Robust heavy-traffic approximations for service systems facing overdispersed demand

\u3cp\u3eArrival processes to service systems often display fluctuations that are larger than anticipated under the Poisson assumption, a phenomenon that is referred to as overdispersion. Motivated by this, we analyze a class of discrete-time stochastic models for which we derive heavy-traffic approximations that are scalable in the system size. Subsequently, we show how this leads to novel capacity sizing rules that acknowledge the presence of overdispersion. This, in turn, leads to robust approximations for performance characteristics of systems that are of moderate size and/or may not operate in heavy traffic.\u3c/p\u3

Robust heavy-traffic approximations for service systems facing overdispersed demand

Arrival processes to service systems are prevalently assumed non-homogeneous Poisson. Though mathematically convenient, arrival processes are often more volatile, a phenomenon that is referred to as overdispersion. Motivated by this, we analyze a class of stochastic models for which we develop performance approximations that are scalable in the system size, under a heavy-traffic condition. Subsequently, we show how this leads to novel capacity sizing rules that acknowledge the presence of overdispersion. This, in turn, leads to robust approximations for performance characteristics of systems that are of moderate size and/or may not operate in heavy traffic. To illustrate the value of our approach, we apply it to actual arrival data of an emergency department of a hospital

Robust heavy-traffic approximations for service systems facing overdispersed demand Citation for published version (APA): Robust heavy-traffic approximations for service systems facing overdispersed demand

. Robust heavy-traffic approximations for service systems facing overdispersed demand. arXiv, [1512.05581V1]. Document status and date: Published: 17/12/2015 Document Version: Publisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement: www.tue.nl/taverne Take down policy If you believe that this document breaches copyright please contact us at: [email protected] providing details and we will investigate your claim. Abstract Arrival processes to service systems are prevalently assumed non-homogeneous Poisson. Though mathematically convenient, arrival processes are often more volatile, a phenomenon that is referred to as overdispersion. Motivated by this, we analyze a class of stochastic models for which we develop performance approximations that are scalable in the system size, under a heavy traffic condition. Subsequently, we show how this leads to novel capacity sizing rules that acknowledge the presence of overdispersion. This, in turn, leads to robust approximations for performance characteristics of systems that are of moderate size and/or may not operate in heavy traffic. To illustrate the value of our approach, we apply it to actual arrival data of an emergency department of a hospital