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Research journals in the Physical, life and social sciences have a linear relationship between the average number of references and the normalized paper lengths. After, reviewing 18 journals, the difference between the papers average was twice the number of references.

Abstract. In this paper, an analytical approach to National Football League (NFL) survival pools is investigated. This paper introduces into the literature NFL survival pools and presents optimization models for [...]

Managed print service (MPS) is a type of information technology infrastructure service that provides centralized management of companies' printing device fleets. In this paper, we estimate the provider's risk preference in MPS using a proprietary data set from Xerox Corporation. We adopt a structural approach in our empirical analysis by modeling the contracting and usage processes of MPS as a two-stage screening game and building econometric models based on the equilibrium contracts and print volumes. Our econometric models have a unique hierarchical structure that allows clustering of printers with the same contracts in the same company, thereby capturing the B2B nature of MPS. We find that Xerox exhibits risk aversion in MPS contracting and provide institutional details of Xerox's commission holdback policy that may cause the observed risk aversion. In the counterfactual analysis, we demonstrate the significance of the provider's risk aversion and the implications of the commission holdback policy on equilibrium contracts, the expected earnings of Xerox and customer companies, and their preferences for printer models. Funding: This project was funded by a Xerox Foundation grant. Supplemental Material: The E-companion is available at https://doi.org/10.1287/opre.2017.1673. Keywords: risk-aversion * structural estimation * servicizing * empirical OM * contracting, 1. Introduction Modeling contracting parties' risk preferences is crucial in various business-to-business (B2B) and business-to-consumer (B2C) settings. Risk preferences naturally shape theoretical models, their analysis, and managerial insights. However, although [...]

In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time. Neither earliness nor tardiness is desirable for customer and freight forwarders. In this paper, we investigate ways to assess and forecast transport risks using a half-year of air cargo data, provided by a leading forwarder on 1,336 routes served by 20 airlines. Interestingly, our preliminary data analysis shows a strong multimodal feature in the transport risks, driven by unobserved events, such as cargo missing flights. To accommodate this feature, we introduce a Bayesian nonparametric model--the probit stick-breaking process mixture model--for flexible estimation of the conditional (i.e., state-dependent) density function of transport risk. We demonstrate that using alternative methods can lead to misleading inferences. Our model provides a tool for the forwarder to offer customized price and service quotes. It can also generate baseline airline performance to enable fair supplier evaluation. Furthermore, the method allows us to separate recurrent risks from disruption risks. This is important, because hedging strategies for these two kinds of risks are often drastically different. Funding: This research was supported, in part, by the National Natural Science Foundation of China [Grant 71390331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2017.1612. Keywords: Bayesian statistics * big data * disruptions and risks * empirical * international air cargo logistics * nonparametric * probit stick-breaking mixture model, 1. Introduction Global trade has grown considerably in recent decades; many companies now have overseas facilities and supply chain partners. International cargo logistics management thus plays an increasingly important role [...]

Abstract. In this paper, we study price optimization with price-demand relationships captured by the paired combinatorial logit (PCL) model, which overcomes restrictions of the well-studied multinomial logit (MNL) and nested [...]

Value at Risk (VaR) has emerged in recent years as a standard tool to measure and control the risk of trading portfolios. Yet, existing theoretical analysis of the optimal behavior of a trader subject to VaR limits has produced a negative view of VaR as a risk-control tool. In particular, VaR limits have been found to induce increased risk exposure in some states and an increased probability of extreme losses. However, these conclusions are based on models that are either static or dynamically inconsistent. In this paper, we formulate a dynamically consistent model of optimal portfolio choice subject to VaR limits and show that the concerns expressed in earlier papers do not apply if, consistently with common practice, the VaR limit is reevaluated dynamically. In particular, we find that the optimal risk exposure of a trader subject to a VaR limit is always lower than that of an unconstrained trader and that the probability of extreme losses is also lower. We also consider risk limits formulated in terms of tail conditional expectation (TCE), a coherent risk measure often advocated as an alternative to VaR, and show that in our dynamic setting it is always possible to transform a TCE limit into an equivalent VaR limit, and conversely. Subject classifications: investment; management; portfolio. Area of review: Finance., 1. Introduction Investment firms customarily limit the discretionality of their traders by imposing limits on the risk of trading portfolios. Because Value at Risk (VaR) has gained in recent years [...]

