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In recent years, one of the main thrusts of set-theoretic research has been the investigation of maximality principles for V, the universe of sets. The Hyperuniverse Programme (HP) has formulated several maximality principles, which express the maximality of V both in height and width. The paper provides an overview of the principles which have been investigated so far in the programme, as well as of the logical and model-theoretic tools which are needed to formulate them mathematically, and also briefly shows how optimal principles, among those available, may be selected in a justifiable way., Author(s): Sy-David Friedman [sup.1] , Claudio Ternullo [sup.2] Author Affiliations: (1) grid.10420.37, 0000 0001 2286 1424, Kurt Gödel Research Center for Mathematical Logic, University of Vienna, , Kolingasse, 14-16, 1090, [...]

In this analysis of the risk and return of stocks in global and Chinese markets, we build a reasonably large number of models for stock selection and create optimized portfolios to outperform a global benchmark. We apply robust regression techniques in producing stock-selection models and Markowitz-based optimization techniques in portfolio construction in a global stock universe and two Chinese stock universes. We report the results of applying a data mining corrections test to the global and Chinese stock universes. We find that (1) robust regression applications are appropriate for modeling stock returns in global and Chinese stock markets; (2) mean-variance techniques continue to produce portfolios capable of generating returns that exceed transactions costs; and (3) our global portfolio selection models pass data mining tests, such that the models produce statistically significant asset selection for global and MSCI-China universes, but not for China A-shares. History: This paper has been accepted for the special issue on Applications of Analytics and Operations Research in Big Data Analysis. Keywords: global portfolio selection * investment * optimization, In this study, we employ fundamental, price-momentum, and forecasted earnings acceleration variables and regression techniques to generate stock-selection models in global and Chinese stock universes. We test whether the stock-selection [...]

Recent research in hybrid optimization shows that a combination of technologies that exploits their complementary strengths can significantly speed up computation. The use of high-level metaconstraints in the problem formulation [...]

Territory-based routing approaches (TBRAs) are commonly used to achieve high service consistency, e.g., in the small package shipping industry, but their drawback is a decline in routing flexibility. Consequently, a [...]

When the leader's objective function of a nonlinear bilevel programming problem is nondifferentiable and the follower's problem of it is nonconvex, the existing algorithms cannot solve the problem. In this [...]

In 2007, the Department of Industrial Engineering at the University of Chile allied with a major Chilean mining company to inaugurate a master's degree program in globalization management. The program's [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2010.07.015 Byline: Pedro Godinho (a), Joana Dias (b) Keywords: Location; Game theory; Simultaneous decisions; Optimization algorithms; Nash equilibrium Abstract: In this paper we will describe and study a competitive discrete location problem in which two decision-makers (players) will have to decide where to locate their own facilities, and customers will be assigned to the closest open facilities. We will consider the situation in which the players must decide simultaneously, unsure about the decisions of one another, and we will present the problem in a franchising environment. Most problems described in the literature consider sequential rather than simultaneous decisions. In a competitive environment, most problems consider that there is a set of known and already located facilities, and new facilities will have to be located, competing with the existing ones. In the presence of more than one decision-maker, most problems found in the literature belong to the class of Stackelberg location problems, where one decision-maker, the leader, locates first and then the other decision-maker, the follower, locates second, knowing the decisions made by the first. These types of problems are sequential and differ significantly from the problem tackled in this paper, where we explicitly consider simultaneous, non-cooperative discrete location decisions. We describe the problem and its context, propose some mathematical formulations and present an algorithmic approach that was developed to find Nash equilibria. Some computational tests were performed that allowed us to better understand some of the features of the problem and the associated Nash equilibria. Among other results, we conclude that worsening the situation of a player tends to benefit the other player, and that the inefficiency of Nash equilibria tends to increase with the level of competition. Author Affiliation: (a) Faculdade de Economia and GEMF, Universidade de Coimbra, Av. Dias da Silva, 165, 3004-512 Coimbra, Portugal (b) Faculdade de Economia and Inesc-Coimbra, Universidade de Coimbra, Av. Dias da Silva, 165, 3004-512 Coimbra, Portugal Article History: Received 17 November 2009; Accepted 14 July 2010

