Machine-Level Collaborative Manufacturing and Scheduling for Heterogeneous Plants

Current Industrial Internet supports the sharing of information on heterogeneous resources and elements in a process of industrial production. It enables intelligent production processes and supports cost-effective scheduling. However, collaborative manufacturing and scheduling planning for enterprises with multiple plants cause several major challenges because of a large number of decision variables and constraints of manufacturing abilities of plants, resources of production, etc. Existing methods cannot comprehensively optimize the cost of multiple products in different plants, and fail to consider machine-level optimization of tasks of manufacturing. We propose a comprehensive machine-level architecture for enterprises with multiple plants. Based on this architecture, we formulate a limited non-linear integer optimization problem to decrease the total cost of transportation, production, and sales. In it, several real-life complicated nonlinear constraints are jointly considered, and they include constraints of storage space, replacement times, pairing production, substitution, and order fulfillment rates. To solve this optimization problem, we design a hybrid meta-heuristic optimization algorithm named Genetic Simulated annealing-based Particle Swarm Optimizer with Auto-Encoders (GSPAE). Extensive experiments with real-life data show that GSPAE decreases the total cost by 25% than other state-of-the-art methods.