本文精选了运筹学领域国际顶刊《Operations Research》近期发表的论文，提供运筹学领域最新的学术动态。

Error Propagation in Asymptotic Analysis of the Data-Driven (s, S) Inventory Policy

原刊和作者：

Operations Research Volume 73, Issue 1

Xun Zhang （Southern University of Science and Technology）

Zhi-Sheng Ye （National University of Singapore）

William B. Haskell （Purdue University）

Abstract

We study periodic review stochastic inventory control in the data-driven setting where the retailer makes ordering decisions based only on historical demand observations without any knowledge of the probability distribution of the demand. Because an (s, S)-policy is optimal when the demand distribution is known, we investigate the statistical properties of the data-driven (s, S)-policy obtained by recursively computing the empirical cost-to-go functions. This policy is inherently challenging to analyze because the recursion induces propagation of the estimation error backward in time. In this work, we establish the asymptotic properties of this data-driven policy by fully accounting for the error propagation. In this setting, the empirical cost-to-go functions for the estimated parameters are not i.i.d. sums because of the error propagation. Our main methodological innovation comes from an asymptotic representation for multi-sample U-processes in terms of i.i.d. sums. This representation enables us to apply empirical process theory to derive the influence functions of the estimated parameters and to establish joint asymptotic normality. Based on these results, we also propose an entirely data-driven estimator of the optimal expected cost, and we derive its asymptotic distribution. We demonstrate some useful applications of our asymptotic results, including sample size determination and interval estimation.

Link: https://doi.org/10.1287/opre.2020.0568

Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecasting

原刊和作者：

Operations Research Volume 73, Issue 1

Joaquim Dias Garcia （LAMPS）

Alexandre Street （LAMPS）

Tito Homem-de-Mello （Universidad Adolfo Ibáñez）

Francisco D. Muñoz （Generadoras de Chile）

Abstract

Decision making is generally modeled as sequential forecast-decision steps with no feedback, following an open-loop approach. For instance, in the electricity sector, system operators use the forecast-decision approach followed by ad hoc rules to determine reserve requirements and biased net load forecasts to guard the system against renewable generation and demand uncertainty. Such procedures lack technical formalism to minimize operating and reliability costs. We present a new closed-loop framework, named application-driven learning, in which the best forecasting model is defined according to a given application cost function. We consider applications in which the decision-making process is driven by two-stage optimization schemes fed by multivariate point forecasts. We present our estimation method as a bilevel optimization problem and prove convergence to the best estimator regarding the expected application cost. We propose two solution methods: an exact method based on the KKT conditions of the second-level problems and a scalable heuristic suitable for decomposition. Thus, we offer an alternative scientifically grounded approach to current ad hoc procedures implemented in industry practices. We test the proposed methodology with real data and large-scale systems with thousands of buses. Results show that the proposed methodology is scalable and consistently performs better than the standard open-loop approach.

Link: https://doi.org/10.1287/opre.2023.0565

Preventing Price-Mediated Contagion Due to Fire Sales Externalities: Strategic Foundations of Macroprudential Regulation

原刊和作者：

Operations Research Volume 73, Issue 1

Yann Braouezec （IESEG School of Management）

Keyvan Kiani （Emlyon Business School）

Abstract

We offer a stress test framework in which interaction between regulated banks occurs through the impact they may have on asset prices when they deleverage. Because banks are constrained to maintain their risk-based capital ratio higher than a threshold, the deleveraging problem yields a generalized game in which the solvency constraint of each bank depends on the decisions of the others. We analyze the game under microprudential but also under macroprudential regulation. Microprudential regulation corresponds to the standard situation in which each bank considers its own solvency constraint, whereas macroprudential regulation is defined as the situation in which each bank faces a systemic constraint in that it must consider the solvency constraints of all the banks. When bankruptcies can be avoided, we show that a Nash equilibrium generically exists under macroprudential regulation, contagion of failures due to fire sales externalities is prevented, whereas it may not exist under microprudential regulation. We eventually analyze the deleveraging problem when bankruptcies cannot be avoided and present additional results.

Link: https://doi.org/10.1287/opre.2023.0237

Improving the Security of United States Elections with Robust Optimization

原刊和作者：

Operations Research Volume 73, Issue 1

Braden L. Crimmins （University of Michigan）

J. Alex Halderman （University of Michigan）

Bradley Sturt （University of Illinois Chicago）

Abstract

For more than a century, election officials across the United States have inspected voting machines before elections using a procedure called logic and accuracy testing (LAT). This procedure consists of election officials casting a test deck of ballots into each voting machine and confirming the machine produces the expected vote total for each candidate. We bring a scientific perspective to LAT by introducing the first formal approach to designing test decks with rigorous security guarantees. Specifically, our approach employs robust optimization to find test decks that are guaranteed to detect any voting machine misconfiguration that would cause votes to be swapped across candidates. Of all the test decks with this security guarantee, our robust optimization problem yields the test deck with the minimum number of ballots, thereby minimizing implementation costs for election officials. To facilitate deployment at scale, we develop a practically efficient exact algorithm for solving our robust optimization problems based on the cutting plane method. In partnership with the Michigan Bureau of Elections, we retrospectively applied our approach to all 6,928 ballot styles from Michigan’s November 2022 general election; this retrospective study reveals that the test decks with rigorous security guarantees obtained by our approach require, on average, only 1.2% more ballots than current practice. Our approach has since been piloted in real-world elections by the Michigan Bureau of Elections as a low-cost way to improve election security and increase public trust in democratic institutions.

Link: https://doi.org/10.1287/opre.2023.0422

Drone-Delivery Network for Opioid Overdose: Nonlinear Integer Queueing-Optimization Models and Methods

Operations Research Volume 73, Issue 1

Miguel A. Lejeune （George Washington University）

Wenbo Ma （George Washington University）

Abstract

We propose a new stochastic emergency network design model that uses a fleet of drones to quickly deliver naloxone in response to opioid overdoses. The network is represented as a collection of ??/??/?? queueing systems in which the capacity K of each system is a decision variable, and the service time is modeled as a decision-dependent random variable. The model is a queuing-based optimization problem which locates fixed (drone bases) and mobile (drones) servers and determines the drone dispatching decisions and takes the form of a nonlinear integer problem intractable in its original form. We develop an efficient reformulation and algorithmic framework. Our approach reformulates the multiple nonlinearities (fractional, polynomial, exponential, factorial terms) to give a mixed-integer linear programming (MILP) formulation. We demonstrate its generalizability and show that the problem of minimizing the average response time of a collection of ??/??/?? queueing systems with unknown capacity K is always MILP-representable. We design an outer approximation branch-and-cut algorithmic framework that is computationally efficient and scales well. The analysis based on real-life data reveals that drones can in Virginia Beach: (1) decrease the response time by 82%, (2) increase the survival chance by more than 273%, (3) save up to 33 additional lives per year, and (4) provide annually up to 279 additional quality-adjusted life years.

Link: https://doi.org/10.1287/opre.2022.0489