Boundless multiobjective models for cash management

"This is an Accepted Manuscript of an article published by Taylor & Francis in Engineering Economist on 31-05-2018, available online: https://doi.org/10.1080/0013791X.2018.1456596"[EN] Cash management models are usually based on a set of bounds that complicate the selection of the optimal policies due to nonlinearity. We here propose to linearize cash management models to guarantee optimality through linear-quadratic multiobjective compromise programming models. We illustrate our approach through a reformulation of the suboptimal state-of-the-art Gormley-Meade¿s model to achieve optimality. Furthermore, we introduce a much simpler formulation that we call the boundless model that also provides optimal solutions without using bounds. Results from a sensitivity analysis using real data sets from 54 different companies show that our boundless model is highly robust to cash flow prediction errors.Generalitat de Catalunya [2014 SGR 118]; Ministerio de Economia y Competitividad [Collectiveware TIN2015-66863-C2-1-R].Salas-Molina, F.; Rodriguez-Aguilar, JA.; Pla Santamaría, D. (2018). Boundless multiobjective models for cash management. Engineering Economist (Online). 63(4):363-381. https://doi.org/10.1080/0013791X.2018.1456596S363381634Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203-228. doi:10.1111/1467-9965.00068Baccarin, S. (2009). Optimal impulse control for a multidimensional cash management system with generalized cost functions. European Journal of Operational Research, 196(1), 198-206. doi:10.1016/j.ejor.2008.02.040Ballestero, E., & Romero, C. (1998). Multiple Criteria Decision Making and its Applications to Economic Problems. doi:10.1007/978-1-4757-2827-9Bar-Ilan, A., Perry, D., & Stadje, W. (2004). A generalized impulse control model of cash management. Journal of Economic Dynamics and Control, 28(6), 1013-1033. doi:10.1016/s0165-1889(03)00064-2Baumol, W. J. (1952). The Transactions Demand for Cash: An Inventory Theoretic Approach. The Quarterly Journal of Economics, 66(4), 545. doi:10.2307/1882104Bemporad, A., & Morari, M. (1999). Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), 407-427. doi:10.1016/s0005-1098(98)00178-2Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. doi:10.1515/9781400831050Branke, J., Deb, K., Miettinen, K., & Słowiński, R. (Eds.). (2008). Multiobjective Optimization. Lecture Notes in Computer Science. doi:10.1007/978-3-540-88908-3Chelouah, R., & Siarry, P. (2000). Journal of Heuristics, 6(2), 191-213. doi:10.1023/a:1009626110229Chen, X., & Simchi-Levi, D. (2009). A NEW APPROACH FOR THE STOCHASTIC CASH BALANCE PROBLEM WITH FIXED COSTS. Probability in the Engineering and Informational Sciences, 23(4), 545-562. doi:10.1017/s0269964809000242Constantinides, G. M., & Richard, S. F. (1978). Existence of Optimal Simple Policies for Discounted-Cost Inventory and Cash Management in Continuous Time. Operations Research, 26(4), 620-636. doi:10.1287/opre.26.4.620Moraes, M. B. da C., & Nagano, M. S. (2014). Evolutionary models in cash management policies with multiple assets. Economic Modelling, 39, 1-7. doi:10.1016/j.econmod.2014.02.010Da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic Cash Flow Management Models: A Literature Review Since the 1980s. Decision Engineering, 11-28. doi:10.1007/978-3-319-11949-6_2De Avila Pacheco, J. V., & Morabito, R. (2011). Application of network flow models for the cash management of an agribusiness company. Computers & Industrial Engineering, 61(3), 848-857. doi:10.1016/j.cie.2011.05.018Girgis, N. M. (1968). Optimal Cash Balance Levels. Management Science, 15(3), 130-140. doi:10.1287/mnsc.15.3.130Golden, B., Liberatore, M., & Lieberman, C. (1979). Models and solution techniques for cash flow management. Computers & Operations Research, 6(1), 13-20. doi:10.1016/0305-0548(79)90010-8Gormley, F. M., & Meade, N. (2007). The utility of cash flow forecasts in the management of corporate cash balances. European Journal of Operational Research, 182(2), 923-935. doi:10.1016/j.ejor.2006.07.041Gregory, G. (1976). Cash flow models: A review. Omega, 4(6), 643-656. doi:10.1016/0305-0483(76)90092-xGurobi Optimization, Inc (2017) Gurobi optimizer reference manual, Houston.Keown, A. J., & Martin, J. D. (1977). A Chance Constrained Goal Programming Model for Working Capital Management. The Engineering Economist, 22(3), 153-174. doi:10.1080/00137917708965174Miller, M. H., & Orr, D. (1966). A Model of the Demand for Money by Firms. The Quarterly Journal of Economics, 80(3), 413. doi:10.2307/1880728Neave, E. H. (1970). The Stochastic Cash Balance Problem with Fixed Costs for Increases and Decreases. Management Science, 16(7), 472-490. doi:10.1287/mnsc.16.7.472PARK, C. S., & HERATH, H. S. B. (2000). EXPLOITING UNCERTAINTY—INVESTMENT OPPORTUNITIES AS REAL OPTIONS: A NEW WAY OF THINKING IN ENGINEERING ECONOMICS. The Engineering Economist, 45(1), 1-36. doi:10.1080/00137910008967534Penttinen, M. J. (1991). Myopic and stationary solutions for stochastic cash balance problems. European Journal of Operational Research, 52(2), 155-166. doi:10.1016/0377-2217(91)90077-9Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443-1471. doi:10.1016/s0378-4266(02)00271-6Salas-Molina, F., Martin, F. J., Rodríguez-Aguilar, J. A., Serrà, J., & Arcos, J. L. (2017). Empowering cash managers to achieve cost savings by improving predictive accuracy. International Journal of Forecasting, 33(2), 403-415. doi:10.1016/j.ijforecast.2016.11.002Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, 267(1-2), 515-529. doi:10.1007/s10479-016-2359-1Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145-166. doi:10.1016/0305-0483(86)90017-4Stone, B. K. (1972). The Use of Forecasts and Smoothing in Control-Limit Models for Cash Management. Financial Management, 1(1), 72. doi:10.2307/3664955Stone, B. K., & Miller, T. W. (1987). Daily Cash Forecasting with Multiplicative Models of Cash Flow Patterns. Financial Management, 16(4), 45. doi:10.2307/3666108Xu, X., & Birge, J. R. (2008). Operational Decisions, Capital Structure, and Managerial Compensation: A News Vendor Perspective. The Engineering Economist, 53(3), 173-196. doi:10.1080/00137910802262887Yu, P.-L. (1985). Multiple-Criteria Decision Making. doi:10.1007/978-1-4684-8395-

