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This paper describes a decision support system for paper production scheduling. This is the first system to provide an integrated solution to paper production scheduling and to consider interactions between [...]

A common problem encountered in paper-production facilities is that of allocating customer orders to machines so as to minimize the total cost of production. It can be formulated as a [...]

The aim of this note is to correct an error in the formulation of Theorem 1 by Savin and Terwiesch [Savin, S., C. Terwiesch. 2005. Optimal product launch times in [...]

Introduction Operations Research is one of the flagship journals of our profession, and it has a long and distinguished history of excellence. In recent years, the journal has enjoyed significant [...]

Tenure decisions, key decisions in academic institutions, are primarily based on subjective assessments of candidates. Using a large-scale bibliometric database containing 198,310 papers published 1975-2012 in the field of operations [...]

With this issue of Operations Research, the second term of the editorial board is coming to an end. This is an opportunity to evaluate the reputation, stature, and health of [...]

With the economic recession in full swing, Operations Research, both the profession and the journal, can and should play a role in the recovery process. Indeed, the impact of the [...]

The Decision Analysis area will publish papers that extend, unify, or creatively apply the concepts of decision analysis to enrich our understanding of how to make better decisions. Such papers [...]

Since the early days of OR, optimization has been one of the foundations of this discipline. Over the years, optimization models and methods have grown in their ability to handle [...]

This issue marks the publication of the first paper in the newly established area entitled OR Forum along with the associated expert comments that appear in the new online Discussion [...]

Humans interact in many different ways. When one models individuals or groups of people as nodes in a network with edges or arcs representing specific types of interaction between them, [...]

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 [...]

Subject classifications: professional: journal policies; OR/MS history; editorial comments., The second anniversary of the current editorial board is an opportunity to reflect on many of the initiatives implemented by the board in the last two years. It is also [...]

Comment on "An Algorithmic Approach to Linear Regression" by Dimitris Bertsimas and Angela King. Subject classifications: programming: integer: applications; statistics. In this issue of Operations Research we have chosen to [...]

The Military area has been expanded to include papers on homeland security and related problems. Accordingly, the name of this area has been changed to Military and Homeland Security (M [...]

The OR Practice area publishes papers that report on outstanding applications of operations research to real problems. The intent of the area is to attract and highlight innovative examples of [...]

Comment on "Tenure Analytics: Models for Predicting Research Impact" by Dimitris Bertsimas, Erik Brynjolfsson, Shachar Reichman, and John Silberholz. Subject classifications: statistics: data analysis; information systems: decision support systems; forecasting. [...]

Subject classification: OR history. In 1961, Operations Research published a short, five-page article by John D. C. Little, then of Case Institute of Technology (now Case Western Reserve University), entitled [...]

This note is concerned with the inventory models with finite horizon and cost changes. We presented more specific results on the optimal inventory policy than those in previous papers and [...]

We address the problem of risk sharing among agents using a two-parameter class of quantile-based risk measures, the so-called range-value-at-risk (RVaR), as their preferences. The family of RVaR includes the value-at-risk (VaR) and the expected shortfall (ES), the two popular and competing regulatory risk measures, as special cases. We first establish an inequality for RVaR-based risk aggregation, showing that RVaR satisfies a special form of subadditivity. Then, the Pareto-optimal risk sharing problem is solved through explicit construction. To study risk sharing in a competitive market, an Arrow-Debreu equilibrium is established for some simple yet natural settings. Furthermore, we investigate the problem of model uncertainty in risk sharing and show that, in general, a robust optimal allocation exists if and only if none of the underlying risk measures is a VaR. Practical implications of our main results for risk management and policy makers are discussed, and several novel advantages of ES over VaR from the perspective of a regulator are thereby revealed. Funding: R. Wang would like to thank Forschungsinstitut fur Mathematik at ETH Zurich for supporting his visits in 2015 and 2016, and he acknowledges financial support from the Natural Sciences and Engineering Research Council of Canada [RGPIN-2018-03823, RGPAS-2018-522590]. P. Embrechts would like to thank the Swiss Finance Institute for financial support. H. Liu acknowledges financial support from the University of Waterloo and the China Scholarship Council during her Ph.D. study at the University of Waterloo. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2017.1716. Keywords: value-at-risk * expected shortfall * risk sharing * regulatory capital * robustness * Arrow-Debreu equilibrium, 1. Introduction 1.1. Risk Sharing Problems and Quantile-Based Risk Measures A risk sharing problem concerns the redistribution of a total risk among multiple participants. In this paper, we address collaborative [...]

