6,153 research outputs found
Clips: a capacity and lead time integrated procedure for scheduling.
We propose a general procedure to address real life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its own route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on a Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four phase hierarchical operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real life shops. We therefore report on a real life application.Model; Models; Applications; Product; Scheduling;
Optimal Network Control in Partially-Controllable Networks
The effectiveness of many optimal network control algorithms (e.g.,
BackPressure) relies on the premise that all of the nodes are fully
controllable. However, these algorithms may yield poor performance in a
partially-controllable network where a subset of nodes are uncontrollable and
use some unknown policy. Such a partially-controllable model is of increasing
importance in real-world networked systems such as overlay-underlay networks.
In this paper, we design optimal network control algorithms that can stabilize
a partially-controllable network. We first study the scenario where
uncontrollable nodes use a queue-agnostic policy, and propose a low-complexity
throughput-optimal algorithm, called Tracking-MaxWeight (TMW), which enhances
the original MaxWeight algorithm with an explicit learning of the policy used
by uncontrollable nodes. Next, we investigate the scenario where uncontrollable
nodes use a queue-dependent policy and the problem is formulated as an MDP with
unknown queueing dynamics. We propose a new reinforcement learning algorithm,
called Truncated Upper Confidence Reinforcement Learning (TUCRL), and prove
that TUCRL achieves tunable three-way tradeoffs between throughput, delay and
convergence rate
Optimization and sensitivity analysis of computer simulation models by the score function method
Experimental Design;Simulation;Optimization;Queueing Theory
The Power of Online Learning in Stochastic Network Optimization
In this paper, we investigate the power of online learning in stochastic
network optimization with unknown system statistics {\it a priori}. We are
interested in understanding how information and learning can be efficiently
incorporated into system control techniques, and what are the fundamental
benefits of doing so. We propose two \emph{Online Learning-Aided Control}
techniques, and , that explicitly utilize the
past system information in current system control via a learning procedure
called \emph{dual learning}. We prove strong performance guarantees of the
proposed algorithms: and achieve the
near-optimal utility-delay tradeoff
and possesses an convergence time.
and are probably the first algorithms that
simultaneously possess explicit near-optimal delay guarantee and sub-linear
convergence time. Simulation results also confirm the superior performance of
the proposed algorithms in practice. To the best of our knowledge, our attempt
is the first to explicitly incorporate online learning into stochastic network
optimization and to demonstrate its power in both theory and practice
Run Time Approximation of Non-blocking Service Rates for Streaming Systems
Stream processing is a compute paradigm that promises safe and efficient
parallelism. Modern big-data problems are often well suited for stream
processing's throughput-oriented nature. Realization of efficient stream
processing requires monitoring and optimization of multiple communications
links. Most techniques to optimize these links use queueing network models or
network flow models, which require some idea of the actual execution rate of
each independent compute kernel within the system. What we want to know is how
fast can each kernel process data independent of other communicating kernels.
This is known as the "service rate" of the kernel within the queueing
literature. Current approaches to divining service rates are static. Modern
workloads, however, are often dynamic. Shared cloud systems also present
applications with highly dynamic execution environments (multiple users,
hardware migration, etc.). It is therefore desirable to continuously re-tune an
application during run time (online) in response to changing conditions. Our
approach enables online service rate monitoring under most conditions,
obviating the need for reliance on steady state predictions for what are
probably non-steady state phenomena. First, some of the difficulties associated
with online service rate determination are examined. Second, the algorithm to
approximate the online non-blocking service rate is described. Lastly, the
algorithm is implemented within the open source RaftLib framework for
validation using a simple microbenchmark as well as two full streaming
applications.Comment: technical repor
The Power of Online Learning in Stochastic Network Optimization
In this paper, we investigate the power of online learning in stochastic
network optimization with unknown system statistics {\it a priori}. We are
interested in understanding how information and learning can be efficiently
incorporated into system control techniques, and what are the fundamental
benefits of doing so. We propose two \emph{Online Learning-Aided Control}
techniques, and , that explicitly utilize the
past system information in current system control via a learning procedure
called \emph{dual learning}. We prove strong performance guarantees of the
proposed algorithms: and achieve the
near-optimal utility-delay tradeoff
and possesses an convergence time.
and are probably the first algorithms that
simultaneously possess explicit near-optimal delay guarantee and sub-linear
convergence time. Simulation results also confirm the superior performance of
the proposed algorithms in practice. To the best of our knowledge, our attempt
is the first to explicitly incorporate online learning into stochastic network
optimization and to demonstrate its power in both theory and practice
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