算法对MDHFSP非常有效。基于三个目标的几个多邻域本地搜索算子被设计来生成后代。一些较差的相邻解决方案被具有模拟退火概率的实现集中的解决方案所取代。为了避免陷入局部最优，当实现集没有变化时，采用自适应权重更新机制。与其他经典多目标优化算法的综合比较表明，该算法对MDHFSP非常有效。