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

Author(s):  
Dong Han ◽  
Wangming Li ◽  
Xinyu Li ◽  
Liang Gao ◽  
Yang Li

Abstract 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.

Author(s):  
Jingcao Cai ◽  
Deming Lei

AbstractDistributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention; however, DHFSP with uncertainty and energy-related element is seldom studied. In this paper, distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with fuzzy processing time is considered and a cooperated shuffled frog-leaping algorithm (CSFLA) is presented to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption simultaneously. Iterated greedy, variable neighborhood search and global search are designed using problem-related features; memeplex evaluation based on three quality indices is presented, an effective cooperation process between the best memeplex and the worst memeplex is developed according to evaluation results and performed by exchanging search times and search ability, and an adaptive population shuffling is adopted to improve search efficiency. Extensive experiments are conducted and the computational results validate that CSFLA has promising advantages on solving the considered DEHFSP.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223782-223796
Author(s):  
Xixing Li ◽  
Hongtao Tang ◽  
Zhipeng Yang ◽  
Rui Wu ◽  
Yabo Luo

2012 ◽  
Vol 252 ◽  
pp. 354-359
Author(s):  
Xin Min Zhang ◽  
Meng Yue Zhang

A main-branch hybrid Flow shop scheduling problem in production manufacturing system is studied. Under the premise of JIT, targeting of smallest cost, a Flow-Shop production line scheduling model is built in cycle time of buffer. Two stages Quantum Genetic Algorithm (QGA) is proposed. By the results of numerical example, the effective and advantageous of QGA was shown.


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