Concurrent local search for process planning and scheduling in the industrial Internet-of-Things environment

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.