100,901 research outputs found
Heavy-tailed Distributions In Stochastic Dynamical Models
Heavy-tailed distributions are found throughout many naturally occurring
phenomena. We have reviewed the models of stochastic dynamics that lead to
heavy-tailed distributions (and power law distributions, in particular)
including the multiplicative noise models, the models subjected to the
Degree-Mass-Action principle (the generalized preferential attachment
principle), the intermittent behavior occurring in complex physical systems
near a bifurcation point, queuing systems, and the models of Self-organized
criticality. Heavy-tailed distributions appear in them as the emergent
phenomena sensitive for coupling rules essential for the entire dynamics
Dynamic scheduling in a multi-product manufacturing system
To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation
A Dynamical Systems Approach for Static Evaluation in Go
In the paper arguments are given why the concept of static evaluation has the
potential to be a useful extension to Monte Carlo tree search. A new concept of
modeling static evaluation through a dynamical system is introduced and
strengths and weaknesses are discussed. The general suitability of this
approach is demonstrated.Comment: IEEE Transactions on Computational Intelligence and AI in Games, vol
3 (2011), no
Project scheduling under undertainty – survey and research potentials.
The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;
A Lightweight, Non-intrusive Approach for Orchestrating Autonomously-managed Network Elements
Software-Defined Networking enables the centralized orchestration of data
traffic within a network. However, proposed solutions require a high degree of
architectural penetration. The present study targets the orchestration of
network elements that do not wish to yield much of their internal operations to
an external controller. Backpressure routing principles are used for deriving
flow routing rules that optimally stabilize a network, while maximizing its
throughput. The elements can then accept in full, partially or reject the
proposed routing rule-set. The proposed scheme requires minimal, relatively
infrequent interaction with a controller, limiting its imposed workload,
promoting scalability. The proposed scheme exhibits attracting network
performance gains, as demonstrated by extensive simulations and proven via
mathematical analysis.Comment: 6 pages 7, figures, IEEE ISCC'1
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