We study a class of problems with both binary selection decisions and associated continuous choices that result in stochastic rewards and costs. The rewards are received based on the decision maker's selection, and the costs depend both on the decisions and realizations of the stochastic variables. We consider a family of risk-based objective functions that contains the traditional risk-neutral expected-value objective as a special case. A combination of rounding and sample average approximation is used to produce solutions that are guaranteed to be close to the optimal solution with high probability. We also provide an empirical comparison of the performance of the algorithms on a set of randomly generated instances of a supply chain example problem. The computational results illustrate the theoretical claims in the paper that, for this problem, high-quality solutions can be found with small computational effort. Funding: This research was conducted with government support under and awarded by a Department of Defense (DoD), Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a. Keywords: two-stage stochastic optimization * selection * resource allocation * approximation algorithms, 1. Introduction In this paper we study a class of two-stage stochastic selection problems with recourse and develop approximation algorithms to efficiently solve them. In particular, a subset of options [...]

In this paper, we study the relationship between several well known classes of discrete choice models, i.e., the random utility model (RUM), the representative agent model (RAM), and the semiparametric choice model (SCM). Using a welfare-based model as an intermediate, we show that the RAM and the SCM are equivalent. Furthermore, we show that both models as well as the welfare-based model strictly subsume the RUM when there are three or more alternatives, while the four are equivalent when there are only two alternatives. Thus, this paper presents a complete picture of the relationship between these choice models. Funding: The research of the third author is supported by the National Science Foundation [Grant CMMI-1462676]. Keywords: welfare function * random utility model * representative agent model * semiparametric choice model, 1. Introduction In this paper, we study the discrete choice models. Discrete choice models are used to model choices made by people among a finite set of alternatives. As examples, [...]

How to minimize the expected costs for managing a waiting line of customers when the information about their necessary service time and urgency is only known to the customers themselves? [...]

This article recalls some of George B. Dantzig's many contributions to operations research and the management sciences in his ninety-year lifetime. Subject classifications: professional: OR/MS history, obituaries. Area of review: [...]

The OR Forum area invites work that challenges the reader to consider and evaluate the status of past, present, or future prospects and challenges within the field of operations research. [...]

The Decision Analysis (DA) area will publish papers that extend, unify, or creatively apply the concepts of decision analysis to enrich our understanding of how to make better decisions. Submitted [...]

There has been a revival of interest in traffic flow theory in the late 1990s, mostly because vehicle detectors have been installed at many highway locations throughout the world to [...]

The Revenue Management area encompasses all work related to operational pricing and demand management, broadly defined. This includes traditional problems in the field such as capacity allocation, overbooking, and dynamic [...]

The Transportation area of Operations Research seeks papers that take a scientific approach to the analysis of transportation systems. All modes of transportation are of interest as are all aspects [...]

The Policy Modeling and Public Sector OR area seeks papers that creatively apply operations research methods to important policy problems in the public sector. Especially welcomed are manuscripts that utilize [...]

An important problem in the theory of dynamic programming is that of characterizing sufficient conditions under which the optimal policies for Markov decision processes (MDPs) under the infinite-horizon discounted cost [...]

Comment on "Design of Risk Weights" by Paul Glasserman and Wanmo Kang. Subject classifications: finance; banks; quadratic programming. In this November-December 2014 issue of Operations Research, we have chosen to [...]

