Data-driven discrete manufacturing resource collaborative optimization method and system

• Main Authors: SONG QINGRU, YAN PING, PEI JUN, WANG XINGMING, LU SHAOJUN, FAN WENJUAN, ZHOU ZHIPING, LIU XINBAO, KONG MIN, WEI JINLING, QIAN XIAOFEI, HONG MINGXIA
• Format: Patent
• Language: Chinese; English
• Published: 22.01.2019
• Subjects: CALCULATING; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention provides a data-driven discrete manufacturing resource collaborative optimization method and system. The method includes the following steps: analyzing the historical production data bymulti-factor analysis of variance, extracting the core factors that affect the manufacturing efficiency, and then calculating the net income of all the workpieces according to the core factors, that is fitness function, then, referring to the mechanism that the optimal solution destroys and reconstructs the existing solution, the variable neighborhood search algorithm is introduced to optimize thegreedy reference iterative algorithm. The invention can improve the robustness and diversity of solutions, can effectively improve the production efficiency of the aluminum ingot forging molding process, and can be extended to the complex product discrete manufacturing process to reduce the production cost. 本发明提供了种数据驱动的离散制造资源协同优化方法及系统。该方法包括：通过多因素方差分析方法对历史生产数据进行分析，提取出影响生产制造效率的核心影响因素，然后根据核心影响因素计算所有工件的净收益，即适应度函数。之后，参考最优解对现有解