Packaging Capacity Analysis of a Biopharmaceutical
Production Operation
Steve Chu and Prasad V. Saraph (Bayer
HealthCare) and Lee Schruben (University of California)
Abstract:
This paper discusses an application of discrete event
simulation in analyzing packaging capacity at Bayer Corporation’s Berkeley,
California facility. A discrete event simulation model was used to estimate
output under differing employee staffing and scheduling policies, taking into
consideration product and equipment requirements. This model was also used to
study the effects on packaging operations due to changes in the manufacturing
environment. The model and its recommendations were used to support a major
business process decision.
Simulation with Real World Network
Stacks
Sam Jansen and Anthony McGregor (University of Waikato)
Abstract:
Network simulation is used widely in network research
to test new protocols, modifications to existing protocols and new ideas. The
tool used in many cases is ns-2. The nature of the ns-2 protocols means that
they are often based on theoretical models that might not behave in the same
way as real networks. This paper presents the Network Simulation Cradle which
allows real world network stacks to be used in a wrapper that allows the
stacks protocols to be used in the ns-2 network simulator. The network stacks
from the open source operating systems Linux, FreeBSD and OpenBSD are included
in the simulation cradle as well as a stack designed for embedded systems,
lwIP. Our results show that ns-2's TCP implementations do not match observed
behaviour from real machines in some respects and using the Network Simulation
Cradle produces results closer to real world network stacks.
Queuing Models of Vehicle-based Automated Material
Handling Systems in Semiconductor Fabs
Dima Nazzal and Leon F.
McGinnis (Georgia Institute of Technology)
Abstract:
This research explores analytical models useful in the
design of vehicle-based Automated Material Handling Systems (AMHS) to support
semiconductor manufacturing. The objective is to correctly estimate the
throughput and move request delay. The analysis approach is based on queuing
network models, but taking into account details of the operation of the AMHS.
We analyze the vehicles movement in the system using a Markov chain. This
analysis provides the essential parameters such as the blocking probabilities
in order to estimate the performance measures. A numerical example is provided
to demonstrate and validate the queuing model.