The multiple vehicle routing problem with time windows (VRPTW) is a hard and extensively studied combinatorial optimization problem. This paper considers a dynamic VRPTW with stochastic customers, where the goal [...]

In today's business environment, unpredictable economic and noneconomic forces can affect firms' operational costs and discount factors, as well as demand. In this paper, we incorporate these uncertainties into a [...]

Theoretical studies of process flexibility designs have mostly focused on expected sales. In this paper, we take a different approach by studying process flexibility designs from the worst-case point of [...]

We study a periodically reviewed multiechelon inventory system in series such that order quantities at every stage have to be multiples of a given stage-specific batch size. The batch sizes [...]

This paper describes a methodology for setting long-term care capacity levels over a multiyear planning horizon to achieve target wait time service levels. Our approach integrates demographic and survival analysis, [...]

Delay announcements informing customers about anticipated service delays are prevalent in service-oriented systems. How-delay announcements can influence customers in service systems is a complex problem that depends on both the [...]

We consider a model where a finite number of producers compete to meet an infinitely divisible but inelastic demand for a product. Each firm is characterized by a production cost that is convex in the output produced, and firms act as profit maximizers. We consider a uniform price market design that uses supply function bidding: firms declare the amount they would supply at any positive price, and a single price is chosen to clear the market. We are interested in evaluating the impact of price-anticipating behavior both on the allocative efficiency of the market and on the prices seen at equilibrium. We show that by restricting the strategy space of the firms to parameterized supply functions, we can provide upper bounds on both the inflation of aggregate cost at the Nash equilibrium relative to the socially optimal level, as well as the markup of the Nash equilibrium price above the competitive level: as long as N > 2 firms are competing, these quantities are both upper bounded by 1 + 1/(N - 2). This result holds even in the presence of asymmetric cost structure across firms. We also discuss several extensions, generalizations, and related issues. Subject classifications: supply function equilibrium; resource allocation; efficiency loss. Area of review: Revenue Management. History: Received October 2008; revisions received June 2009, November 2009; accepted January 2010., 1. Introduction We consider a model where a finite number of producers compete to meet an infinitely divisible, inelastic demand for a product. Each firm is characterized by a production [...]

In this paper, we examine a supply chain in which a single supplier sells to a downstream retailer. We consider a multiperiod model with the following sequence of events. In [...]

In this paper, we propose a simple heuristic approach for the inventory control problem with stochastic demand and multiplicative random yield. Our heuristic tries to find the best candidate within [...]

It is of crucial importance to develop risk-averse models for multicriteria decision making under uncertainty. A major stream of the related literature studies optimization problems that feature multivariate stochastic benchmarking constraints. These problems typically involve a univariate stochastic preference relation, often based on stochastic dominance or a coherent risk measure such as conditional value-at-risk, which is then extended to allow the comparison of random vectors by the use of a family of scalarization functions: All scalarized versions of the vector of the uncertain outcomes of a decision are required to be preferable to the corresponding scalarizations of the benchmark outcomes. While this line of research has been dedicated almost entirely to linear scalarizations, the corresponding deterministic literature uses a wide variety of scalarization functions that, among other advantages, offer a high degree of modeling flexibility. In this paper we aim to incorporate these scalarizations into a stochastic context by introducing the general class of min-biaffine functions. We study optimization problems in finite probability spaces with multivariate stochastic benchmarking constraints based on min-biaffine scalarizations. We develop duality results, optimality conditions, and a cut generation method to solve these problems. We also introduce a new characterization of the risk envelope of a coherent risk measure in terms of its Kusuoka representation as a tool toward proving the finite convergence of our solution method. The main computational challenge lies in solving cut generation subproblems; we develop several mixed-integer programming formulations by exploiting the min-affine structure and leveraging recent advances for solving similar problems with linear scalarizations. We conduct a computational study on a well-known homeland security budget allocation problem to examine the impact of the proposed scalarizations on optimal solutions, and illustrate the computational performance of our solution methods. Funding: Both authors acknowledge the support from Bilim Akademisi--The Science Academy, Turkey, under the BAGEP program. The first author has also been supported in part by The Scientific and Technological Research Council of Turkey (TUBITAK) [Grant #115M560]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2017.1671. Keywords: stochastic programming * multicriteria * multivariate risk * coherent risk measures * conditional value-at-risk * stochastic dominance * cut generation, 1. Introduction Multicriteria stochastic optimization problems, which involve decisions leading to uncertain outcomes that can be evaluated according to multiple stochastic performance measures of interest, find natural applications in a [...]

