8,301 research outputs found
Energy-saving policies for temperature-controlled production systems with state-dependent setup times and costs
There are numerous practical examples of production systems with servers that require heating in order to process jobs. Such production systems may realize considerable energy savings by temporarily switching off the heater and building up a queue of jobs to be processed later, at the expense of extra queueing costs. In this paper, we optimize this trade-off between energy and queueing costs. We model the production system as an M/G/1 queue with a temperature-controlled server that can only process jobs if a minimum production temperature is satisfied. The time and energy required to heat a server depend on its current temperature, hence the setup times and setup costs for starting production are state dependent. We derive the optimal policy structure for a fluid queue approximation, called a wait-heat-clear policy. Building upon these insights, for the M/G/1 queue we derive exact and approximate costs for various intuitive types of wait-heat-clear policies. Numerical results indicate that the optimal wait-heat-clear policy yields average cost savings of over 40% compared to always keeping the server at the minimum production temperature. Furthermore, an encouraging result for practice is that simple heuristics, depending on the queue length only, have near-optimal performance
State-dependent importance sampling for a Jackson tandem network
This paper considers importance sampling as a tool for rare-event simulation. The focus is on estimating the probability of overflow in the downstream queue of a Jacksonian two-node tandem queue – it is known that in this setting ‘traditional’ state-independent importance-sampling distributions perform poorly. We therefore concentrate on developing a state-dependent change of measure, that we prove to be asymptotically efficient.\ud
More specific contributions are the following. (i) We concentrate on the probability of the second queue exceeding a certain predefined threshold before the system empties. Importantly, we identify an asymptotically efficient importance-sampling distribution for any initial state of the system. (ii) The choice of the importance-sampling distribution is backed up by appealing heuristics that are rooted in large-deviations theory. (iii) Our method for proving asymptotic efficiency is substantially more straightforward than some that have been used earlier. The paper is concluded by simulation experiments that show a considerable speed up
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
In the study of human learning, there is broad evidence that our ability to
retain information improves with repeated exposure and decays with delay since
last exposure. This plays a crucial role in the design of educational software,
leading to a trade-off between teaching new material and reviewing what has
already been taught. A common way to balance this trade-off is spaced
repetition, which uses periodic review of content to improve long-term
retention. Though spaced repetition is widely used in practice, e.g., in
electronic flashcard software, there is little formal understanding of the
design of these systems. Our paper addresses this gap in three ways. First, we
mine log data from spaced repetition software to establish the functional
dependence of retention on reinforcement and delay. Second, we use this memory
model to develop a stochastic model for spaced repetition systems. We propose a
queueing network model of the Leitner system for reviewing flashcards, along
with a heuristic approximation that admits a tractable optimization problem for
review scheduling. Finally, we empirically evaluate our queueing model through
a Mechanical Turk experiment, verifying a key qualitative prediction of our
model: the existence of a sharp phase transition in learning outcomes upon
increasing the rate of new item introductions.Comment: Accepted to the ACM SIGKDD Conference on Knowledge Discovery and Data
Mining 201
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