On Multiobjective Evolution Model

Self-Organized Criticality (SOC) phenomena could have a significant effect on the dynamics of ecosystems. The Bak-Sneppen (BS) model is a simple and robust model of biological evolution that exhibits punctuated equilibrium behavior. Here we will introduce random version of BS model. Also we generalize the single objective BS model to a multiobjective one.Comment: 6 pages, 5 figure

The MetNet vehicle: a lander to deploy environmental stations for local and global investigations of Mars

Investigations of global and related local phenomena on Mars such as atmospheric circulation patterns, boundary layer phenomena, water, dust and climatological cycles and investigations of the planetary interior would benefit from simultaneous, distributed in situ measurements. Practically, such an observation network would require low-mass landers, with a high packing density, so a large number of landers could be delivered to Mars with the minimum number of launchers. The Mars Network Lander (MetNet Lander; MNL), a small semi-hard lander/penetrator design with a payload mass fraction of approximately 17 %, has been developed, tested and prototyped. The MNL features an innovative Entry, Descent and Landing System (EDLS) that is based on inflatable structures. The EDLS is capable of decelerating the lander from interplanetary transfer trajectories down to a surface impact speed of 50-70 ms(-1) with a deceleration of < 500 g for < 20 ms. The total mass of the prototype design is approximate to 24 kg, with approximate to 4 kg of mass available for the payload. The EDLS is designed to orient the penetrator for a vertical impact. As the payload bay will be embedded in the surface materials, the bay's temperature excursions will be much less than if it were fully exposed on the Martian surface, allowing a reduction in the amount of thermal insulation and savings on mass. The MNL is well suited for delivering meteorological and atmospheric instruments to the Martian surface. The payload concept also enables the use of other environmental instruments. The small size and low mass of a MNL makes it ideally suited for piggy-backing on larger spacecraft. MNLs are designed primarily for use as surface networks but could also be used as pathfinders for high-value landed missions

Standstill Electric Charge Generates Magnetostatic Field Under Born-Infeld Electrodynamics

The Abelian Born-Infeld classical non-linear electrodynamic has been used to investigate the electric and magnetostatic fields generated by a point-like electrical charge at rest in an inertial frame. The results show a rich internal structure for the charge. Analytical solutions have also been found. Such findings have been interpreted in terms of vacuum polarization and magnetic-like charges produced by the very high strengths of the electric field considered. Apparently non-linearity is to be accounted for the emergence of an anomalous magnetostatic field suggesting a possible connection to that created by a magnetic dipole composed of two mognetic charges with opposite signals. Consistently in situations where the Born-Infeld field strength parameter is free to become infinite, Maxwell`s regime takes over, the magnetic sector vanishes and the electric field assumes a Coulomb behavior with no trace of a magnetic component. The connection to other monopole solutions, like Dirac`s, t' Hooft`s or Poliakov`s types, are also discussed. Finally some speculative remarks are presented in an attempt to explain such fields.Comment: 11 pages, 3 figures. In this version is update a permanent address of the author L.P.G. De Assis and information on submission publication. Submetted to International Journal of Theoretical Physic

Application of Compromise Programming to a semi-detached housing development in order to balance economic and environmental criteria

This is a post-peer-review, pre-copyedit version of an article published in Journal of the Operational Research Society. The definitive publisher-authenticated version: Ruá Aguilar, MJ.; Guadalajara Olmeda, MN. (2012). Application of Compromise Programming to a semi-detached housing development in order to balance economic and environmental criteria. Journal of the Operational Research Society. 64(3):459-468, is available online at: http://www.palgrave-journals.com/jors/journal/v64/n3/full/jors201276a.html.European Energy Performance of Buildings Directives DE promote energy efficiency in buildings. Under these Directives, the European Union States must apply minimum requirements regarding the energy performance of buildings and ensure the certification of their energy performance. The Directives set only the basic principles and requirements, leaving a significant amount of room for the Member States to establish their specific mechanisms, numeric requirements and ways to implement them, taking into account local conditions. With respect to the Spanish case, the search for buildings that are more energy efficient results in a conflict between users¿ economic objectives and society's environmental objectives. In this paper, Compromise Programming is applied to help in the decision-making process. An appropriate distribution of types of dwellings, according to their energy performance and to the climatic zone considered in Spain, will be suggested. Results provide a compromise solution between both objectives.Ruá Aguilar, MJ.; Guadalajara Olmeda, MN. (2012). Application of Compromise Programming to a semi-detached housing development in order to balance economic and environmental criteria. Journal of the Operational Research Society. 64(3):459-468. doi:10.1057/jors.2012.76S459468643Andaloro, A. P. F., Salomone, R., Ioppolo, G., & Andaloro, L. (2010). Energy certification of buildings: A comparative analysis of progress towards implementation in European countries. Energy Policy, 38(10), 5840-5866. doi:10.1016/j.enpol.2010.05.039André, F. J., Cardenete, M. A., & Romero, C. (2008). Using compromise programming for macroeconomic policy making in a general equilibrium framework: theory and application to the Spanish economy. Journal of the Operational Research Society, 59(7), 875-883. doi:10.1057/palgrave.jors.2602415Baja, S., Chapman, D. M., & Dragovich, D. (2006). Spatial based compromise programming for multiple criteria decision making in land use planning. Environmental Modeling & Assessment, 12(3), 171-184. doi:10.1007/s10666-006-9059-1Ballestero, E., & Romero, C. (1991). A theorem connecting utility function optimization and compromise programming. Operations Research Letters, 10(7), 421-427. doi:10.1016/0167-6377(91)90045-qBallestero, E., & Romero, C. (1993). Weighting in compromise programming: A theorem on shadow prices. Operations Research Letters, 13(5), 325-329. doi:10.1016/0167-6377(93)90055-lDavies, H., & Wyatt, D. (2004). Appropriate use of the ISO 15686-1 factor method for durability and service life prediction. Building Research & Information, 32(6), 552-553. doi:10.1080/0961321042000291938Diakaki, C., Grigoroudis, E., Kabelis, N., Kolokotsa, D., Kalaitzakis, K., & Stavrakakis, G. (2010). A multi-objective decision model for the improvement of energy efficiency in buildings. Energy, 35(12), 5483-5496. doi:10.1016/j.energy.2010.05.012Dı́az-Balteiro, L., & Romero, C. (2003). Forest management optimisation models when carbon captured is considered: a goal programming approach. Forest Ecology and Management, 174(1-3), 447-457. doi:10.1016/s0378-1127(02)00075-0Diaz-Balteiro, L., & Rodriguez, L. C. E. (2006). Optimal rotations on Eucalyptus plantations including carbon sequestration—A comparison of results in Brazil and Spain. Forest Ecology and Management, 229(1-3), 247-258. doi:10.1016/j.foreco.2006.04.005Fattahi, P., & Fayyaz, S. (2009). A Compromise Programming Model to Integrated Urban Water Management. Water Resources Management, 24(6), 1211-1227. doi:10.1007/s11269-009-9492-4Hamdy, M., Hasan, A., & Siren, K. (2011). Applying a multi-objective optimization approach for Design of low-emission cost-effective dwellings. Building and Environment, 46(1), 109-123. doi:10.1016/j.buildenv.2010.07.006Johnstone, I. M. (2001). Energy and mass flows of housing: a model and example. Building and Environment, 36(1), 27-41. doi:10.1016/s0360-1323(99)00065-7Johnstone, I. M. (2001). Energy and mass flows of housing: estimating mortality. Building and Environment, 36(1), 43-51. doi:10.1016/s0360-1323(99)00066-9Linares, P., & Romero, C. (2000). A multiple criteria decision making approach for electricity planning in Spain: economic versus environmental objectives. Journal of the Operational Research Society, 51(6), 736-743. doi:10.1057/palgrave.jors.2600944Rey, F. J., Velasco, E., & Varela, F. (2007). Building Energy Analysis (BEA): A methodology to assess building energy labelling. Energy and Buildings, 39(6), 709-716. doi:10.1016/j.enbuild.2006.07.009Rudbeck, C. (2002). Service life of building envelope components: making it operational in economical assessment. Construction and Building Materials, 16(2), 83-89. doi:10.1016/s0950-0618(02)00003-xSan-José, J. T., Losada, R., Cuadrado, J., & Garrucho, I. (2007). Approach to the quantification of the sustainable value in industrial buildings. Building and Environment, 42(11), 3916-3923. doi:10.1016/j.buildenv.2006.11.013Yu, P. L. (1973). A Class of Solutions for Group Decision Problems. Management Science, 19(8), 936-946. doi:10.1287/mnsc.19.8.936Zelany, M. (1974). A concept of compromise solutions and the method of the displaced ideal. Computers & Operations Research, 1(3-4), 479-496. doi:10.1016/0305-0548(74)90064-

Flexible aggregation in multiple attribute decision making: Application to the Kuranda Range Road Upgrade

The conventional method of aggregating the satisfaction of transport projects with respect to multiple attributes is commonly some variant of Simple Additive Weighting (SAW), which involves the sum of products of standardized outcomes of projects with respect to attributes and attribute importance weights. It is suggested that alternative forms of aggregation might be more useful, in particular, the Ordered Weighted Averaging (OWA) operator introduced by Yager (1988). Attribute importance weights and satisfaction of attributes by projects may be aggregated prior to aggregation via an OWA operator. In this case OWA operator weights may be based on the "attitudinal character of the decision maker expressed in terms of the degree of "orness and "andness of the aggregation. A well-known approach is maximum entropy aggregation, in which weights are derived to be as "even (or as minimally dispersed) as a possible subject to satisfying a given "orness or "andness constraint. Recently, aggregation processes have been proposed by Larsen (199920022003) which have several desirable properties and also may be considered as alternative forms of aggregation. An example is given relating to the Kuranda Range Road upgrade (Queensland, Australia) which is limited by grade, poor overtaking opportunities, poor horizontal alignment, and other constraints, and the road is expected to become increasingly congested over the next few years. A more flexible Multiple Attribute Decision Making is used to identify a "best project from a set of four alternative projects

Selecting cash management models from a multiobjective perspective

[EN] This paper addresses the problem of selecting cash management models under different operating conditions from a multiobjective perspective considering not only cost but also risk. A number of models have been proposed to optimize corporate cash management policies. The impact on model performance of different operating conditions becomes an important issue. Here, we provide a range of visual and quantitative tools imported from Receiver Operating Characteristic (ROC) analysis. More precisely, we show the utility of ROC analysis from a triple perspective as a tool for: (1) showing model performance; (2) choosingmodels; and (3) assessing the impact of operating conditions on model performance. We illustrate the selection of cash management models by means of a numerical example.Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118.Salas-Molina, F.; Rodríguez-Aguilar, JA.; Díaz-García, P. (2018). Selecting cash management models from a multiobjective perspective. Annals of Operations Research. 261(1-2):275-288. https://doi.org/10.1007/s10479-017-2634-9S2752882611-2Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.Ballestero, E., & Romero, C. (1998). Multiple criteria decision making and its applications to economic problems. Berlin: Springer.Bi, J., & Bennett, K. P. (2003). Regression error characteristic curves. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 43–50.Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159.da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic cash flow management models: A literature review since the 1980s. In Decision models in engineering and management (pp. 11–28). New York: Springer.Doumpos, M., & Zopounidis, C. (2007). Model combination for credit risk assessment: A stacked generalization approach. Annals of Operations Research, 151(1), 289–306.Drummond, C., & Holte, R. C. (2000). Explicitly representing expected cost: An alternative to roc representation. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 98–207). New York: ACM.Drummond, C., & Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65(1), 95–130.Elkan, C. (2001). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, pp. 973–978). Lawrence Erlbaum associates Ltd.Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874.Flach, P. A. (2003). The geometry of roc space: understanding machine learning metrics through roc isometrics. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 194–201.Garcia-Bernabeu, A., Benito, A., Bravo, M., & Pla-Santamaria, D. (2016). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western spain. Annals of Operations Research, 245(1–2), 163–175.Glasserman, P. (2003). Monte Carlo methods in financial engineering (Vol. 53). New York: Springer.Gregory, G. (1976). Cash flow models: a review. Omega, 4(6), 643–656.Hernández-Orallo, J. (2013). Roc curves for regression. Pattern Recognition, 46(12), 3395–3411.Hernández-Orallo, J., Flach, P., & Ferri, C. (2013). Roc curves in cost space. Machine Learning, 93(1), 71–91.Hernández-Orallo, J., Lachiche, N., & Martınez-Usó, A. (2014). Predictive models for multidimensional data when the resolution context changes. In Workshop on learning over multiple contexts at ECML, volume 2014.Metz, C. E. (1978). Basic principles of roc analysis. In Seminars in nuclear medicine (Vol. 8, pp. 283–298). Amsterdam: Elsevier.Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Berlin: Springer.Ringuest, J. L. (2012). Multiobjective optimization: Behavioral and computational considerations. Berlin: Springer.Ross, S. A., Westerfield, R., & Jordan, B. D. (2002). Fundamentals of corporate finance (sixth ed.). New York: McGraw-Hill.Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, pp. 1–15.Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145–166.Steuer, R. E., Qi, Y., & Hirschberger, M. (2007). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297–317.Stone, B. K. (1972). The use of forecasts and smoothing in control limit models for cash management. Financial Management, 1(1), 72.Torgo, L. (2005). Regression error characteristic surfaces. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 697–702). ACM.Yu, P.-L. (1985). Multiple criteria decision making: concepts, techniques and extensions. New York: Plenum Press.Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill

Application of Decision Theory methods for a Community of Madrid Soil classification case

A land classification method was designed for the Community of Madrid (CM), which has lands suitable for either agriculture use or natural spaces. The process started from an extensive previous CM study that contains sets of land attributes with data for 122 types and a minimum-requirements method providing a land quality classification (SQ) for each land. Borrowing some tools from Operations Research (OR) and from Decision Science, that SQ has been complemented by an additive valuation method that involves a more restricted set of 13 representative attributes analysed using Attribute Valuation Functions to obtain a quality index, QI, and by an original composite method that uses a fuzzy set procedure to obtain a combined quality index, CQI, that contains relevant information from both the SQ and the QI methods

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

A multiobjective model for passive portfolio management: an application on the S&P 100 index

This is an author's accepted manuscript of an article published in: “Journal of Business Economics and Management"; Volume 14, Issue 4, 2013; copyright Taylor & Francis; available online at: http://dx.doi.org/10.3846/16111699.2012.668859Index tracking seeks to minimize the unsystematic risk component by imitating the movements of a reference index. Partial index tracking only considers a subset of the stocks in the index, enabling a substantial cost reduction in comparison with full tracking. Nevertheless, when heterogeneous investment profiles are to be satisfied, traditional index tracking techniques may need different stocks to build the different portfolios. The aim of this paper is to propose a methodology that enables a fund s manager to satisfy different clients investment profiles but using in all cases the same subset of stocks, and considering not only one particular criterion but a compromise between several criteria. For this purpose we use a mathematical programming model that considers the tracking error variance, the excess return and the variance of the portfolio plus the curvature of the tracking frontier. The curvature is not defined for a particular portfolio, but for all the portfolios in the tracking frontier. This way funds managers can offer their clients a wide range of risk-return combinations just picking the appropriate portfolio in the frontier, all of these portfolios sharing the same shares but with different weights. An example of our proposal is applied on the S&P 100.García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). A multiobjective model for passive portfolio management: an application on the S&P 100 index. Journal of Business Economics and Management. 14(4):758-775. doi:10.3846/16111699.2012.668859S758775144Aktan, B., Korsakienė, R., & Smaliukienė, R. (2010). TIME‐VARYING VOLATILITY MODELLING OF BALTIC STOCK MARKETS. Journal of Business Economics and Management, 11(3), 511-532. doi:10.3846/jbem.2010.25Ballestero, E., & Romero, C. (1991). A theorem connecting utility function optimization and compromise programming. Operations Research Letters, 10(7), 421-427. doi:10.1016/0167-6377(91)90045-qBeasley, J. E. (1990). OR-Library: Distributing Test Problems by Electronic Mail. Journal of the Operational Research Society, 41(11), 1069-1072. doi:10.1057/jors.1990.166Beasley, J. E., Meade, N., & Chang, T.-J. (2003). An evolutionary heuristic for the index tracking problem. European Journal of Operational Research, 148(3), 621-643. doi:10.1016/s0377-2217(02)00425-3Canakgoz, N. A., & Beasley, J. E. (2009). Mixed-integer programming approaches for index tracking and enhanced indexation. European Journal of Operational Research, 196(1), 384-399. doi:10.1016/j.ejor.2008.03.015Connor, G., & Leland, H. (1995). Cash Management for Index Tracking. Financial Analysts Journal, 51(6), 75-80. doi:10.2469/faj.v51.n6.1952Corielli, F., & Marcellino, M. (2006). Factor based index tracking. Journal of Banking & Finance, 30(8), 2215-2233. doi:10.1016/j.jbankfin.2005.07.012Derigs, U., & Nickel, N.-H. (2004). On a Local-Search Heuristic for a Class of Tracking Error Minimization Problems in Portfolio Management. Annals of Operations Research, 131(1-4), 45-77. doi:10.1023/b:anor.0000039512.98833.5aDose, C., & Cincotti, S. (2005). Clustering of financial time series with application to index and enhanced index tracking portfolio. Physica A: Statistical Mechanics and its Applications, 355(1), 145-151. doi:10.1016/j.physa.2005.02.078Focardi, S. M., & Fabozzi 3, F. J. (2004). A methodology for index tracking based on time-series clustering. Quantitative Finance, 4(4), 417-425. doi:10.1080/14697680400008668Gaivoronski, A. A., Krylov, S., & van der Wijst, N. (2005). Optimal portfolio selection and dynamic benchmark tracking. European Journal of Operational Research, 163(1), 115-131. doi:10.1016/j.ejor.2003.12.001Hallerbach, W. G., & Spronk, J. (2002). The relevance of MCDM for financial decisions. Journal of Multi-Criteria Decision Analysis, 11(4-5), 187-195. doi:10.1002/mcda.328Jarrett, J. E., & Schilling, J. (2008). DAILY VARIATION AND PREDICTING STOCK MARKET RETURNS FOR THE FRANKFURTER BÖRSE (STOCK MARKET). Journal of Business Economics and Management, 9(3), 189-198. doi:10.3846/1611-1699.2008.9.189-198Roll, R. (1992). A Mean/Variance Analysis of Tracking Error. The Journal of Portfolio Management, 18(4), 13-22. doi:10.3905/jpm.1992.701922Rudolf, M., Wolter, H.-J., & Zimmermann, H. (1999). A linear model for tracking error minimization. Journal of Banking & Finance, 23(1), 85-103. doi:10.1016/s0378-4266(98)00076-4Ruiz-Torrubiano, R., & Suárez, A. (2008). A hybrid optimization approach to index tracking. Annals of Operations Research, 166(1), 57-71. doi:10.1007/s10479-008-0404-4Rutkauskas, A. V., & Stasytyte, V. (s. f.). Decision Making Strategies in Global Exchange and Capital Markets. Advances and Innovations in Systems, Computing Sciences and Software Engineering, 17-22. doi:10.1007/978-1-4020-6264-3_4Tabata, Y., & Takeda, E. (1995). Bicriteria Optimization Problem of Designing an Index Fund. Journal of the Operational Research Society, 46(8), 1023-1032. doi:10.1057/jors.1995.139Teresienė, D. (2009). LITHUANIAN STOCK MARKET ANALYSIS USING A SET OF GARCH MODELS. Journal of Business Economics and Management, 10(4), 349-360. doi:10.3846/1611-1699.2009.10.349-36