A Diffusion Approximation for a GI/GI/1 Queue with Balking or Reneging

Ward, Amy R.; Glynn, Peter W.

doi:10.1007/s11134-005-3282-3

Cited by 141 publications

(165 citation statements)

References 18 publications

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“…In fact, to the best of our knowledge, this quantity has never been studied in the classical queuing, which seems to be more interested in the stability and analysis of the whole system rather than the dynamics of each individual in the system. Finally, despite some similarity between the limit order with order cancellation and the reneging queues (Ward and Glynn [60,61]), the motivation and characteristics of cancellations in the limit order are very complex, especially when there are nondisplayable orders. This is an important issue because different cancellation assumptions lead to different fluid and diffusion limits, and the corresponding optimal trading strategies could be qualitatively distinct.…”

Section: Continuous-time Model With Reduced Formmentioning

confidence: 99%

Optimal Placement in a Limit Order Book

Guo¹

2013

Theory Driven by Influential Applications

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Please scroll down for article-it is on subsequent pagesINFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Abstract Algorithmic trading refers to the automatic and rapid trading of large quantities with orders specified and implemented by an algorithm. Roughly speaking, algorithmic trading is based on two different time scales: the daily or weekly scale and a smaller (10-to 100-second) time scale. These two time scales essentially reflect the two steps by which the traders slice and place orders. The first step is to optimally slice big orders into smaller ones on a daily basis with the goal to minimize the price impact and/or to maximize the expected utility; the second step is to optimally place the orders within seconds. The former is the well-known optimal execution problem, and the latter is the much less studied optimal placement problem. This paper reviews several simple models and approaches for the optimal placement problem. Several most relevant statistical issues are presented, together with a brief discussion on the key differences between the system of limit order book and the multiclass queues with reneging.

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Section: Continuous-time Model With Reduced Formmentioning

confidence: 99%

Optimal Placement in a Limit Order Book

Guo¹

2013

Theory Driven by Influential Applications

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“…Whitt (2006) uses fluid models to approximate the steady state performance measures of queueing models with generally distributed interarrival, service and abandonment times. Other studies include those by Baccelli and Hebuterne (1981), Brandt and Brandt (2002), Ward andGlynn (2003), Whitt (2005), Avramidis et al (2010), and references therein.…”

Section: Literature Reviewmentioning

confidence: 99%

Online scheduling policies for multiclass call centers with impatient customers

Jouini¹,

Pot²,

Koole³

et al. 2010

European Journal of Operational Research

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We consider a call center with two classes of impatient customers: premium and regular classes. Modeling our call center as a multiclass GI/GI/s + M queue, we focus on developing scheduling policies that satisfy a target ratio constraint on the abandonment probabilities of premium customers to regular ones. The problem is inspired by a real call center application in which we want to reach some predefined preference between customer classes for any workload condition. The motivation for this constraint comes from the difficulty of predicting in a quite satisfying way the workload. In such a case, the traditional routing problem formulation with differentiated service levels for different customer classes would be useless. For this new problem formulation, we propose two families of online scheduling policies: queue joining and call selection policies. The principle of our policies is that we adjust their routing rules by dynamically changing their parameters. We then evaluate the performance of these policies through a numerical study. The policies are characterized by simplicity and ease of implementation.

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“…Such subsystems are always a component of the automated systems in various fields of activity-science, education, industry, economy, medicine, social sphere [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

The Assessment of Properties of the Information Flow in the Subsystem «The Measuring Sensor – The Processing Element»

Nikolaev¹,

Stuchkov²

2016

MATEC Web Conf.

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Abstract. Analyzed the scope and types of automated information systems containing subsystems of the "measuring sensor -element processing". On the basis of the Queuing theory a model of evaluating the properties of information flow in the subsystem. The time spent by the sensor data generation and processing time of the output data array to the processing elements described by an arbitrary distribution law. Using little's formula the analytical solution allows to estimate the required level of performance of the subsystem. The research of the obtained solution in the range of parameters of interest. On the basis of General theoretical solutions identified four private practical case, different values of the coefficients of variation of information flows.

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A Diffusion Approximation for a GI/GI/1 Queue with Balking or Reneging

Cited by 141 publications

References 18 publications

Optimal Placement in a Limit Order Book

Optimal Placement in a Limit Order Book

Online scheduling policies for multiclass call centers with impatient customers

The Assessment of Properties of the Information Flow in the Subsystem «The Measuring Sensor – The Processing Element»

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