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2010.05.013 Byline: L. Andrieu (a), R. Henrion (b), W. Romisch (c) Keywords: Dynamic chance constraints; Probabilistic constraints; Multistage; Water reservoir management Abstract: In this paper, a model for (joint) dynamic chance constraints is proposed and applied to an optimization problem in water reservoir management. The model relies on discretization of the decision variables but keeps the probability distribution continuous. Our approach relies on calculating probabilities of rectangles which is particularly useful in the presence of independent random variables but works equally well in the case of correlated variables. Numerical results are provided for two and three stages. Author Affiliation: (a) Electricite de France, Recherche and Developpement, 1 Avenue du General de Gaulle, 92141 Clamart, France (b) Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstr. 39, 10117 Berlin, Germany (c) Humboldt University Berlin, Department of Mathematics, Rudower Chaussee 25, 12489 Berlin, Germany Article History: Received 7 February 2009; Accepted 12 May 2010 Article Note: (footnote) [star] This work was supported by the OSIRIS Department of Electricite de France R&D and by the DFG Research Center Matheon 'Mathematics for key technologies' in Berlin.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jebo.2010.06.012 Byline: Kwang Soo Cheong (a), Kineung Choo (b), Keun Lee (c) Keywords: Business groups; Stand-alone firms; Lumpy investment; Chaebols; Korea; Resource-based theory of the firm Abstract: Business groups play significant economic roles in many countries. Motivated by the observation that the literature on business groups is ever-expanding yet providing ambiguous or conflicting results based on empirical investigations, this paper intends to first develop a theoretical model to explain behavior and performance of business groups and then conduct complementary empirical analysis. Drawing on resource-based theory of firm growth, this paper models the firms of which some key resources are lumpy or indivisible and thus they must be purchased or installed only in certain sizes. This premise in the basic model leads to an advantage for business-group firms relative to stand-alone firms without respect to the existence of market failure. The model also predicts that business-group firms will be more profitable in terms of profit-to-sales and will have higher sales and asset, leading to faster growth, other things being equal. Implications of such advantage on firms' performance and behavior are investigated first using numerical experiments based on a dynamic optimization model and then regression analyses based on the Korean firm data. It turns out that a set of testable predictions derived from the basic theoretical model is generally consistent with the conclusions from numerical experiments and empirical tests. Author Affiliation: (a) Carey Business School, Johns Hopkins University, Baltimore, MD, United States (b) Korea Naval Academy, Republic of Korea (c) Economics Department, Seoul National University, Seoul 151-746, Republic of Korea Article History: Received 9 July 2009; Revised 29 June 2010; Accepted 30 June 2010 Article Note: (footnote) [star] The authors gratefully acknowledge the financial support from the SK Economic and Business Research Institute, the research assistance of Nan Ge at Johns Hopkins University, and the comments by the editor and referee of this journal. Any errors and misrepresentations in the paper are the authors' responsibility.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2010.01.013 Byline: P. Casas (a)(c), S. Vaton (a), L. Fillatre (b), I. Nikiforov (b) Keywords: Network Monitoring and Traffic Analysis; Traffic Matrix; Network Traffic Modeling; Optimal Volume Anomaly Detection and Isolation Abstract: Recent studies from major network technology vendors forecast the advent of the Exabyte era, a massive increase in network traffic driven by high-definition video and high-speed access technology penetration. One of the most formidable difficulties that this forthcoming scenario poses for the Internet is congestion problems due to traffic volume anomalies at the core network. In the light of this challenging near future, we develop in this work different network-wide anomaly detection and isolation algorithms to deal with volume anomalies in large-scale network traffic flows, using coarse-grained measurements as a practical constraint. These algorithms present well-established optimality properties in terms of false alarm and miss detection rate, or in terms of detection/isolation delay and false detection/isolation rate, a feature absent in previous works. This represents a paramount advantage with respect to current in-house methods, as it allows to generalize results independently of particular evaluations. The detection and isolation algorithms are based on a novel linear, parsimonious, and non-data-driven spatial model for a large-scale network traffic matrix. This model allows detecting and isolating anomalies in the Origin-Destination traffic flows from aggregated measurements, reducing the overhead and avoiding the challenges of direct flow measurement. Our proposals are analyzed and validated using real traffic and network topologies from three different large-scale IP backbone networks. Author Affiliation: (a) Telecom Bretagne, Brest, France (b) Universite de Technolgie de Troyes, Troyes, France (c) Universidad de la Republica, Montevideo, Uruguay Article History: Received 10 August 2009; Revised 13 January 2010; Accepted 23 January 2010 Article Note: (miscellaneous) Responsible Editor: A. Popescu

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1523-1739.2009.01443.x Byline: ALANA L. MOORE (*[dagger][double dagger]), MICHAEL A. McCARTHY (*) Keywords: active adaptive management; Bayesian statistics; decision theory; optimal monitoring; terminal rewards; revegetation Abstract: Abstract: Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive-management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay-off for learning about their benefits is large. Abstract (Spanish): Valoracion de la Informacion en Modelos de Manejo Adaptativo Resumen: El manejo adaptativo activo considera los beneficios de la utilizacion de estrategias que pueden ser suboptimas en el corto plazo pero pueden proporcionar informacion adicional que facilitara un mejor manejo en el futuro. En muchos problemas de manejo adaptativo que han sido estudiados, las politicas optimas activas y pasivas (considerar el aprendizaje cuando se disenan politicas y diseno de politicas con base en la mejor informacion disponible, respectivamente) son muy similares. Esto parece paradojico; cuando hay incertidumbre sobre la mejor accion, los manejadores deben gastar poca energia en el diseno de programas activos para aprender sobre el sistema que estan manejando. Consideramos dos posibles razones por las que las soluciones adaptativas activas y pasivas a menudo son similares. Primero, los beneficios del aprendizaje a menudo estan confinados al estudio de caso particular en el escenario modelado, mientras que en la realidad la informacion obtenida de estudios locales a menudo es aplicada mas ampliamente. Segundo, los objetivos de manejo que incorporan la varianza de una estimacion pueden poner mayor enfasis en el aprendizaje que los objetivos utilizados mas comunmente que intentan maximizar un valor esperado. Exploramos estos temas en un estudio de caso de Merri Creek, Melbourne, Australia, en el que el objetivo fue elegir entre dos opciones para revegetacion. Explicitamente incorporamos los costos de monitoreo en el modelo. El valor de las recompensas terminales y la eleccion del objetivo influyeron en la diferencia entre las soluciones adaptativas activas y pasivas. La consideracion explicita del costo de monitoreo proporciono una perspectiva diferente de como afectaron al aprendizaje la recompensa terminal y el objetivo de manejo. Los estados para los que era optimo monitorear no coincidieron siempre con los estados en los que hubo diferencias en el manejo adaptativo activo y pasivo. Nuestros resultados enfatizan que el gasto de recursos en monitoreo solo es optimo cuando los beneficios esperados de las opciones consideradas son similares y cuando la amortizacion del aprendizaje de sus beneficios es grande. Author Affiliation: (*)School of Botany, University of Melbourne, Parkville, Victoria 3010, Australia ([dagger])Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria 3010, Australia Article History: Paper submitted March 18, 2009; revised manuscript accepted September 30, 2009. Article note: ([double dagger]) email moa@unimelb.edu.au

ABSTRACT This paper applies a geospatial network optimization model to explore environmental, economic, and time-of-delivery tradeoffs associated with the application of marine vessels as substitutes for heavy-duty trucks operating in [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.06.027 Byline: C. Gicquel (a), N. Miegeville (b), M. Minoux (c), Y. Dallery (a) Keywords: Integer programming; Branch and bound; Valid inequalities; Production line design; Glass coating Abstract: Glass coating is a specific transformation aiming at improving glass performance. The work presented in this paper deals with the determination of the optimal configuration of the production lines used to perform this operation. We propose a first MIP formulation of the problem and then discuss several types of valid inequalities for improving it. The main idea is to exploit explicit or implicit binary exclusion constraints to derive stronger valid inequalities: the maximal clique constraints. Efficient (polynomial time) separation algorithms exploiting special structure of the problem are described, giving rise to a cutting-plane generation procedure for strengthening the initial formulation. The computational study carried out shows that, with the enhanced formulation, good solutions can be obtained within reasonable computation times using currently available integer programming software. Author Affiliation: (a) Laboratoire de Genie Industriel, Grande Voie des Vignes, 92290 Chatenay-Malabry, France (b) Saint-Gobain Recherche, 39 quai Lucien-Lefranc, 93303 Aubervilliers, France (c) Laboratoire d'Informatique de Paris 6, 4 place Jussieu, 75005 Paris, France Article History: Received 15 June 2007; Accepted 27 June 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.03.016 Byline: Jurij MiheliA (a), Amine Mahjoub (b), Christophe Rapine (c), Borut RobiA (a) Keywords: Complexity theory; Uncertainty modeling; Scenarios; Combinatorial optimization; Decision analysis; Network flows Abstract: A significant input-data uncertainty is often present in practical situations. One approach to coping with this uncertainty is to describe the uncertainty with scenarios. A scenario represents a potential realization of the important parameters of the problem. In this paper we apply a recent approach, called flexibility, to solving two-stage flexible-choice problems. The first stage represents the present, where a decision maker must plan ahead to make a decision to hedge against uncertainty in the second stage, which represents the uncertain future, and is described as a set of scenarios. When one of the future scenarios is realized, a decision maker is willing to pay some recourse cost to augment the earlier solution to be more suitable for the realized scenario. Since all of the future scenarios are known, it is reasonable to presume that their desired solutions are also known. Thus, the aim of a decision maker is to find a solution in the present that is as easy as possible to adapt to solutions in the future. In this paper we study the problem where feasible solutions of the first stage are all p-element subsets of some finite set, and the solutions of the second stage are fixed p-element subsets. We present computational complexity results and algorithms for two versions of the two-stage flexible-choice problem. We formally define both problems, i.e., the sum-flexibility problem and the max-flexibility problem. For the sum-flexibility problem we describe an exact polynomial-time algorithm for the 3-scenario version, and we show non-approximability for the 4-scenario version. For the max-flexibility problem we show that the 3-scenario version is NP-hard, but approximable within a constant performance guarantee. Additionally, we prove non-approximability for the 4-scenario version of the problem. Author Affiliation: (a) Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia (b) UTIC, Higher School of Sciences and Techniques of Tunis, Tunis, Tunisia (c) G-SCOP Laboratory, Grenoble, France Article History: Received 11 March 2008; Accepted 12 March 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jedc.2009.09.008 Byline: Ling Qi (a)(b), Sadao Kanaya (a)(b) Keywords: The uniqueness of the optimal path; Endogenous fertility; Economic growth model Abstract: In this paper, the model of endogenous fertility proposed by Becker and Barro (1988), and extended by Benhabib and Nishimura (1989) is considered. However, in their model, the uniqueness of the optimal path may fail to hold. Furthermore, the value function may not be concave, because of the variable discount factor with respect to the choice made about fertility. In this paper, we show that under a set of mild conditions, based on the assumption that the cost of raising a child is non-constant, there exists a unique optimal path and the value function is concave and continuously differentiable. We also show the existence of the unique steady state and a monotonically optimal path, and confirm that the steady state is saddle point stable. Author Affiliation: (a) China Institute for Actuarial Science, Central University of Finance and Economics, College South Road 39, Haidian District, Beijing 100081, China (b) Faculty of Economics, Nihon University, Japan Article History: Received 13 November 2007; Accepted 8 July 2009

Optimal foraging theory has only been sporadically applied to nonhuman primates. The classical prey model, modified for patch choice, predicts a sliding 'profitability threshold' for dropping patch types from the diet, preference for profitable foods, dietary niche breadth reduction as encounter rates increase, and that exploitation of a patch type is unrelated to its own abundance. We present results from a l-year study testing these predictions with Himalayan langurs (Semnopithecus entellus) at Langtang National Park, Nepal. Behavioral data included continuous recording of feeding bouts and between-patch travel times. Encounter rates were estimated for 55 food types, which were analyzed for crude protein, lipid, free simple sugar, and fibers. Patch types were entered into the prey model algorithm for eight seasonal time periods and differing age-sex classes and nutritional currencies. Although the model consistently under estimated diet breadth, the majority of nonpredicted patch types represented rare foods. Profitability was positively related to annual/seasonal dietary contribution by organic matter estimates, whereas time estimates provided weaker relationships. Patch types utilized did not decrease with increasing encounter rates involving profitable foods, although low-ranking foods available year-round were taken predominantly when high-ranking foods were scarce. High-ranking foods were taken in close relation to encounter rates, while low-ranking foods were not. The utilization of an energetic currency generally resulted in closest conformation to model predictions, and it performed best when assumptions were most closely approximated. These results suggest that even simple models from foraging theory can provide a useful framework for the study of primate feeding behavior. Am J Phys Anthropol 141:337-357, 2010. KEY WORDS theoretical ecology; diet selection; patch choice; colobines DOI 10.1002/ajpa.21149

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.02.026 Byline: Mariano Gallo (a), Luca D'Acierno (b), Bruno Montella (b) Keywords: Network design; Transportation; Scatter Search Abstract: This paper proposes an optimisation model and a meta-heuristic algorithm for solving the urban network design problem. The problem consists in optimising the layout of an urban road network by designing directions of existing roads and signal settings at intersections. A non-linear constrained optimisation model for solving this problem is formulated, adopting a bi-level approach in order to reduce the complexity of solution methods and the computation times. A Scatter Search algorithm based on a random descent method is proposed and tested on a real dimension network. Initial results show that the proposed approach allows local optimal solutions to be obtained in reasonable computation times. Author Affiliation: (a) Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy (b) Department of Transportation Engineering, 'Federico II' University of Naples, via Claudio 21, 80125 Naples, Italy Article History: Received 11 September 2007; Accepted 15 February 2009

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1467-937X.2009.00576.x Byline: HAN HONG (1), MATTHEW SHUM (2) Abstract: We develop a new estimation methodology for dynamic optimization models with unobserved shocks and deterministic accumulation of the observed state variables. Investment models are an important example of such models. Our pairwise-difference approach exploits two common features of these models: (1) the monotonicity of the agent's decision (policy) function in the shocks, conditional on the observed state variables; and (2) the state-contingent nature of optimal decision making which implies that, conditional on the observed state variables, the variation in observed choices across agents must be due to randomness in the shocks across agents. We illustrate our procedure by estimating a dynamic trading model for the milk production quota market in Ontario, Canada. Author Affiliation: (1)Stanford University (2)California Institute of Technology Article History: First version received December 2004; final version accepted April 2009 (Eds.)