Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Three Essays on Modeling Consumer Behavior and Its Operations Management Implications.

Traditionally, models used in operations management have considered the firm side of the problem by making simplifying assumptions on demand or market. In practice, however, consumers or agents in the market actively make decisions or choices based on self interest. This dissertation aims to analyze how insights and results from traditional models are affected when we account for such active decision making by consumers or the market. In Chapter II, we study how the customers' decision of joining the queue to receive a service varies by the individual incentive as well as the firm's capacity decision, which also depends on the firm’s selfishness. By considering three customer types: individual, collective, and social, and two firm types: profit maximizing and welfare maximizing, we are able to disentangle the effects of selfishness of the customers and the firm, and the interactions between these two in equilibrium. Among other results, we find that there can be a ``benefit of selfishness'' to consumers and the system, in contrast to the price of anarchy literature. In Chapter III, we discuss the customers' redemption behavior of loyalty points and its impact on the seller's pricing and inventory rationing strategy. We model the customer choice between cash or loyalty points by characterizing consumers in three dimensions: the reservation price, the point balance, and their perceived valuation of points. Applying this choice model into the seller's dynamic pricing model, we characterize the seller's optimal strategy that specifies the optimal price, the control of reward sales (black-out), and the redemption points. In Chapter IV, we study the customers’ substitution behavior when their preferred product is not available, and the seller's assortment optimization problem. Motivated by the exogenous demand model and the recently developed Markov chain model, we propose a new approximation to the random utility customer choice model called rescaled multi-attempt model. The key feature of our proposed approach is that the resulting approximate choice probability can be explicitly written. From a practical perspective, this allows the decision maker to use an off-the-shelf solver to solve a general assortment optimization problem with a variety of real-world constraints.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133387/1/hakjin_1.pd

Η Επίδραση της Πληροφόρησης στη Στρατηγική Συμπεριφορά των Πελατών σε Συστήματα Εξυπηρέτησης

Το αντικείμενο της διπλωματικής εργασίας είναι η μελέτη της στρατηγικής συμπεριφοράς των πελατών στη βασική Μαρκοβιανή ουρά με έναν υπηρέτη (Μ|Μ|1 ουρά) με παρατηρούντες και μη-παρατηρούντες πελάτες. Συγκεκριμένα, θεωρούμε μια Μ|Μ|1 ουρά, στην οποία φτάνει ένα ποσοστό p παρατηρούντων πελατών και ένα ποσοστό 1-p μη-παρατηρούντων πελατών και προσδιορίζουμε τα σημεία στρατηγικής ισορροπίας των πελατών ως προς το δίλημμα της εισόδου/άμεσης αποχώρησης. Επίσης, συγκρίνουμε τις στρατηγικές ισορροπίας, που μπορούν να θεωρηθούν ως το αποτέλεσμα ιδιοτελούς συμπεριφοράς, με τις κοινωνικά βέλτιστες στρατηγικές, μέσω του Τιμήματος της Αναρχίας και υπολογίζουμε τη συνάρτηση του ρυθμού κέρδους του διαχειριστή του συστήματος. Τέλος, παρουσιάζουμε αποτελέσματα σχετικά με την επίδραση της πληροφόρησης των πελατών στον ρυθμό κέρδους του διαχειριστή του συστήματος στην περίπτωση της Μ|Μ|1 ουράς με μη-παρατηρούντες πελάτες και μία αβέβαιη παράμετρο.The objective of this thesis is the study of the strategic customer behavior in the standard single-server Markovian queue (M/M/1 queue) with observing and non-observing customers. More concretely, we consider an M/M/1 queue, where a fraction p of the customers are observing, whereas a fraction 1-p of them are non-observing, and we determine the equilibrium strategy profiles with respect to the dilemma of joining/balking. Moreover, we compare the equilibrium strategies, that can be considered as the result of selfish customer behavior, with the socially optimal strategies, via the Price of Anarchy and we compute the profit rate function of the administrator of the system. Finally, we present several results on the influence of the information that receive the customers on the profit rate of the administrator of the system in the case of the M/M/1 queue with non-observing customers and a random parameter