As various firms initially make information and access to their products/services scarce within a social network, identifying influential players emerges as a pivotal step for their success. In this paper, we tackle this problem using a stylized model that features payoff externalities and local network effects. The network designer is allowed to release information to only a subset of players (leaders); these targeted players make their contributions first and the rest (followers) move subsequently after observing the leaders' decisions. In the presence of incomplete information, the signaling incentive drives the optimal selection of leaders and can lead to a first-order materialistic effect on equilibrium contributions. We propose a novel index for the key leader selection with incomplete information that can be substantially different from the key player index in Ballester et al. (2006) [Ballester C, Calvo-Armengol A, Zenou Y (2006) Who's who in networks, wanted: The key player. Econometrica 74(5):1403-1417] and the key leader index with complete information proposed in Zhou and Chen (2015) [Zhou J, Chen Y-J (2015) Key leaders in social networks. J. Econom. Theory 157:212-235], We also show that in undirected graphs, the optimal leader group identified in Zhou and Chen (2015) is exactly the optimal follower group when signaling is present. In particular, if the graphs are complete, the network designer ranks the players by the ascending order of their intrinsic valuations, and the leaders are those with lower intrinsic valuations. In the out-tree hierarchical structure, the key leader turns out to be the one that stays in the middle, and it is not necessarily exactly the central player in the network. Keywords: social network; signaling; information management; targeted advertising; game theory Subject classifications', games/group decisions: noncooperative; network/graphs: applications; organizational studies: information, leadership. Area of review: Special Issue on Information and Decisions in Social and Economic Networks. History: Received June 2013; revisions received November 2014, August 2015; accepted August 2015. Published online in Articles in Advance February 15, 2016., 1. Introduction In the past decade, we have witnessed the explosive growth of social networks, as exemplified by the success of Facebook, Linkedin, Twitter, and various online game producers. One [...]

The OR Practice area publishes papers that report on outstanding applications of operations research to real problems. The intent of the area is to attract and highlight innovative examples of [...]

The Military and Homeland Security (M&HLS) area seeks papers that present original research and innovative models of defense-related problems. While the area still considers for publication papers that address classical [...]

The field of operations research has been instrumental in developing several areas of applied mathematics and has had a profound impact on a wide range of issues in the public, [...]

This paper studies sourcing decisions of firms in a multitier supply chain when procurement is subject to disruption risk. We argue that features of the production process that are commonly encountered in practice (including differential production technologies and financial constraints) may result in the formation of inefficient supply chains, owing to the misalignment of the sourcing incentives of firms at different tiers. We provide a characterization of the conditions under which upstream suppliers adopt sourcing strategies that are suboptimal from the perspective of firms further downstream. Our analysis highlights that a focus on optimizing procurement decisions in each tier of the supply chain in isolation may not be sufficient for mitigating risks at an aggregate level. Rather, we argue that a holistic view of the entire supply network is necessary to properly assess and secure against disruptive events. Importantly, the misalignment we identify does not originate from cost or reliability asymmetries. Rather, firms' sourcing decisions are driven by the interplay of the firms' risk considerations with nonconvexities in the production process. This implies that bilateral contracts that could involve under-delivery penalties may be insufficient to align incentives. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2017.1708. Keywords: multisourcing * disruption risk * supply chain networks, 1. Introduction Despite their vital role in the production process in any modern economy, supply chain linkages have been increasingly recognized as a source of propagation and amplification of risk. [...]

Since the early days of OR, optimization has been one of the foundations of this discipline. Over the years, optimization models and methods have grown in their ability to handle [...]

Simulation has evolved into a mature discipline that is now one of the most widely used techniques in operations research. The Simulation area supports the field's continuing growth by publishing [...]

The marketing science department at Operations Research will publish papers that use applied operations research methods to address marketing topics. The range of relevant topics available is very large. They [...]

Where to begin? I became involved with inventory theory in 1955, but the proper starting point for this memoir is some years earlier, in the fall of 1951, when I [...]

Adaptive robust optimization problems are usually solved approximately by restricting the adaptive decisions to simple parametric decision rules. However, the corresponding approximation error can be substantial. In this paper we show that two-stage robust and distributionally robust linear programs can often be reformulated exactly as conic programs that scale polynomially with the problem dimensions. Specifically, when the ambiguity set constitutes a 2-Wasserstein ball centered at a discrete distribution, the distributionally robust linear program is equivalent to a copositive program (if the problem has complete recourse) or can be approximated arbitrarily closely by a sequence of copositive programs (if the problem has sufficiently expensive recourse). These results directly extend to the classical robust setting and motivate strong tractable approximations of two-stage problems based on semidefinite approximations of the copositive cone. We also demonstrate that the two-stage distributionally robust optimization problem is equivalent to a tractable linear program when the ambiguity set constitutes a 1-Wasserstein ball centered at a discrete distribution and there are no support constraints. Funding: This research was supported by the National Science Foundation [NSF Grant 1752125] and the Swiss National Science Foundation [Grant BSCGI0_157733]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2017.1698. Keywords: two-stage decision problems * distributionally robust optimization * copositive programming, 1. Introduction In two-stage optimization under uncertainty, an agent selects a here-and-now decision before observing the realization of some decision-relevant random vector. Once the uncertainty has been revealed, a wait-and-see [...]