Empirical analysis of daily cash flow time series and its implications for forecasting

Usual assumptions on the statistical properties of daily net cash flows include normality,absence of correlation and stationarity. We provide a comprehensive study based on a real-world cash flow data set showing that: (i) the usual assumption of normality, absence of correlation and stationarity hardly appear; (ii) non-linearity is often relevant for forecasting; and (iii) typical data transformations have little impact on linearity and normality. This evidence may lead to consider a more data-driven approach such as time-series forecasting in an attempt to provide cash managers with expert systems in cash management

Returning to the Past to Rethink Socio-Political Antagonisms: Mapping Today’s Situation in Regards to Popular Insurrections

This article seeks to elaborate a map or cartogram based on a number of protests and social mobilizations that took place in different parts of the world -mainly in Latin America, but also in Europe and Asia. Beyond the data and figures available from various sources, which never speak for themselves, an interpretation is proposed here to reveal the meaning of these events. In other words, by displaying a map of these social movements, the authors propose not only the visualization of a collection of data, but also an illumination of these events in the light of history. From there, the authors offer hypothetical predictions. These predictions allow the authors to consider the lessons that, sometimes, seem to be forgotten or are not learned yet

Benthic Fauna of Littoral and Deep-Sea Habitats of the Alboran Sea: A Hotspot of Biodiversity

En prens

Asociaciones de moluscos de fondos sedimentarios circalitorales y batiales del norte del mar de Alborán

Molluscan assemblages from shelf and slope soft bottoms of the Alboran Sea have been sampled with a beam trawl during 2014 and 2015 MEDITS expeditions. A total of 134 spp. of molluscs (shell size > 3 mm) were identified, being gastropods the most diverse and dominant group. Four main depth related assemblages were detected in multivariate analyses and characterized by (1) Turritella communis, Chamelea striatula and Nucula sulcata for the inner shelf, (2) Timoclea ovata, Clelandella miliaris and Neopycnodonte cochlear for the outer shelf, (3) Nassarius ovoideus, Calumbonella suturale and N. sulcata for the upper slope and (4) Abra longicallus, Euspira fusca and Aporrhais serresianus for the middle slope. Species richness and abundance decreased with depth, unlike evenness and Shannon-Wiener diversity which displayed an opposite pattern. A higher spatial variability was detected for the shelf, indicating that more assemblages may occur at this level and further sampling is needed for covering all sedimentary habitat types of the Alboran Sea.Versión del edito

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

Detection of dengue virus serotype 2 in aedes aegypti in Quintana Roo, Mexico, 2011

Abstract. In October 2011, the State Health Department announced that several laboratory-confirmed cases of dengue had occurred among residents in two neighborhoods of Benito Juarez, Quintana Roo State, Mexico. To identify the dengue virus serotype(s) temporally and spatially associated with the cases, entomologic-based virus surveillance was initiated in October 2011 in both neighborhoods. Adult mosquitoes were collected from 88 houses by CDCbackpack aspirator, and all female Aedes aegypti L. (n = 419) were individually homogenized and assayed in pools of as many as 10 by reverse transcriptionpolymerase chain reaction (RT-PCR) using dengue virus-specific primers. Five (12%) of 41 pools were positive for dengue virus RNA. The individual mosquitoes that comprised the pools were analyzed separately by RT-PCR using dengue virus serotype-specific primers. Six mosquitoes were positive for dengue virus serotype-2 (DENV-2) RNA, three of which were collected in the same house. The mean number of female Ae. aegypti collected in each house was 4.76 ± 6.19. The overall dengue virus-infection rate in female Ae. aegypti was 1.4%. Interestingly, most (60%) of mosquito females were collected only from 15 (17%) houses. In summary, we provide evidence of recent DENV-2 transmission in Quintana Roo State

REBISALUD: la red de bibliotecas virtuales de salud

Rebisalud; Cooperació bibliotecària; Biblioteques virtuals de ciències de la salutRebisalud; Cooperación bibliotecaria; Bibliotecas virtuales de ciencias de la saludRebisalud; Library cooperation; Virtual libraries of health sciencesLa Red de Bibliotecas Virtuales de Salud - REBISALUD, como red que integra a 9 bibliotecas virtuales de salud de ámbito autonómico (Andalucía, Aragón, Islas Baleares, Canarias, Cataluña, Comunidad Valenciana, Región de Murcia, Comunidad Foral de Navarra y País Vasco), surge con la misión de potenciar la cooperación entre ellas, mediante la coordinación y elaboración de distintas estrategias y proyectos de colaboración conjuntos, encaminados a optimizar la difusión y el intercambio de la información en el ámbito de la salud