Tactical capacity planning for semiconductor manufacturing: MILP models and scalable distributed parallel algorithms

A multiperiod stochastic mixed‐integer linear programming model is developed to address the tactical capacity planning of semiconductor manufacturing with considerations of complex routing of material flows, in‐process inventory, demand and capacity variability, multisite production, capacity utiliz...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:AIChE journal Jg. 62; H. 11; S. 3930 - 3946
Hauptverfasser: Zhou, Rui-Jie, Li, Li-Juan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Blackwell Publishing Ltd 01.11.2016
Schlagworte:
Algorithms
capacity planning
Decisions
distributed parallel optimization
Inventories
Mathematical models
Optimization
Risk management
scalable optimization
semiconductor manufacturing
Semiconductors
Stockpiling
two-stage stochastic integer optimization
ISSN:0001-1541, 1547-5905
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A multiperiod stochastic mixed‐integer linear programming model is developed to address the tactical capacity planning of semiconductor manufacturing with considerations of complex routing of material flows, in‐process inventory, demand and capacity variability, multisite production, capacity utilization rate, and downside risk management. Both planning level decisions (i.e., capacity allocation and customer service level decisions) as well as operational level decisions (i.e., production, inventory, and shipment decisions) can be simultaneously determined based on the two proposed multiobjective optimization models. To address the huge number of scenarios needed to characterize the uncertainty and the large number of first‐stage integer variables in industrial scale applications, two novel scalable distributed parallel optimization algorithms are developed to mitigate the computational burden. The proposed mathematical models and algorithms are illustrated through two case studies from a major US semiconductor manufacturer. Results from these case studies provide key decision support for capacity expansion in semiconductor industry. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3930–3946, 2016
Bibliographie:istex:75E5C934C5DF7EB88631321DCB4AF316A2BDF674
ark:/67375/WNG-1RKXRTNV-F
ArticleID:AIC15309
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.15309