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We consider a two-stage planning problem where a fleet of snowplow trucks is divided among a set of independent regions, each of which then designs routes for efficient snow removal. The central authority wishes to allocate trucks to improve service quality across the regions. Stochasticity is introduced by uncertain weather conditions and unforeseen failures of snowplow trucks. We study two versions of this problem. The first aims to minimize the maximum turnaround time (across all regions) that can be sustained with a user-specified probability. The second seeks to minimize the total expected workload that has not been completed within a user-specified time frame. We develop algorithms that solve these problems effectively and demonstrate their practical value through a case application to snowplowing operations in Utah, obtaining solutions that significantly outperform the allocation currently used in practice. Funding: Financial support from the Utah Department of Transportation [Grant 218138]; the National Science Foundation [Grant CMMI-2112758]; and the Mountain-Plains Consortium [Grant 637] is gratefully acknowledged. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0024. [ABSTRACT FROM AUTHOR]

Gaussian process regression is widely used to model an unknown function on a continuous domain by interpolating a discrete set of observed design points. We develop a theoretical framework for proving new moderate deviations inequalities on different types of error probabilities that arise in GP regression. Two specific examples of broad interest are the probability of falsely ordering pairs of points (incorrectly estimating one point as being better than another) and the tail probability of the estimation error at an arbitrary point. Our inequalities connect these probabilities to the mesh norm, which measures how well the design points fill the space. [ABSTRACT FROM AUTHOR]

Abstract. In a two-tiered city logistics system, an urban logistics company usually partitions the urban area into regions and allocates its delivery fleet (e.g., vehicles, couriers) to these regions. On [...]

In the ranking and selection problem, a sampling budget is allocated among a finite number of designs with the goal of efficiently identifying the best. Allocations of this budget may be static (with no dependence on the random values of the samples) or adaptive (decisions are made based on the results of previous decisions). A popular methodological strategy in the simulation literature is to first characterize optimal static allocations by using large deviations theory to derive a set of optimality conditions, and then to use these conditions to guide the design of adaptive allocations. We propose a new methodology that can be guaranteed to adaptively learn the solution to these optimality conditions in a computationally efficient manner, without any tunable parameters, and under a wide variety of parametric sampling distributions. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2022.4527. [ABSTRACT FROM AUTHOR]

We present a new framework for sequential information collection in applications where regression is used to learn about a set of unknown parameters and alternates with optimization to design new [...]

A sequential information collection problem, where a risk-averse decision maker updates a Bayesian belief about the unknown objective function of a linear program, is used to investigate the informational value [...]

Biopharmaceutical manufacturing is a rapidly growing industry with impact in virtually all branches of medicine. Biomanufacturing processes require close monitoring and control, in the presence of complex bioprocess dynamics with many interdependent factors, as well as extremely limited data due to the high cost of experiments and the novelty of personalized bio-drugs. We develop a new model-based reinforcement learning framework that can achieve human-level control in low-data environments. A dynamic Bayesian network is used to capture causal interdependencies between factors and predict how the effects of different inputs propagate through the pathways of the bioprocess mechanisms. This model is interpretable and enables the design of process control policies that are robust against model risk. We present a computationally efficient, provably convergent stochastic gradient method for optimizing such policies. Validation is conducted on a realistic application with a multidimensional, continuous state variable. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was partially supported by National Institute of Standards and Technology [Grant 70NANB17H002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.1232. [ABSTRACT FROM AUTHOR]