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Modeling and decision making for queueing systems have been one of fundamental topics in operations research. For these problems, uncertainty models are established by estimation of key parameters such as expected interarrival and service times. In practice, however, their distributions are unknown, and decision makers are only given historical records of waiting times, which contain relevant but indirect information on the uncertainties. Their complex temporal dependence on the queueing dynamics and the absence of distributional information on the model primitives render estimation of queueing systems remarkably challenging. In the paper "Robust Queue Inference from Waiting Times" by Chaithanya Bandi, Eojin Han, and Alexej Proskynitopoulos, a new inference framework based on robust optimization is proposed to estimate unknown service times from waiting time observations. This new framework allows data-driven, distribution-free estimation on unknown model primitives by solving tractable optimization problems. Observational data from queueing systems are of great practical interest in many application areas because they can be leveraged for better statistical inference of service processes. However, these observations often only provide partial information of the system for various reasons in real-world settings. Moreover, their complex temporal dependence on the queueing dynamics and the absence of distributional information on the model primitives render estimation of queueing systems remarkably challenging. To this end, we consider the problem of inferring service times from waiting time observations. Specifically, we propose an inference framework based on robust optimization, where service times are described via sets that are calibrated by the observed waiting times. We provide conditions under which these data-driven uncertainty sets become asymptotically confident estimators of the service process; that is, they contain unknown service times almost surely as the number of observations grows. We also introduce tractable optimization formulations to compute bounds of various service time characteristics such as moments and risk measures. In this way, our approach is data driven and free of distributional assumptions on unknown model primitives, which is required by existing methods. We also generalize the proposed inference framework to tandem queues and feed-forward networks, offering broader capability in estimation of real-world queueing systems. Our simulation study demonstrates that the proposed approach easily incorporates information of arrival processes such as moments and correlations and performs consistently well on queueing networks under various settings. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.0091. [ABSTRACT FROM AUTHOR]

The paper by Han, Bandi, and Nohadani on "On Finite Adaptability in Two-Stage Distributionally Robust Optimization" studies finite adaptability with the goal to construct interpretable and easily implementable policies in the context of two-stage distributionally robust optimization problems. To achieve this, the set of uncertainty realizations needs to be partitioned. The authors show that an optimal partitioning can be accomplished via "translated orthants." They then propose a nondecreasing orthant partitioning and binary approximation to obtain the corresponding "orthant-based policies" from a mixed-integer optimization problem of a moderate size. For these policies, they provide provable performance bounds, generalizing the existing bounds in the literature. For more general settings, they also propose optimization formulations to obtain posterior lower bounds that can serve to evaluate performance. Two numerical experiments support these findings. A joint inventory-routing problem highlights the practical applicability for large-sized instances with mixed-integer recourse. A case study from a pharmacy retailer demonstrates that the orthant-based policies are less sensitive to cost parameters than optimal solutions, enabling these policies to outperform comparable methods when the realized cost ratio deviates from its nominal value. In many real applications, practitioners prefer policies that are interpretable and easy to implement. This tendency is magnified in sequential decision-making settings. In this paper, we leverage the concept of finite adaptability to construct policies for two-stage optimization problems. More specifically, we focus on the general setting of distributional uncertainties affecting the right-hand sides of constraints, because in a broad range of applications, uncertainties do not affect the objective function and recourse matrix. The aim is to construct policies that have provable performance bounds. This is done by partitioning the uncertainty realization and assigning a contingent decision to each piece. We first show that the optimal partitioning can be characterized by translated orthants, which only require the problem structure and hence are free of modeling assumptions. We then prove that finding the optimal partitioning is hard and propose a specific partitioning scheme with orthants, allowing the efficient computation of orthant-based policies via solving a mixed-integer optimization problem of a moderate size. By leveraging the geometry of this partitioning, we provide performance bounds of the orthant-based policies, which also generalize the existing bounds in the literature. These bounds offer multiple theoretical insights on the performance, for example, its independence on problem parameters. We also assess suboptimality in more general settings and provide techniques to obtain lower bounds. The proposed policies are applied to a stylized inventory routing problem with mixed-integer recourse. We also study the case of a pharmacy retailer by comparing alternative methods regarding computational performance and robustness to parameter variation. Funding: E. Han is funded by the Southern Methodist University start-up fund for this research. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2273. [ABSTRACT FROM AUTHOR]

We introduce a new class of adaptive policies called periodic-affine policies, which allows a decision maker to optimally manage and control large-scale newsvendor networks in the presence of uncertain demand without distributional assumptions. These policies are data-driven and model many features of the demand such as correlation and remain robust to parameter misspecification. We present a model that can be generalized to multiproduct settings and extended to multiperiod problems. This is accomplished by modeling the uncertain demand via sets. In this way, it offers a natural framework to study competing policies such as base-stock, affine, and approximative approaches with respect to their profit, sensitivity to parameters and assumptions, and computational scalability. We show that the periodic-affine policies are sustainable—that is, time consistent—because they warrant optimality both within subperiods and over the entire planning horizon. This approach is tractable and free of distributional assumptions, and, hence, suited for real-world applications. We provide efficient algorithms to obtain the optimal periodic-affine policies and demonstrate their advantages on the sales data from one of India's largest pharmacy retailers. This paper was accepted by Yinyu Ye, optimization. [ABSTRACT FROM AUTHOR]