Abstract
Automated material handling systems (AMHSs) in semiconductor fabrication facilities (fabs) are inherently capital intensive because they are moving toward full automation. However, in addition to being capital intensive, full automation can come at the cost of compromised performance or instability when abnormal events occur. Vehicular congestion is one example of an abnormal event and is a recurring problem in fabs that reduces production efficiency. In this paper, motivated by a material handling system design problem when constructing a new semiconductor fabrication plant in practice, we present a model for optimizing the location of overhead hoist transport lifters, which have proven to be a suitable addition to AMHSs for resolving bottlenecks caused by heavy congestion. To do so, we study a capacitated facility location problem (CFLP) that incorporates real-life constraints and consider the interactions between lifters of differing types. We first propose a hybrid approach that combines a genetic algorithm with a depth-first search (DFS) based on memorization to approximate the optimum positions for the installation of the lifters. We then conduct a numerical experiment to compare the performance of our approach with optimal solutions in small- to medium-sized facilities and perform a sensitivity analysis for the important parameters involved. Finally, an experimental study based on real data from semiconductor fabs is conducted to demonstrate the applicability and usefulness of the proposed model.
| Original language | English |
|---|---|
| Pages (from-to) | 106-119 |
| Number of pages | 14 |
| Journal | Journal of Manufacturing Systems |
| Volume | 51 |
| DOIs | |
| Publication status | Published - 2019 Apr 1 |
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Keywords
- AMHS design
- Depth-first search
- Facility location problem
- Genetic algorithm
- Overhead hoist transport lifter
- Semiconductor manufacturing
- Vehicle lifter
ASJC Scopus subject areas
- Control and Systems Engineering
- Software
- Hardware and Architecture
- Industrial and Manufacturing Engineering
Cite this
Iterative two-stage hybrid algorithm for the vehicle lifter location problem in semiconductor manufacturing. / Lee, Sangmin; Kahng, Hyun Gu; Cheong, Tae Su; Kim, Seoung Bum.
In: Journal of Manufacturing Systems, Vol. 51, 01.04.2019, p. 106-119.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Iterative two-stage hybrid algorithm for the vehicle lifter location problem in semiconductor manufacturing
AU - Lee, Sangmin
AU - Kahng, Hyun Gu
AU - Cheong, Tae Su
AU - Kim, Seoung Bum
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Automated material handling systems (AMHSs) in semiconductor fabrication facilities (fabs) are inherently capital intensive because they are moving toward full automation. However, in addition to being capital intensive, full automation can come at the cost of compromised performance or instability when abnormal events occur. Vehicular congestion is one example of an abnormal event and is a recurring problem in fabs that reduces production efficiency. In this paper, motivated by a material handling system design problem when constructing a new semiconductor fabrication plant in practice, we present a model for optimizing the location of overhead hoist transport lifters, which have proven to be a suitable addition to AMHSs for resolving bottlenecks caused by heavy congestion. To do so, we study a capacitated facility location problem (CFLP) that incorporates real-life constraints and consider the interactions between lifters of differing types. We first propose a hybrid approach that combines a genetic algorithm with a depth-first search (DFS) based on memorization to approximate the optimum positions for the installation of the lifters. We then conduct a numerical experiment to compare the performance of our approach with optimal solutions in small- to medium-sized facilities and perform a sensitivity analysis for the important parameters involved. Finally, an experimental study based on real data from semiconductor fabs is conducted to demonstrate the applicability and usefulness of the proposed model.
AB - Automated material handling systems (AMHSs) in semiconductor fabrication facilities (fabs) are inherently capital intensive because they are moving toward full automation. However, in addition to being capital intensive, full automation can come at the cost of compromised performance or instability when abnormal events occur. Vehicular congestion is one example of an abnormal event and is a recurring problem in fabs that reduces production efficiency. In this paper, motivated by a material handling system design problem when constructing a new semiconductor fabrication plant in practice, we present a model for optimizing the location of overhead hoist transport lifters, which have proven to be a suitable addition to AMHSs for resolving bottlenecks caused by heavy congestion. To do so, we study a capacitated facility location problem (CFLP) that incorporates real-life constraints and consider the interactions between lifters of differing types. We first propose a hybrid approach that combines a genetic algorithm with a depth-first search (DFS) based on memorization to approximate the optimum positions for the installation of the lifters. We then conduct a numerical experiment to compare the performance of our approach with optimal solutions in small- to medium-sized facilities and perform a sensitivity analysis for the important parameters involved. Finally, an experimental study based on real data from semiconductor fabs is conducted to demonstrate the applicability and usefulness of the proposed model.
KW - AMHS design
KW - Depth-first search
KW - Facility location problem
KW - Genetic algorithm
KW - Overhead hoist transport lifter
KW - Semiconductor manufacturing
KW - Vehicle lifter
UR - http://www.scopus.com/inward/record.url?scp=85065048163&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065048163&partnerID=8YFLogxK
U2 - 10.1016/j.jmsy.2019.02.003
DO - 10.1016/j.jmsy.2019.02.003
M3 - Article
AN - SCOPUS:85065048163
VL - 51
SP - 106
EP - 119
JO - Journal of Manufacturing Systems
JF - Journal of Manufacturing Systems
SN - 0278-6125
ER -