527,174 research outputs found
A blind hierarchical coherent search for gravitational-wave signals from coalescing compact binaries in a network of interferometric detectors
We describe a hierarchical data analysis pipeline for coherently searching
for gravitational wave (GW) signals from non-spinning compact binary
coalescences (CBCs) in the data of multiple earth-based detectors. It assumes
no prior information on the sky position of the source or the time of
occurrence of its transient signals and, hence, is termed "blind". The pipeline
computes the coherent network search statistic that is optimal in stationary,
Gaussian noise, and allows for the computation of a suite of alternative
statistics and signal-based discriminators that can improve its performance in
real data. Unlike the coincident multi-detector search statistics employed so
far, the coherent statistics are different in the sense that they check for the
consistency of the signal amplitudes and phases in the different detectors with
their different orientations and with the signal arrival times in them. The
first stage of the hierarchical pipeline constructs coincidences of triggers
from the multiple interferometers, by requiring their proximity in time and
component masses. The second stage follows up on these coincident triggers by
computing the coherent statistics. The performance of the hierarchical coherent
pipeline on Gaussian data is shown to be better than the pipeline with just the
first (coincidence) stage.Comment: 12 pages, 3 figures, accepted for publication in Classical and
Quantum Gravit
Two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve
Purpose: The aim of this research is to develop a two stages optimization model on make or buy analysis and quality improvement considering learning and forgetting curve. The first stage model is developed to determine the optimal selection of process/suppliers and the component allocation to those corresponding process/suppliers. The second stage model deals with quality improvement efforts to determine the optimal investment to maximize Return on Investment (ROI) by taking into consideration the learning and forgetting curve.
Design/methodology/approach: The research used system modeling approach by mathematically modeling the system consists of a manufacturer with multi suppliers where the manufacturer tries to determine the best combination of their own processes and suppliers to minimize certain costs and provides funding for quality improvement efforts for their own processes and suppliers sides.
Findings: This research provides better decisions in make or buy analysis and to improve the components by quality investment considering learning and forgetting curve.
Research limitations/implications: This research has limitations concerning investment fund that assumed to be provided by the manufacturer which in the real system the fund may be provided by the suppliers. In this model we also does not differentiate two types of learning, namely autonomous and induced learning.
Practical implications: This model can be used by a manufacturer to gain deeper insight in making decisions concerning process/suppliers selection along with component allocation and how to improve the component by investment allocation to maximize ROI.
Originality/value: This paper combines two models, which in previous research the models are discussed separately. The inclusions of learning and forgetting also gives a new perspective in quality investment decision.Peer Reviewe
Adaptive Two-stage Stochastic Programming with an Application to Capacity Expansion Planning
Multi-stage stochastic programming is a well-established framework for
sequential decision making under uncertainty by seeking policies that are fully
adapted to the uncertainty. Often such flexible policies are not desirable, and
the decision maker may need to commit to a set of actions for a number of
planning periods. Two-stage stochastic programming might be better suited to
such settings, where the decisions for all periods are made here-and-now and do
not adapt to the uncertainty realized. In this paper, we propose a novel
alternative approach, where the stages are not predetermined but part of the
optimization problem. Each component of the decision policy has an associated
revision point, a period prior to which the decision is predetermined and after
which it is revised to adjust to the uncertainty realized thus far. We motivate
this setting using the multi-period newsvendor problem by deriving an optimal
adaptive policy. We label the proposed approach as adaptive two-stage
stochastic programming and provide a generic mixed-integer programming
formulation for finite stochastic processes. We show that adaptive two-stage
stochastic programming is NP-hard in general. Next, we derive bounds on the
value of adaptive two-stage programming in comparison to the two-stage and
multi-stage approaches for a specific problem structure inspired by the
capacity expansion planning problem. Since directly solving the mixed-integer
linear program associated with the adaptive two-stage approach might be very
costly for large instances, we propose several heuristic solution algorithms
based on the bound analysis. We provide approximation guarantees for these
heuristics. Finally, we present an extensive computational study on an
electricity generation capacity expansion planning problem and demonstrate the
computational and practical impacts of the proposed approach from various
perspectives
Computational procedures for stochastic multi-echelon production systems
This paper is concerned with the numerical evaluation of multi-echelon production systems. Each stage requires a fixed predetermined leadtime; furthermore, we assume a stochastic, stationary end-time demand process. In a previous paper, we have developed an analytical framework for determining optimal control policies for such systems under an average cost criterion.\ud
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The current paper is based on this analytical theory but discusses computational aspects, in particular for serial and assembly systems. A hierarchical (exact) decomposition of these systems can be obtained by considering echelon stocks and by transforming penalty and holding costs accordingly. The one-dimensional problems arising after this decomposition however involve incomplete convolutions of distribution functions, which are only recursively defined. We develop numerical procedures for analysing these incomplete convolutions; these procedures are based on approximations of distribution functions by mixtures of Erlang distributions. Combining the analytically obtained (exact) decomposition results with these numerical procedures enables us to quickly determine optimal order-up-to levels for all stages. Moreover, expressions for the customer service level of such a multi-stage are obtained, yielding the possibility to determine policies which minimize average inventory holding costs, given a service level constraint
Variable dimension weighted universal vector quantization and noiseless coding
A new algorithm for variable dimension weighted universal coding is introduced. Combining the multi-codebook system of weighted universal vector quantization (WUVQ), the partitioning technique of variable dimension vector quantization, and the optimal design strategy common to both, variable dimension WUVQ allows mixture sources to be effectively carved into their component subsources, each of which can then be encoded with the codebook best matched to that source. Application of variable dimension WUVQ to a sequence of medical images provides up to 4.8 dB improvement in signal to quantization noise ratio over WUVQ and up to 11 dB improvement over a standard full-search vector quantizer followed by an entropy code. The optimal partitioning technique can likewise be applied with a collection of noiseless codes, as found in weighted universal noiseless coding (WUNC). The resulting algorithm for variable dimension WUNC is also described
On multi-stage production/inventory systems under stochastic demand
This paper was presented at the 1992 Conference of the International Society of Inventory Research in Budapest, as a tribute to professor Andrew C. Clark for his inspiring work on multi-echelon inventory models both in theory and practice. It reviews and extends the work of the authors on periodic review serial and convergent multi-echelon systems under stochastic stationary demand. In particular, we highlight the structure of echelon cost functions which play a central role in the derivation of the decomposition results and the optimality of base stock policies. The resulting optimal base stock policy is then compared with an MRP system in terms of cost effectiveness, given a predefined target customer service level. Another extension concerns an at first glance rather different problem; it is shown that the problem of setting safety leadtimes in a multi-stage production-to-order system with stochastic lead times leads to similar decomposition structures as those derived for multi-stage inventory systems. Finally, a discussion on possible extensions to capacitated models, models with uncertainty in both demand and production lead time as well as models with an aborescent structure concludes the paper
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