Operations Research

High accuracy in demand prediction allows retailers to effectively manage their inventory and mitigate stock-outs and excess supply. A typical retail setting involves predicting the demand for hundreds of items simultaneously, some with abundant ...

We study how retailers can use data aggregation and clustering to improve demand prediction. High accuracy in demand prediction allows retailers to effectively manage their inventory as well as mitigate stock-outs and excess supply. A typical retail ...

How Reactive Capacity Affects Product Quality and Firm Profitability

The COVID-19 pandemic exacerbates both supply and demand uncertainties, which have strategically influenced firms’ product quality and targeting decisions. For example, during the ...

In many supply chains, the brand-owning retailer designs product quality and decides the retail price but often outsources its production to suppliers. For products with a short selling season, low reactive capacity in the supply chain requires the ...

One of the mantras of risk measurement is the avoidance of risk concentration. However, most formal approaches to the topic actually require more than this. In “Star-Shaped Risk Measures,” Castagnoli, Cattelan, Maccheroni, Tebaldi, and Wang study this ...

In this paper, monetary risk measures that are positively superhomogeneous, called star-shaped risk measures, are characterized and their properties are studied. The measures in this class, which arise when the subadditivity property of coherent risk ...

Motivated by logistical problems faced by a large supply chain software company, the paper, “A New Approach for Vehicle Routing with Stochastic Demand: Combining Route Assignment with Process Flexibility,” studies a vehicle routing problem where some ...

We propose a new approach for the vehicle routing problem with stochastic customer demands revealed before vehicles are dispatched. We combine ideas from vehicle routing and manufacturing process flexibility to propose overlapped routing strategies with ...

The paper “An Optimal Control Framework for Online Job Scheduling with General Cost Functions,” by Etesami devises online speed-augmented competitive algorithms for minimizing the generalized completion time on a single and multiple unrelated machines for ...

We consider the problem of online job scheduling on a single machine or multiple unrelated machines with general job and machine-dependent cost functions. In this model, each job has a processing requirement and arrives with a nonnegative nondecreasing ...

Endogenous Inverse Demand Functions

Buying or selling assets in a financial market impacts the prices upward or downward. Quantifying these price impacts is fundamental to many problems within finance (e.g., optimal liquidation and systemic risk). In “...

In this work we present an equilibrium formulation for price impacts. This is motivated by the Bühlmann equilibrium in which assets are sold into a system of market participants, for example, a fire sale in systemic risk, and can be viewed as a ...

Station-based bike-sharing systems have changed the urban landscape of major American cities including New York City, Boston, Washington DC, and Chicago. In these systems, stations are capacitated: a station’s capacity is given by the number of docks it ...

The growing popularity of bike-sharing systems around the world has motivated recent attention to models and algorithms for their effective operation. Most of this literature focuses on their daily operation for managing asymmetric demand. In this work, ...

How should a firm structure its concept testing processes when testing efforts must be delegated to self-interested testing agents? In “Delegated Concept Testing in New Product Development,” J. Schlapp and G. Schumacher analyze different configurations of ...

Testing a large variety of different product concepts is an integral part of nearly all new product development initiatives—especially in the concept selection phase, where firms seek to identify the most promising concept for further development. Test ...

Individualized Patient Monitoring Under Alarm Fatigue

Hospitals are rife with alarms, many of which are false. This leads to alarm fatigue, in which clinicians become desensitized and may inadvertently ignore real threats. “Individualized Dynamic Patient ...

Hospitals are rife with alarms, many of which are false. This leads to alarm fatigue, in which clinicians become desensitized and may inadvertently ignore real threats. We develop a partially observable Markov decision process model for recommending ...

A vast part of the Internet economy is powered by advertising, much of which is sold at auction. A key question for sellers is how to optimize the auction mechanism they use. Bidders, conversely, try to optimize their bidding strategy. Incentive ...

We address the problem of improving bidders’ strategies in prior-dependent revenue-maximizing auctions and introduce a simple and generic method to design novel bidding strategies whenever the seller uses past bids to optimize her mechanism. We propose a ...

Two-sided platforms, which enable two distinct groups of agents to match and transact with each other, are now ubiquitous in a variety of markets, such as those for labor, dating, and accommodation rentals. Many such online platforms, rather than ...

Two-sided matching platforms provide users with menus of match recommendations. To maximize the number of realized matches between the two sides (referred to herein as customers and suppliers), the platform must balance the inherent tension between ...

Allocating scarce resources with (weak) priority and complex constraints has many applications ranging from course allocation, and healthcare rationing to refugee resettlement. Its generality, however, mostly leads to impossibility results. We offer a ...