Cost-effectiveness studies of medical innovations often suffer from data inadequacy. When Markov chains are used as a modeling framework for such studies, this data inadequacy can manifest itself as imprecision in the elements of the transition matrix. In this paper, we study how to compute maximal and minimal values for the discounted value of the chain (with respect to a vector of state-wise costs or rewards) as these uncertain transition parameters jointly vary within a given uncertainty set. We show that these problems are computationally tractable if the uncertainty set has a row-wise structure. Conversely, we prove that the row-wise structure is necessary for tractability. Without it, the problems become computationally intractable (strongly NP-hard). We apply our model to assess the cost-effectiveness of fecal immunochemical testing (FIT), a new screening method for colorectal cancer. Our results show that despite the large uncertainty in FIT's performance, it could be cost-effective relative to the prevailing screening method of colonoscopy. Funding: M. Bavati gratefully acknowledges the National Science Foundation [NSF EAGER award CMMI:1451037]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2017.1685. Keywords: cost-effectiveness analysis * Markov models * uncertain parameters * sensitivity analysis, 1. Introduction Cost-effectiveness analyses are essential to the process of innovation. They weigh the proposed benefits of an innovation against the costs of implementing or developing the innovation, and are [...]

Display advertising has a 39% share of the online advertising market and is its fastest-growing category. In this paper, we consider an online display advertising setting in which a web publisher posts display ads on its website and charges based on the cost-per-impression (CPM) pricing scheme while promising to deliver a certain number of impressions on the ads posted. The publisher faces uncertain demand for advertising slots and uncertain supply of visits from viewers. We formulate the problem as a queueing system, where the advertising slots correspond to service channels with the service rate of each server synchronized with other active servers. We determine the publisher's optimal price to charge per impression and show that it can increase in the number of impressions made of each ad, which is in contrast to the quantity discount commonly offered in practice. We show that the optimal CPM price may increase in the number of ads rotating among slots. This result is typically not expected because an increase in the number of rotating ads in the system can be interpreted as an increase in the service capacity. However, the capacity increase leads to an increase in the fill rate of the demand (the portion of demand satisfied by the publisher). Hence, the publisher can afford to optimally decrease the arrival rate by increasing the price. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/opre.2017.1697. Keywords: queueing systems * online advertising * pricing * Markov chains * cost per impression * viewers targeting, 1. Introduction Display advertising currently has a 49.7% share of the online advertising market and is its fastest-growing category (eMarketer 2018). Growing at 17% each year, display advertising is expected [...]

Consumer reviews and ratings of products and services have become ubiquitous on the Internet. This paper analyzes, given the sequential nature of reviews and the limited feedback of such past reviews, the information content they communicate to future customers. We consider a model with heterogeneous customers who buy a product of unknown quality and we focus on two different informational settings. In the first setting, customers observe the whole history of past reviews. In the second one they only observe the sample mean of past reviews. We examine under which conditions, in each setting, customers can recover the true quality of the product based on the feedback they observe. In the case of total monitoring, if consumers adopt a fully rational Bayesian updating paradigm, then they asymptotically learn the unknown quality. With access to only the sample mean of past reviews, inference becomes intricate for customers and it is not clear if, when, and how social learning can take place. We first analyze the setting when customers interpret the mean as the proxy of quality. We show that in the long run, the sample mean of reviews stabilizes and, in general, customers overestimate the underlying quality of the product. We establish properties of the bias, stemming from the selection associated with observing only reviews of customers who purchase. Then, we show the existence of a simple non-Bayesian quality inference rule that leads to social learning when all customers use such a rule. The results point to the strong information content of even limited statistics of past reviews as long as customers have minimal sophistication. Funding: Marco Scarsini is a member of GNAMPA-INdAM. His work was partially supported by PRIN 20103S5RN3 and IDG31300110. Keywords: online reviews * quality inference * sequential analysis * social learning * Bayesian updating * stochastic approximation algorithms, 1. Introduction 1.1. Motivation The use of reviews has become ubiquitous on the Internet, where websites allow users to comment on the products or services they purchased. Typically the review [...]

