Essays on service systems with matching
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- Tez No: 601917
- Danışmanlar: DR. AMY R. WARD
- Tez Türü: Doktora
- Konular: Kamu Yönetimi, Public Administration
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2018
- Dil: İngilizce
- Üniversite: University of Southern California
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 255
Özet
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Özet (Çeviri)
We study operational problems in service systems in which matching plays an important role. Specifically, we focus on operational problems in fork-join networks and ridesharing platforms. In the first chapter of the dissertation, we study scheduling decisions in fork-join networks. In the second and third chapters, we study matching and pricing decisions in ridesharing platforms. A fork-join network is a special type of queueing network in which processing of jobs occurs both sequentially and in parallel. Fork-join networks are prevalent in many application domains, such as computer systems, healthcare, manufacturing, and project management. The parallel processing of jobs gives rise to synchronization constraints that can be a main reason for job delay. In comparison with feedforward queueing networks that have only sequential processing of jobs, the approximation and control of fork-join networks is less understood. Therefore, in the first chapter of the dissertation, we study a specific fork-join processing network with multiple job types in which there is first a fork operation, then there are activities that can be performed in parallel, and then a join operation. The difficulty is that some of the activities that can be performed in parallel require a shared resource. We solve the scheduling problem for that shared server (that is, which type of job to prioritize at any given time) when that server is in heavy traffic and propose a scheduling policy that is asymptotically optimal in diffusion scale. Then, we extend our proposed scheduling policy to more general fork-join processing networks. Ridesharing platforms are online mobile platforms which match paying customers who need a ride with drivers who provide transportation. Some examples of these platforms are Uber and Lyft in the USA, Didi Chuxing in China, Ola in India, and Grab in Southeast Asia. When a customer requests a ride, the ridesharing firm should charge a price and offer a driver to the customer. The matching decisions affect the overall number of customers matched because they impact whether or not future available drivers will be close to the locations of arriving customers. The pricing decisions are important because they have opposite effect on the customer demand and driver supply. As the price in an area increases, customer demand decreases but the driver supply (roughly speaking) increases in that area. Since customer demand and driver supply change dramatically over time, an ideal ridesharing model should have time dependent parameters and the customer and driver arrival rates should depend on the pricing and matching decisions. However, such a model is very difficult to analyze. Therefore, in the second chapter of the dissertation, we present a model in which the prices are given, customer and driver arrival rates are time dependent but exogenous, and we optimize the matching decisions. We propose to base the matching decisions on the solution to 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 pick-up, and (iii) 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. We leverage that result to also prove the asymptotic optimality of a myopic LP-based matching policy when drivers are fully utilized. In the third chapter of the dissertation, we present a ridesharing model in which the customer and driver behaviors are endogenous but the parameters are time homogeneous. We jointly optimize the pricing and matching decisions. We prove that neither optimizing the pricing decisions while keeping the matching decisions simple nor optimizing the matching decisions while keeping the pricing decisions simple provides better performance than the simple pricing and matching decisions do. (Simple pricing decisions mean that the firm charges the same price in all areas of the city and simple matching decisions mean that a customer can be offered a driver only from the same area.) In other words, the firm cannot achieve an optimal performance by focusing on only the pricing (matching) decisions and ignoring the matching (pricing) decisions. Therefore, the pricing and the matching decisions should be optimized simultaneously. Moreover, we derive conditions under which simple pricing and matching decisions are optimal and we derive conditions under which sophisticated pricing and matching decisions are necessary for optimal performance.
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