We study the value of dynamic pricing to maximize revenues in queueing systems with price- and delay-sensitive customers. The system queue length is visible so that upon arrival, customers decide to join the system based on the congestion and the price at that time. We analyze this problem in the asymptotic regime of large customer market size and capacity. We find that dynamic pricing performs significantly better than static pricing at mitigating the effect of uncertainty. Asymptotically, the revenue in such systems consists of a positive deterministic component and a negative stochastic component, the latter representing the impact of variability. Static pricing leads to the [n.sup.1/2]-scale effect of variability, i.e., the expected steady-state queue length is [Kn.sup.1/2] for some K > 0, where n represents the system size. However, dynamic pricing can lower this effect of variability to the [n.sup.1/3]-scale. We further show that a simple policy of using only two prices can achieve most of the benefits of dynamic pricing. We also discuss how our results can apply to other dynamic control problems in queueing systems. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2017.1668. Keywords: revenue management * dynamic pricing * queueing * asymptotic analysis * diffusion analysis, 1. Introduction Time is an important attribute of many products and services with customers valuing quick access at a premium. Given that the ability of a firm to provide quick [...]

The central issue in supply chain management is to match supply with demand, and the heart of a planning model is the modeling of supply and demand functions. To allow for analytical tractability, the existing literature often assumes almost surely linear supply and demand functions, which greatly limits the applicability of the models. The goal of this paper is to provide a unified approach to analyze general random supply and demand functions. By transforming the problem into one defined on a higher dimension, we show that many of the seemingly highly nonlinear supply and demand functions (in the almost sure sense) are linear in the stochastic sense. With this new notion of linearity, called stochastic linearity in midpoint, our ability to analyze supply chain problems is much enhanced. We are able to prove the concavity of the profit function in the transformed supply and demand decisions for a general class of supply and demand functions that include, but are not restricted to, the ones studied in the existing literature. Thus, many of the challenging problems now become tractable. Moreover, we characterize a set of easy-to-verify conditions for stochastic linearity in midpoint. Funding: Q. Feng's research is partly supported by 2013-2015 Purdue Office of the Vice President for Research (OVRP) funding and National Natural Science Foundation of China [NSFC-71471107 and 71431004]. J. G. Shanthikumar's research is partly supported by 2013-2015 Purdue OVRP funding. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2017.1648. Keywords: stochastic linearity * concavity * dispersive order * supply functions * demand functions * quantile regression, 1. Introduction Matching supply with demand is the central theme of supply chain management. This issue has intrigued practitioners as well as researchers for decades. The uncertainties involved in supply [...]

Often, network users are not perfectly rational, especially when they are satisficing--rather than optimizing--decision makers and each individual's perception of the decision environment reflects personal preferences or perception errors due to lack of information. While the assumption of satisficing drivers has been used in modeling route choice behavior, this research uses a link-based perception error model to describe driver's uncertain behavior, without assuming stochasticity. In congestion-free networks, we show that the perception error model is more general than the existing bounded rationality models with satisficing drivers with special cases when the two approaches yield the same results; that is, satisficing under accurate perception is equivalent to optimizing under inaccurate perception. This motivates us to define generalized bounded rationality in route choice behavior modeling. The proposed modeling framework is general enough to capture link-specific cost-perception of drivers. We use a Monte Carlo method to estimate modeling parameter values to guarantee a certain coverage probability in comparison with the random utility model. We demonstrate how the notion of generalized bounded rationality can be used in robust multicommodity network design problems and devise a cutting plane algorithm. We illustrate our approaches in the context of hazardous materials transportation. Funding: This research was partially supported by the National Science Foundation [Grant CMMI-1558359]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/opre.2017.1621. Keywords: bounded rationality * satisficing * perception * network design * robust optimization * inverse optimization, 1. Introduction An economic, or (perfectly) rational, person needs to have a clear system of preferences and knowledge of the decision environment (Simon 1955). This rationality assumption has been widely [...]

