Process planning and scheduling is one of the key factors to influence manufacturing quality and efficiency. The Industrial Internet-of-Things is built up to enable intelligent sharing of distributed manufacturing equipment, and more processes and resources are available for a production order. Thus, compared to the traditional single workshop manufacturing environment, process planning and scheduling is extended to a more complex problem of finding reasonable quoted suppliers, selecting suitable processes they offered, sequencing the operations of each process, and assigning resources for these operations to minimize production cost and time. This paper re-models process planning and scheduling in the Industrial Internet-of-Things environment to minimize the overall production time, rental cost, and transportation cost of an order that includes several interconnected jobs. A concurrent local search method is proposed to address this problem. It includes two types of local search operators performing concurrently with an evolutionary operator. An adjustment strategy is also proposed to leverage the concurrent operators toward diverse Pareto-front. A case study on the production of three different parts of an automobile engine demonstrates that the proposed method is able to find high-quality solutions in such a distributed manufacturing environment than four typical multi-objective evolutionary algorithms to save 1.3% to 5.0% of production time, 5.6% to 11.8% rental cost, and 3.2% to 8.2% transportation cost in average.

工艺规划和调度是影响制造质量和效率的关键因素之一。构建工业物联网，实现分布式制造设备的智能共享，为生产订单提供更多的流程和资源。因此，与传统的单车间制造环境相比，流程规划和调度被扩展到更复杂的问题，即寻找合理报价的供应商，选择他们提供的合适流程，对每个流程的操作进行排序，并为这些操作分配资源以最小化生产成本和时间。本文对工业物联网环境中的流程规划和调度进行了重新建模，以最大限度地减少整体生产时间、租金成本、包括多个相互关联的工作的订单的运输成本。提出了一种并发的局部搜索方法来解决这个问题。它包括两种与进化算子同时执行的局部搜索算子。还提出了一种调整策略，以利用并发算子朝向多样化的帕累托前沿。对汽车发动机三个不同部件生产的案例研究表明，与四种典型的多目标进化算法相比，所提出的方法能够在这样的分布式制造环境中找到高质量的解决方案，从而节省 1.3% 到 5.0% 的生产。时间，平均 5.6% 到 11.8% 的租金成本和 3.2% 到 8.2% 的运输成本。它包括两种与进化算子同时执行的局部搜索算子。还提出了一种调整策略，以利用并发算子朝向多样化的帕累托前沿。对汽车发动机三个不同部件生产的案例研究表明，与四种典型的多目标进化算法相比，所提出的方法能够在这样的分布式制造环境中找到高质量的解决方案，从而节省 1.3% 到 5.0% 的生产。时间，平均 5.6% 到 11.8% 的租金成本和 3.2% 到 8.2% 的运输成本。它包括两种与进化算子同时执行的局部搜索算子。还提出了一种调整策略，以利用并发算子朝向多样化的帕累托前沿。对汽车发动机三个不同部件生产的案例研究表明，与四种典型的多目标进化算法相比，所提出的方法能够在这样的分布式制造环境中找到高质量的解决方案，从而节省 1.3% 到 5.0% 的生产。时间，平均 5.6% 到 11.8% 的租金成本和 3.2% 到 8.2% 的运输成本。