Operations Research

Monitoring in Dynamic Contract Design

Adverse events are harmful to a firm or to the society. In many occasions, better effort in safeguarding a system can reduce the chance of such events. Consider the scenario in which a company (i.e., “principal”) hires a subcontractor (i.e., “agent”) to ...

Consider a setting in which a principal induces effort from an agent to reduce the arrival rate of a Poisson process of adverse events. The effort is costly to the agent and unobservable to the principal unless the principal is monitoring the agent. ...

Improving Ambulance Response Times in Developing Urban Centers

The lack of emergency medical transportation is viewed as the main barrier to the access and availability of emergency medical care in low- and middle-income countries (LMICs). Designing emergency response systems for urban centers in LMICs presents ...

The lack of emergency medical transportation is viewed as the main barrier to the access and availability of emergency medical care in low- and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the ...

Data-Driven Ordering and Pricing

When a new product has just been introduced or the economy has just entered a new phase, a firm is often at a loss as to what the underlying demand pattern has become, let alone how best to respond to it. A natural idea is to manage ordering and pricing ...

We study adaptive policies that handle dynamic inventory and price controls when the random demand for discrete nonperishable items is unknown. Pure inventory control is achieved by targeting newsvendor ordering quantities that correspond to empirical ...

When engaging in the development of new products, the primary objective of start-up companies is to generate a specified return level quickly and with high confidence. Achieving this goal is complicated because of uncertainties in projects’ returns and ...

The periodic selection of new product development (NPD) projects is a crucial operational decision. The main goals of start-up companies in NPD are to attain a reliable return level and deliver this return level fast. Achieving these goals is complicated ...

Will Service Providers Prioritize Under Competition?

Research in service operations management generally considers prioritization beneficial for service providers (SPs), as they can better differentiate their customers. The healthcare, entertainment, restaurant, and airline industries use prioritization in ...

Time is often used as a differentiating factor in several service operations contexts by service providers (SPs) who prioritize their customers. We use a three-stage game to investigate the competition between two SPs providing service with relatively low ...

Air traffic management measures comprise tactical operating procedures to minimize delay costs and strategic scheduling interventions to control overcapacity scheduling. Although interdependent, these problems have been treated in isolation. This paper ...

Adaptive Matching Under Uncertainties.

Two-sided online markets often propose matches based on imperfect knowledge of the characteristics of the two parties to be matched. Such uncertainty may result in inferior matches and may in turn incur negative externalities: a matched resource becomes ...

A matching in a two-sided market often incurs an externality: a matched resource may become unavailable to the other side of the market, at least for a while. This is especially an issue in online platforms involving human experts, as the expert resources ...

Production scheduling is a large-scale optimization problem that must be solved on a yearly basis by every open pit mining project throughout the world. Surprisingly, however, this problem has only recently started to receive much attention from the ...

Given a discretized representation of an ore body known as a block model, the open pit mining production scheduling problem that we consider consists of defining which blocks to extract, when to extract them, and how or whether to process them, in such a ...

Pricing and inventory replenishment are important operations decisions for firms such as retailers. To make these decisions effectively, a firm needs to know the demand distribution and its dependency on selling price, which is usually estimated using ...

A firm makes pricing and inventory replenishment decisions for a product over T periods to maximize its expected total profit. Demand is random and price sensitive, and unsatisfied demands are lost and unobservable (censored demand). The firm knows the ...

Linear contracts and their variants are quite popular in practice, for example, salesforce incentives and chief executive officer compensation. However, agency theory typically stipulates complex contract forms. Yimin Yu and Xiangyin Kong provide an ...

We consider incentive compensation where the firm has ambiguity on the effort-contingent output distribution: The parameters of the output probability distribution are in an ellipsoidal uncertainty set. The firm evaluates any contract by its worst-case ...

A New Algorithm for Static Bin Packing

Static bin packing is the problem of partitioning a set of items (with scalar sizes) into identical bins of a given capacity, under the constraint that the total size of no partition exceeds the bin capacity. In the online variant, the list of items is ...

Bin packing is an algorithmic problem that arises in diverse applications such as remnant inventory systems, shipping logistics, and appointment scheduling. In its simplest variant, a sequence of T items (e.g., orders for raw material, packages for ...

In many sequential decision-making settings where there is uncertainty about the reward of each action, frequent selection of specific actions may reduce expected reward while choosing less frequently selected actions could lead to an increase. These ...

Many settings involve sequential decision making where a set of actions can be chosen at each time step, each action provides a stochastic reward, and the distribution for the reward provided by each action is initially unknown. However, frequent ...

In several scientific and industrial applications, it is desirable to build compact, interpretable learning models where the output depends on a small number of input features. Recent work has shown that such best-subset selection-type problems can be ...

The L₀-regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communities. Recent work has shown that modern ...

Online Learning with Multiperiod Lookaheads

Sequential decision-making problems with high measurement noise usually involve nonconcave value of information, that is, the value of information is not concave in the amount of information collected. In such cases, existing single-period lookahead ...

We consider an optimal learning problem where we are trying to learn a function that is nonlinear in unknown parameters in an online setting. We formulate the problem as a dynamic program, provide the optimality condition using Bellman’s equation, and ...

The need to estimate a function value from sample data at a point that is itself estimated from the same data set arises in many application settings. Such applications include value-at-risk, conditional value-at-risk, and other so-called distortion risk ...

We provide a simple proof of the central limit theorem (CLT) for estimated functions at estimated points. Such estimators arise in a number of different simulation-based computational settings. We illustrate the methodology via applications to quantile ...

Suppose that “risk” does not concern “what” may happen but “when” something may happen. In “Decision making when things are only a matter of time,” Sebastian Ebert analyzes time risk preferences: that is, preferences toward the risk of something happening ...

This article gives a comprehensive treatment of preferences regarding time risk—the risk of something happening sooner or later—within the expected discounted utility model. We characterize the signs of the discount function’s derivatives of all orders ...

The authors extend previous studies of time inconsistency to risk averse (distributionally robust) inventory models and show that time inconsistency is not unique to robust multistage decision making, but may happen for a large class of risk averse/...

In this paper, we investigate optimal policies of distributionally robust (risk averse) inventory models. We demonstrate that if the respective risk measures are not strictly monotone, then there may exist infinitely many optimal policies that are not ...

When moving from the traditional to combinatorial multiarmed bandit setting, addressing the classical exploration versus exploitation trade-off is a challenging task. In “Learning in Combinatorial Optimization: What and How to Explore,” Modaresi, Sauré, ...

We study dynamic decision making under uncertainty when, at each period, a decision maker implements a solution to a combinatorial optimization problem. The objective coefficient vectors of said problem, which are unobserved before implementation, vary ...

From Tree Ensemble Models to Decisions

Predictive models based on ensembles of trees, such as random forests and gradient boosted trees, are widely used in machine learning and data science. In many applications, the features that these models use are controllable and can be regarded as ...

Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on ...