Paper Info
Title
Constraint-Based Genetic Algorithms for Machine Requirement of Semiconductor Assembly Industry: A Proposed Framework
Abstract
Having an accurate capacity planning is always an ultimate goal for semiconductor manufacturing. However, as capacity planning is highly affected by the demand forecast uncertainty, there is a necessity to make the gap closer to ensure profitability. The paper defines the problems faced by a semiconductor company in handling capacity planning and balancing the capital investment cost against the risk of losing revenue. Machine allocation in capacity planning is a process to determine mixture of a machine types that satisfy all precedence and resource constraints and minimize the total machines allocation. We adopt constraint-based genetic algorithm (GA) to solve this optimization problem with the focus on mapping the right chromosome representation to the domain problem. The method is chosen to allow the running of GA in original form as well as to ensure the computation is straight forward and simple.
Year
DOI
Venue
2009
10.1109/AMS.2009.119
Asia International Conference on Modelling and Simulation
Keywords
Field
DocType
proposed framework,capital investment cost,accurate capacity planning,capacity planning,machine type,constraint-based genetic algorithms,machine requirement,domain problem,semiconductor company,semiconductor assembly industry,machine allocation,optimization problem,semiconductor manufacturing,total machines allocation,manufacturing industries,assembly,investments,profitability,minimisation,costing,genetic algorithms,genetic algorithm,production,gallium,stochastic processes,satisfiability,resource management,optimization,investment,demand forecasting,uncertainty
Resource management,Mathematical optimization,Demand forecasting,Computer science,Semiconductor device fabrication,Capacity planning,Profitability index,Activity-based costing,Optimization problem,Management science,Genetic algorithm
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
2
Name
Order
Citations
PageRank
Umi Kalsom Yusof164.69
Safaai Deris225642.99