An improvement-based MILP optimization approach to complex AWS scheduling

被引：10

作者：

Aguirre, Adrian M. ^{[1
]}

Mendez, Carlos A. ^{[1
]}

Gutierrez, Gloria ^{[2
]}

De Prada, Cesar ^{[2
]}

机构：

[1] INTEC UNL CONICET, RA-3000 Santa Fe, Argentina

[2] Univ Valladolid, E-47011 Valladolid, Spain

来源：

COMPUTERS & CHEMICAL ENGINEERING | 2012年 / 47卷

关键词：

Hybrid decomposition approach; MILP-based strategies; Large-scale scheduling problems; Semiconductor manufacturing system (SMS); Wafer fabrication; Modeling and optimization; WET-ETCH STATION; BATCH PROCESSES; ALGORITHMS; STRATEGIES; PLANTS;

D O I：

10.1016/j.compchemeng.2012.06.036

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The automated wet-etch station (AWS) is one of the most critical stages of a modern semiconductor manufacturing system (SMS), which has to simultaneously deal with many complex constraints and limited resources. Due to its inherent complexity, industrial-sized automated wet-etch station scheduling problems are rarely solved through full rigorous mathematical formulations. Decomposition techniques based on heuristic, meta-heuristics and simulation-based methods have been traditionally reported in literature to provide feasible solutions with reasonable CPU times. This work introduces an improvement MILP-based decomposition strategy that combines the benefits of a rigorous continuous-time MILP (mixed integer linear programming) formulation with the flexibility of heuristic procedures. The schedule generated provides enhanced solutions over time to challenging real-world automated wet etch station scheduling problems with moderate computational cost. This methodology was able to provide more than a 7% of improvement in comparison with the best results reported in literature for the most complex problem instances analyzed. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：217 / 226

页数：10

共 17 条

[1] A novel optimization method to automated wet-etch station scheduling in semiconductor manufacturing systems [J].