Algorithm

Engineering

Tardiness

Crossover

Scheduling (production processes)

Operating system

Computer science

Mathematical optimization

Operations research

Analytic hierarchy process

Mathematics

Genetic algorithm

Industrial engineering

Artificial intelligence

Schedule

Machine learning

Job shop scheduling

Robotics and Computer-Integrated Manufacturing

With the intensification of globalization, the competition among various manufacturing enterprises has become increasingly fierce, enterprises are developing in the direction of the product diversification, zero inventory or low inventory, and scheduling in production management has become more complicated. In this paper, machine and workpiece were as objects to study the problem of workshop scheduling in intelligent manufacturing environment. The resource scheduling model of intelligent manufacturing workshop was established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. The Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm was improved, and the evaluation function was established based on ranking level and crowding degree, then the competition mechanism was introduced. Random mutation strategy and crossover method based on process and machine was adopted to generate a new generation of populations. The elitist retention strategy was improved, the variable proportion method was designed to determine the probability, and the optimal solution is determined by the Analytic Hierarchy Process (AHP). The benchmark cases and practical production and processing problems were tested to verify the superiority and effectiveness of the improved algorithm.

Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm