A Data-Driven Proactive Scheduling Approach for Hybrid Flow Shop Scheduling Problem

As we all know, the COVID-19 pandemic brought a great challenge to manufacturing industry, especially for some traditional and unstable manufacturing systems. It reminds us that intelligent manufacturing certainly will play a key role in the future. Dynamic shop scheduling is also an inevitable hot topic in intelligent manufacturing. However, traditional dynamic scheduling is a kind of passive scheduling mode which takes measures to adjust disturbed scheduling processes after the occurrence of dynamic events. It is difficult to ensure the stability of production because of lack of proactivity. To overcome these shortcomings, manufacturing big data and data technologies as the core driving force of intelligent manufacturing will be used to guide production. Thus, a data-driven proactive scheduling approach is proposed to deal with the dynamic events, especially for machine breakdown. In this paper, the overall procedure of the proposed approach is introduced. More specifically, we first use collected manufacturing data to predict the occurrence of machine breakdowns and provide reliable input for dynamic scheduling. Then a proactive scheduling model is constructed for the hybrid flow shop problem, and an intelligent optimization algorithm is used to solve the problem to realize proactive scheduling. Finally, we design comparative experiments with two traditional rescheduling strategies to verify the effectiveness and stability of the proposed approach.

Proactive Scheduling for Job-Shop Based on Abnormal Event Monitoring of Workpieces and Remaining Useful Life Prediction of Tools in Wisdom Manufacturing Workshop

The job-shop scheduling is an important approach to manufacturing enterprises to improve response speed, reduce cost, and improve service. Proactive scheduling for job-shop based on abnormal event monitoring of workpieces and remaining useful life prediction of tools is proposed with radio frequency identification (RFID) and wireless accelerometer in this paper. Firstly, the perception environment of machining job is constructed, the mathematical model of job-shop scheduling is built, the framework of proactive scheduling is put forward, and the hybrid rescheduling strategy based on real-time events and predicted events is adopted. Then, the multi-objective, double-encoding, double-evolving, and double-decoding genetic algorithm (MD3GA) is used to reschedule. Finally, an actual prototype platform to machine job is built to verify the proposed scheduling method. It is shown that the proposed method solves the integration problem of dynamic scheduling and proactive scheduling of processing workpieces, reduces the waste of redundant time for the scheduling, and avoids the adverse impact on abnormal disturbances.