Researcher:
Özkan, Erhun

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Erhun

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Özkan

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Özkan, Erhun

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2020 - 20214

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Researcher Of Publications: search.filters.isAuthorOfPublication.4077c684-e6c1-4e6a-9a60-a867c1731a98

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Now showing 1 - 4 of 4
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    On the optimal control of parallel processing networks with resource collaboration and multitasking
    (INFORMS Institute for Operations Research and the Management Sciences, 2021) N/A; Özkan, Erhun; Faculty Member; School of Medicine; 289255
    We study scheduling control of parallel processing networks in which some resources need to simultaneously collaborate to perform some activities and some resources multitask. Resource collaboration and multitasking give rise to synchronization constraints in resource scheduling when the resources are not divisible, that is, when the resources cannot be split. The synchronization constraints affect the system performance significant-ly. For example, because of those constraints, the system capacity can be strictly less than the capacity of the bottleneck resource. Furthermore, the resource scheduling decisions are not trivial under those constraints. For example, not all static prioritization policies retain the maximum system capacity, and the ones that retain the maximum system capacity do not necessarily minimize the delay (or, in general, the holding cost). We study optimal scheduling control of a class of parallel networks and propose a dynamic prioritization policy that retains the maximum system capacity and is asymptotically optimal in diffusion scale and a conventional heavy-traffic regime with respect to the expected discounted total holding cost objective.
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    Joint pricing and matching in ride-sharing systems
    (Elsevier, 2020) N/A; Department of Business Administration; Özkan, Erhun; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 294016
    Ride-sharing firms use pricing and matching decisions to control the ride-sharing platforms. Those decisions can be made jointly or interchangeably, which raises the following questions: Is matching optimization necessary? Specifically, is fixing the matching decisions to a simple rule and optimizing only the pricing decisions enough to achieve the optimal performance? In order to answer these questions, we study the interplay between pricing and matching decisions of a ride-sharing firm. There are many studies in the ride-sharing literature that optimize the pricing decisions under an assumed matching policy. However, we show that ignoring matching optimization can result in subpar overall performance. We formulate a stylized ride-sharing model that captures customer and driver behaviors and geospatial nature of the system. Customers are both price and delay sensitive, and drivers are strategic and self-scheduling. We prove that optimizing the matching decisions have first-order effect on the system performance. We show that fixing the matching decisions and optimizing only the pricing decisions does not maximize the number of matchings in general. Similarly, we show that fixing the pricing decisions and optimizing only the matching decisions is not optimal in general. Finally, we show that optimizing in only one dimension (either pricing or matching) has no benefit to the firm under some conditions, whereas joint pricing and matching optimization can lead to a significant performance increase.
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    Dynamic matching for real-time ride sharing
    (The Institute for Operations Research and the Management Sciences (INFORMS), 2020) Ward, Amy R.; Department of Business Administration; Özkan, Erhun; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 294016
    In a ride-sharing system, arriving customers must be matched with available drivers. These decisions affect the overall number of customers matched, because they impact whether future available drivers will be close to the locations of arriving customers. A common policy used in practice is the closest driver policy, which offers an arriving customer the closest driver. This is an attractive policy because it is simple and easy to implement. However, we expect that parameter-based policies can achieve better per-formance. We propose matching policies based on a continuous linear program (CLP) that accounts for (i) the differing arrival rates of customers and drivers in different areas of the city, (ii) how long customers are willing to wait for driver pickup, (iii) how long drivers are willing to wait for a customer, and (iv) the time-varying nature of all the aforementioned parameters. We prove asymptotic optimality of a forward-looking CLP-based policy in a large market regime and of a myopic linear program–based matching policy when drivers are fully utilized. When pricing affects customer and driver arrival rates and parameters are time homogeneous, we show that asymptotically optimal joint pricing and matching decisions lead to fully utilized drivers under mild conditions.
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    PublicationOpen Access
    Control of fork-join processing networks with multiple job types and parallel shared resources
    (The Institute for Operations Research and the Management Sciences (INFORMS), 2021) Department of Business Administration; Özkan, Erhun; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 294016
    A fork-join processing network is a queueing network in which tasks associated with a job can be processed simultaneously. Fork-join processing networks are prevalent in computer systems, healthcare, manufacturing, project management, justice systems, and so on. Unlike the conventional queueing networks, fork-join processing networks have synchronization constraints that arise because of the parallel processing of tasks and can cause significant job delays. We study scheduling in fork-join processing networks with multiple job types and parallel shared resources. Jobs arriving in the system fork into arbitrary number of tasks, then those tasks are processed in parallel, and then they join and leave the network. There are shared resources processing multiple job types. We study the scheduling problem for those shared resources (i.e., which type of job to prioritize at any given time) and propose an asymptotically optimal scheduling policy in diffusion scale.