We consider a resource allocation problem that combines three general features: complex resource constraints, weak priority rankings over the agents, and ordinal preferences over bundles of resources. We develop a mechanism based on a new concept called ...

This paper proposes a carefully crafted dynamic programming approach for capacitated assortment optimization under the nested logit model in its utmost generality, potentially including partially captured nests and possibly synergistic products. ...

The main contribution of this paper resides in proposing a carefully crafted dynamic programming approach for capacitated assortment optimization under the nested logit model in its utmost generality. Specifically, we show that the optimal revenue can be ...

A Novel and Promising Approximation for Network Revenue Management

In “Product-Based Approximate Linear Programs for Network Revenue Management,” Zhang, Samiedaluie, and Zhang propose a novel separable piecewise linear (SPL) approximation for the network ...

The approximate linear programming approach has received significant attention in the network revenue management literature. A popular approximation in the existing literature is separable piecewise linear (SPL) approximation, which estimates the value of ...

In “Technical Note—Ranking Distributions When Only Means and Variances Are Known,” Müller, Scarsini, Tsetlin, and Winkler address the question of ranking distributions when only the first two moments—that is, means and variances—are known. This is ...

Consider a choice between two random variables, for which only means and variances are known. Is it possible to rank them by putting some constraints on risk preferences? We provide such a ranking by bounding how much marginal utility can change. Such ...

A defender dispatches patrollers to circumambulate a perimeter to guard against potential attacks. The defender decides on the time points to dispatch patrollers and each patroller’s direction and speed, as long as the long-run rate at which patrollers ...

We study the problem of finding the Löwner–John ellipsoid (i.e., an ellipsoid with minimum volume that contains a given convex set). We reformulate the problem as a generalized copositive program and use that reformulation to derive tractable semidefinite ...

A New Family of Valid-Inequalities for Dantzig-Wolfe Reformulation of Mixed Integer Linear Programs

In “Consistency Cuts for Dantzig-Wolfe Reformulation,” Jens Vinther Clausen, Richard Lusby, and Stefan Ropke present a new family of valid inequalities to ...

This paper introduces a family of valid inequalities, which we term consistency cuts, to be applied to a Dantzig-Wolfe reformulation (or decomposition) with linking variables. We prove that these cuts ensure an integer solution to the corresponding ...

Although the stochastic optimization paradigm exploits probability theory to optimize the tradeoff between risk and returns, robust optimization has gained significant popularity by reducing computation requirements through the optimization of the worst-...

This paper explores the idea that two-stage worst-case regret minimization problems with either objective or right-hand side uncertainty can be reformulated as two-stage robust optimization problems and can therefore benefit from the solution schemes and ...

Nonconvex Stochastic Optimization

Nonconvex stochastic optimization problems arise in many machine learning problems, including deep learning. The stochastic gradient Hamiltonian Monte Carlo (SGHMC) is a variant of stochastic gradients with a momentum ...

Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is a variant of stochastic gradients with momentum where a controlled and properly scaled Gaussian noise is added to the stochastic gradients to steer the iterates toward a global minimum. Many works ...

We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are independently and identically drawn from an unknown ...

An Exponentially Faster Algorithm for Submodular Maximization Under a Matroid Constraint

This paper studies the problem of submodular maximization under a matroid constraint. It is known since the 1970s that the greedy algorithm obtains a constant-factor ...

In this paper, we study submodular maximization under a matroid constraint in the adaptive complexity model. This model was recently introduced in the context of submodular optimization to quantify the information theoretic complexity of black-box ...

New ventures go through multiple stages: In the early stage, there is a business concept and preliminary evidence supporting the concept. In later stages, there are revenues and sales. In each stage, there are usually ...

We model the creation of a new venture with a novel drift-variance diffusion control framework in which the state of the venture is captured by a diffusion process. The entrepreneur creating the venture chooses costly controls, which determine both the ...

Many organizations combine forecasts of probabilities of binary events to support critical business decisions, such as the approval of credit or the recommendation of a drug. To aggregate individual probabilities, we offer a new method based on Bayesian ...

Probability forecasts of binary events are often gathered from multiple models or experts and averaged to provide inputs regarding uncertainty in important decision-making problems. Averages of well-calibrated probabilities are underconfident, and methods ...

Allocating Resources Across Systems Coupled by Shared Information

Many sequential decision problems involve repeatedly allocating a limited resource across subsystems that are jointly affected by randomly evolving exogenous factors. For example, in ...

We consider a sequential decision problem involving shared resources and signals in which a decision maker repeatedly observes some exogenous information (the signal), modeled as a finite-state Markov process, then allocates a limited amount of a shared ...