Affiliations: [a]
College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, China
| [b]
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
| [c]
College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing, China
Correspondence:
[*]
Corresponding author. Pingxiang Cao, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, 210037, China. E-mail: njfucpx@163.com.
Abstract: Distributed flexible flowshop scheduling is getting more important in the large-scale panel furniture industry. It is vital for a higher manufacturing efficiency and economic profit. The distributed scheduling problem with lot-streaming in a flexible flow shop environment is investigated in this work. Furthermore, the actual constraints of packaging collaborative and machine setup times are considered in the proposed approach. The average order waiting time for packaging and average order delay rate is used as objectives. Non-dominated sorting method is used to handle this bi-objective optimization problem. An improved encoding method was proposed to address the large-scale orders that need to be divided into sub-lots based on genetic algorithm. The proposed approach is firstly validated by benchmark with other multi-objectives evolutionary algorithms. The results found that the proposed approach had a good convergence and diversity. Besides, the influence of the proportion of large-scale orders priority level and sub-lot size was investigated in a panel furniture manufacturing scenario. The results can be concluded that the enterprise could obtain shorter order average waiting time and delay rate when the sub-lot sizes were set as two and the order priority level was allocated in the proportion of 1:2:3:4:5.