We investigate protein purification operations conducted by biomanufacturers and pharmaceutical companies as part of their research and development efforts. Purification of these proteins involves unique challenges such as balancing the yield and purity trade-offs, dealing with uncertainty in the starting material, and estimating the impact of several interlinked decisions. We develop a Markov decision model and partition the state space into decision zones that provide managerial insights to optimize purification operations. We develop practical guidelines to quantify financial risks, and we characterize the optimal operating decisions based on specific production requirements. The optimization framework has been implemented at Aldevron, a contract biomanufacturer specializing in proteins, and has resulted in 25% reduction in the total lead times and 20% reduction in the costs of protein purification operations on average. Funding: This research was conducted due to the generous support provided by the National Science Foundation under grant CMMI 1334933, and the Marie Sklodowska-Curie Individual Fellowship provided by the European Commission under the Horizon 2020 program. Keywords: stochastic control * reachability of target set * guaranteed performance * biomanufacturing, 1. Introduction Recent advances in biomanufacturing have led to novel proteins used in the treatment of cardiovascular diseases, autoimmune disorders, and cancer. In this paper, we focus on protein manufacturing [...]

We analyze the behavior of a competitive n-tier supply chain system, where agents bargain with each other locally. We study the influence of transaction costs on the convergence of the system to a stationary outcome. In particular, we consider a dynamic bargaining game among a finite set of agents and its replications, and use a limit stationary equilibrium to examine the system's behavior as the population's size goes to infinity. The convergence of the system to a limit stationary equilibrium would capture the common belief that as the market gets large, it converges to a competitive and stable outcome. However, we prove that depending on the underlying transaction costs, the system might not converge to such an equilibrium. Interestingly, our result shows that a small increase in the transaction cost at one end of the chain can be greatly amplified and shifts the system away from the steady state. When a limit stationary equilibrium exists, we show that it is unique. We use such an equilibrium outcome to study trade surplus among sellers, buyers, and middlemen. Funding: The author's work was partly supported by the National Science Foundation [Grant AST-1443965]. Keywords: noncooperative bargaining * incentives * supply chains, 1. Introduction Multiple tiers of intermediaries are common in several economic environments, ranging from the supply chains of agricultural products to the global financial markets. In the global coffee supply [...]

In 1964, Kleinrock proposed a queueing discipline for a single-server queue in which customers from different classes accumulate priority as linear functions of their waiting time. At the instant that a server becomes free, it selects the waiting customer with the highest accumulated priority, provided that the queue is nonempty. He developed a recursion for calculating the expected waiting time for each class. In 2014, Stanford, Taylor, and Ziedins reconsidered this queue, which they termed the accumulating priority queue (APQ), and derived the waiting time distribution for each class. Kleinrock and Finkelstein in 1967 also studied an accumulating priority system in which customers' priorities increase as a power-law function of their waiting time. They established that it is possible to associate a particular linear APQ with such a power-law APQ, so that the expected waiting times of customers from all classes are preserved. In this paper, we extend their analysis to characterise the class of nonlinear APQs for which an equivalent linear APQ can be found, in the sense that, for identical sample paths of the arrival and service processes, the ordering of all customers is identical at all times in both the linear and nonlinear systems. Funding: The work of the first and second author was supported by the latter's NSERC Discovery [Grant 41187-2009]. The third author would like to thank the Australian Research Council for supporting his work through Laureate Fellowship FL130100039 and the ARC Centre of Excellence for Mathematical and Statistical Frontiers. The fourth author's work was funded by a grant from the Science FRDF at the University of Auckland and Te Punaha Matatini. Keywords: key performance indicator * linear proxy * waiting times, 1. Introduction Long waiting times for various groups of customers have been a recurring problem in many public systems, particularly in healthcare systems. Among the different techniques used to manage [...]

Abstract. We study a multiserver model with n flexible servers and n queues, connected through a bipartite graph, where the level of flexibility is captured by an upper bound on [...]

Abstract. Many seasonal products are transported via ocean carriers from origin to destination markets. The shipments arriving earlier in the market may sell at higher prices, but faster shipping services [...]