How should decentralized supply chains set the profit sharing terms using minimal information on demand and selling price? We develop a distributionally robust Stackelberg game model to address this question. Our framework uses only the first and second moments of the price and demand attributes, and thus can be implemented using only a parsimonious set of parameters. More specifically, we derive the relationships among the optimal wholesale price set by the supplier, the order decision of the retailer, and the corresponding profit shares of each supply chain partner, based on the information available. Interestingly, in the distributionally robust setting, the correlation between demand and selling price has no bearing on the order decision of the retailer. This allows us to simplify the solution structure of the profit sharing agreement problem dramatically. Moreover, the result can be used to recover the optimal selling price when the mean demand is a linear function of the selling price (cf. Raza 2014) [Raza SA (2014) A distribution free approach to newsvendor problem with pricing. 4OR--A Quart. J. Oper. Res. 12(4):335-358.]. Funding: The first author was supported in part by the University of Macau [Grant MYRG2014-00057-FBA]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2017.1677. Keywords: profit sharing agreements * decentralized supply chains * distributionally robust planning * completely positive program, 1. Introduction Profit sharing agreements are among the most common types of contractual arrangements for firms in a supply chain. Under such agreements, the retailer, as a provider to a [...]

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 [...]

It has become a ubiquitous practice for firms that sell new products, such as software, to offer consumers time-locked product trial periods free of charge. We identify trial length as a nuanced signaling instrument, which, together with the price signal, a firm can use to communicate proprietary information about its product quality. We show that a high-quality firm offers a longer trial period and sets a higher price, and is rewarded with a higher profit, relative to its low-quality counterpart. Our finding extends to cases where the firm faces ex ante heterogeneous consumers or when the firm competes with an incumbent product. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/opre.2017.1675. Keywords: trial length x price x quality signal x Bayesian learning, 1. Introduction In this paper, we study free trial programs that allow consumers to use a fully functional version of a product for a limited time period prior to making [...]

In this paper we investigate joint pricing and inventory control problems in a fini te-horizon, single-product, periodic-review setting with certain/uncertain supply capacities. The demands in different periods are random variables [...]

In recent years, the paradigm of cloud computing has emerged as an architecture for computing that makes use of distributed (networked) computing resources. In this paper, we consider a distributed computing algorithmic scheme for stochastic optimization, which relies on modest communication requirements among processors and most importantly, does not require synchronization. Specifically, we analyze a scheme with N > 1 independent threads each implementing a stochastic gradient algorithm. The threads are coupled via a perturbation of the gradient (with attractive and repulsive forces) in a similar manner to mathematical models of flocking, swarming, and other group formations found in nature with mild communication requirements. When the objective function is convex, we show that a flocking-like approach for distributed stochastic optimization provides a noise reduction effect similar to that of a centralized stochastic gradient algorithm based upon the average of N gradient samples at each step. The distributed nature of flocking makes it an appealing computational alternative. We show that when the overhead related to the time needed to gather N samples and synchronization is not negligible, the flocking implementation outperforms a centralized stochastic gradient algorithm based upon the average of N gradient samples at each step. When the objective function is not convex, the flocking-based approach seems better suited to escape locally optimal solutions due to the repulsive force that enforces a certain level of diversity in the set of candidate solutions. Here again, we show that the noise reduction effect is similar to that associated to the centralized stochastic gradient algorithm based upon the average of N gradient samples at each step. Funding: The authors gratefully acknowledge partial support from AFOSR [FA9550-15-1-0504] and NSF [1561381]. Keywords: stochastic optimization * distributed optimization * flocking, 1. Introduction Swarms, flocks, and other group formations can be found in nature in many organisms ranging from simple bacteria to mammals (see Parrish et al. 2002, Okubo 1986, Reynolds [...]