Integer Programming Approaches for Distributionally Robust Chance Constraints with Adjustable Risks

We study distributionally robust chance constrained programs (DRCCPs) with individual chance constraints and random right-hand sides. The DRCCPs treat the risk tolerances associated with the distributionally robust chance constraints (DRCCs) as decision variables to trade off between the system cost and risk of violations by penalizing the risk tolerances in the objective function. We consider two types of Wasserstein ambiguity sets: one with finite support and one with a continuum of realizations. By exploring the hidden discrete structures, we develop mixed integer programming reformulations under the two types of ambiguity sets to determine the optimal risk tolerance for the chance constraint. Valid inequalities are derived to strengthen the formulations. We test instances with transportation problems of diverse sizes and a demand response management problem

Customer Satisfaction and Equity Mispricing

This paper analyzes the relationship between customer satisfaction and long run stock returns. An equally-weighted portfolio of 230 customer satisfaction score documented companies in American customer satisfaction index (ACSI) delivers a five factor alpha of 3.16% per year. The major economic explanation for this portfolio’s continuous outperformance is that the companies with high customer satisfaction exhibit a high future sale growth. These findings are consistent with word of mouth theory stating that positive word of mouth from highly satisfied customers would help firms penetrate new and existing markets and thus lead to high future sales

Paradoxes of Traffic Flow and Economics of Congestion Pricing

This paper utilizes a unique county-level dataset to examine technical efficiency and technology gap in China’s agriculture. We classify the counties into four regions with distinctive levels of economic development, and hence production technologies. A meta-frontier analysis is applied to the counties. We find that although the eastern counties have the highest efficiency scores with respect to the regional frontier but the northeastern region leads in terms of agricultural production technology nationwide. Meanwhile, the mean efficiency of the northeastern counties is particularly low, suggesting technology and knowledge diffusion within region might help to improve production efficiency and thus output.China’s grain production; County-level; Metafrontier; Stochastic production frontier; Technical efficiency

What You Jointly Know Determines How You Act: Strategic Interactions in Prediction Markets

The primary goal of a prediction market is to elicit and aggregate information about some future event of interest. How well this goal is achieved depends on the behavior of self-interested market participants, which are crucially influenced by not only their private information but also their knowledge of others' private information, in other words, the information structure of market participants. In this paper, we model a prediction market using the now-classic logarithmic market scoring rule (LMSR) market maker as an extensive-form Bayesian game and aim to understand and characterize the game-theoretic equilibria of the market for different information structures. Prior work has shown that when participants' information is independent conditioned on the realized outcome of the event, the only type of equilibria in this setting has every participant race to honestly reveal their private information as soon as possible, which is the most desirable outcome for the market's goal of information aggregation. This paper considers the remaining two classes of information structures: participants' information being unconditionally independent (the I game) and participants' information being both conditionally and unconditionally dependent (the D game). We characterize the unique family of equilibria for the I game with finite number of participants and finite stages. At any equilibrium in this family, if player i's last stage of participation in the market is after player j's, player i only reveals his information after player j's last stage of participation. This suggests that players race to delay revealing their information, which is probably the least desirable outcome for the market's goal. We consider a special case of the D game and cast insights on possible equilibria if one exists.Engineering and Applied Science

Transient asymptotics of the modified Camassa-Holm equation

We investigate long time asymptotics of the modified Camassa-Holm equation in three transition zones under a nonzero background. The first transition zone lies between the soliton region and the first oscillatory region, the second one lies between the second oscillatory region and the fast decay region, and possibly, the third one, namely, the collisionless shock region, that bridges the first transition region and the first oscillatory region. Under a low regularity condition on the initial data, we obtain Painlev\'e-type asymptotic formulas in the first two transition regions, while the transient asymptotics in the third region involves the Jacobi theta function. We establish our results by performing a $\bar{\partial}$ nonlinear steepest descent analysis to the associated Riemann-Hilbert problem.Comment: 58 pages, 16 figures. Comments are welcom

Convex Nonlinear and Integer Programming Approaches for Distributionally Robust Optimization of Complex Systems

The primary focus of the dissertation is to develop distributionally robust optimization (DRO) models and related solution approaches for decision making in energy and healthcare service systems with uncertainties, which often involves nonlinear constraints and discrete decision variables. Without assuming specific distributions, DRO techniques solve for solutions against the worst-case distribution of system uncertainties. In the DRO framework, we consider both risk-neutral (e.g., expectation) and risk-averse (e.g., chance constraint and Conditional Value-at-Risk (CVaR)) measures. The aim is twofold: i) developing efficient solution algorithms for DRO models with integer and/or binary variables, sometimes nonlinear structures and ii) revealing managerial insights of DRO models for specific applications. We mainly focus on DRO models of power system operations, appointment scheduling, and resource allocation in healthcare. Specifically, we first study stochastic optimal power flow (OPF), where (uncertain) renewable integration and load control are implemented to balance supply and (uncertain) demand in power grids. We propose a chance-constrained OPF (CC-OPF) model and investigate its DRO variant which is reformulated as a semidefinite programming (SDP) problem. We compare the DRO model with two benchmark models, in the IEEE 9-bus, 39-bus, and 118-bus systems with different flow congestion levels. The DRO approach yields a higher probability of satisfying the chance constraints and shorter solution time. It also better utilizes reserves at both generators and loads when the system has congested flows. Then we consider appointment scheduling under random service durations with given (fixed) appointment arrival order. We propose a DRO formulation and derive a conservative SDP reformulation. Furthermore, we study a scheduling variant under random no-shows of appointments and derive tractable reformulations for certain beliefs of no-show patterns. One preceding problem of appointment scheduling in the healthcare service operations is the surgery block allocation problem that assigns surgeries to operating rooms. We derive an equivalent 0-1 SDP reformulation and a less conservative 0-1 second-order cone programming (SOCP) reformulation for its DRO model. Finally, we study distributionally robust chance-constrained binary programs (DCBP) for limiting the probability of undesirable events, under mean-covariance information. We reformulate DCBPs as equivalent 0-1 SOCP formulations under two moment-based ambiguity sets. We further exploit the submodularity of the 0-1 SOCP reformulations under diagonal and non-diagonal matrices. We derive extended polymatroid inequalities via submodularity and lifting, which are incorporated into a branch-and-cut algorithm incorporated for efficiently solving DCBPs. We demonstrate the computational efficacy and solution performance with diverse instances of a chance-constrained bin packing problem.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149946/1/zyiling_1.pd

Traffic-Aware Transmission Mode Selection in D2D-enabled Cellular Networks with Token System

We consider a D2D-enabled cellular network where user equipments (UEs) owned by rational users are incentivized to form D2D pairs using tokens. They exchange tokens electronically to "buy" and "sell" D2D services. Meanwhile the devices have the ability to choose the transmission mode, i.e. receiving data via cellular links or D2D links. Thus taking the different benefits brought by diverse traffic types as a prior, the UEs can utilize their tokens more efficiently via transmission mode selection. In this paper, the optimal transmission mode selection strategy as well as token collection policy are investigated to maximize the long-term utility in the dynamic network environment. The optimal policy is proved to be a threshold strategy, and the thresholds have a monotonicity property. Numerical simulations verify our observations and the gain from transmission mode selection is observed.Comment: 7 pages, 6 figures. A shorter version is submitted to EUSIPC