Integrated optimization of process planning and scheduling problems based on complex networks

Abstract

Industrial information integration can promote modern manufacturing enterprises to achieve efficient information processing and data integration in manufacturing systems, providing a good framework for solving process planning and workshop scheduling integration problems. To explore optimization issues in an intelligent manufacturing environment, find a better way to solve the integration of process planning and scheduling, and analyze basic features of manufacturing task-driven workshop scheduling network. The paper constructs a multi-process relational network model based on processes and machines in accordance with the complex network theory. Moreover, this paper proposes a multi-objective integrated model that minimizes maximum makespan, minimum machine load, and minimum machine energy consumption with the constraints of the workpiece, process, and machine. This paper uses the NSGA-II algorithm strategy to analyze the proposed model and issue. The feasibility of the model and algorithm proposed is demonstrated by solving various optimization objectives in actual workshop manufacturing tasks. Then, it analyses and evaluates the network structure characteristic index of the production and manufacturing scheduling process, which provides a decision basis for the task processing process design and scheduling planning in the intelligent manufacturing.