Variable lot-streaming scheduling in multi-stage hybrid flowshop problem – A case study for solar cell industry  

Oleh:

Huang, Lu-Wei ; Chen, Tzuli ; Hung, Tzu-Hao ; Cheng, Chen-Yang

Jenis: Article from Proceeding
Dalam koleksi: The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS), 3-6 December 2013 Cebu, Philippines, page 1-13.
Topik: Hybrid flow shop scheduling; genetic algorithm; solar cell industry; lot streaming
Fulltext: 3010.pdf (774.86KB)

Isi artikel

This research focuses on a multi-stage and parallel-machines scheduling problem considering variable lot streaming which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be determined in advance. In addi-tion, the size of each sublot is split by parallel machines configuration. However, allocation of machine resources, sublot size and lot sequence are highly mutual influence. Without splitting a given job into sublots, the production scheduling cannot have overlapping of successive operations in multi-stage parallel machines environment thereby contributing to the best production scheduling. Therefore, this research motivated to explore these issues. The multi-stage and parallel-machines scheduling problem with variable lot streaming simultaneously considers the optimal sublot size, sublot sequence, parallel machines sublot scheduling and machine configurations through dynamically allocating all sublots to parallel machines in each stage. We formulate this problem as a mixed integer linear programming (MILP) model considering the practical characteristics including parallel machines, sequence-independent setup time, and sequence-dependent setup time in the solar cell industry. A mixed-coded genetic algorithm (MCGA) is developed to find a near-optimal solution. At the end of this study, the result of this research will compare with the optimization method of mixed integer linear programming and case study.

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