This paper studies a one-shot inventory replenishment problem with dynamic pricing. The customer arrival rate is assumed to follow a geometric Brownian motion. Homogeneous customers have an isoelastic demand function [...]

Motivated by the recent adoption of tactical pricing strategies in manufacturing settings, this paper studies a problem of dynamic pricing for a multiproduct make-to-order system. Specifically, for a multiclass [M.sub.n]/M/1 [...]

This paper analyzes a call center model with m customer classes and r agent pools. The model is one with doubly stochastic arrivals, which means that the m-vector λ of [...]

This paper, motivated by the experiences of a major U.S.-based broadcast television network, presents algorithms and heuristics to schedule commercial videotapes. Major advertisers purchase several slots to air commercials during [...]

This paper presents a model for computing the parameters of an integrated inventory replenishment and outbound dispatch scheduling policy under dynamic demand considerations. The optimal policy parameters specify (i) how [...]

This paper integrates pricing and replenishment decisions for the following prototypical two-echelon distribution system with deterministic demands. A supplier distributes a single product to multiple retailers, who in turn sell [...]

The paper considers the single vehicle routing problem with stochastic demands. While most of the literature has studied the a priori solution approach, this work focuses on computing a reoptimization-type [...]

The paper considers the optimal control of a single-item continuous-review inventory system with random demand and discount opportunities. Items can always be purchased with the regular order setup and variable [...]

This paper studies the competitive and cooperative selection of inventory policies in a two-echelon supply chain with one supplier and N retailers. Stochastic demand is monitored continuously. Retailers incur inventory [...]

We study flexible processing systems where there are multiple paths for a certain type of customer to follow through the system. This paper analyzes an open processing network model with [...]

This paper considers a multistage serial inventory system with Markov-modulated demand. Random demand arises at Stage 1, Stage 1 orders from Stage 2, etc., and Stage N orders from an [...]

We show that partial versus full multilateral netting of interbank liabilities increases bank shortfall and reduces clearing asset price and aggregate bank surplus. We also show that partial multilateral netting [...]

Financial institutions are interconnected directly by holding debt claims against each other (the network channel), and they are also bound by the market when selling assets to raise cash in [...]

By exploiting basic common practice accounting and risk-management rules, we propose a simple analytical dynamical model to investigate the effects of microprudential changes on macroprudential outcomes. Specifically, we study the [...]

Sustainable and efficient management of a distribution system requires coordination between transportation planning and inventory control decisions. In this context, we consider a one warehouse multi-retailer inventory system with a [...]

In the paper "A General Attraction Model and Sales-based Linear Program for Network Revenue Management under Customer Choice," G. Gallego, R. Ratliff, and S. Shebalov present new customer choice and [...]

This paper addresses two concerns with the state of the art in network revenue management with dependent demands. The first concern is that the basic attraction model (BAM), of which [...]

We consider the problem of efficiently allocating the capacity of a number of service facilities (prone to congestion) to a set of users with private information regarding their willingness to [...]

In revenue management research and practice, demand models are used that describe how demand for a seller's products depends on the decisions, such as prices, of that seller. Even in [...]

Debris management is one of the most time consuming and complicated activities among post-disaster operations. Debris clearance is aimed at pushing the debris to the sides of the roads so [...]

We consider customer service chat (CSC) systems where customers can receive real time service from agents using an instant messaging (IM) application over the Internet. A unique feature of these [...]

This paper studies a sequential resource allocation problem motivated by distribution operations of a nonprofit organization. The alternate objectives that arise in nonprofit (as opposed to commercial) operations lead to [...]

In a novel multiplayer extension of the famous secretary problem, multiple players seek to employ secretaries from a common labour pool. Secretaries do not accept being put on hold, always [...]

Fast-fashion retailers such as Zara offer continuously changing assortments and use minimal in-season promotions. Their clearance pricing problem is thus challenging because it involves comparatively more different articles of unsold [...]

It is common for rewards to be given on the basis of a rank ordering, so that relative performance amongst a cohort is the criterion. In this paper we formulate [...]

The bullwhip effect, or demand information distortion, has been a subject of both theoretical and empirical studies in the operations management literature. In this paper, we develop a simple set [...]