Robust portfolio modeling with incomplete cost information and project interdependencies

Robust portfolio modeling (RPM) [Liesiö, J., Mild, P., Salo, A., 2007. Preference programming for robust portfolio modeling and project selection. European Journal of Operational Research 181, 1488–1505] supports project portfolio selection in the presence of multiple evaluation criteria and incomplete information. In this paper, we extend RPM to account for project interdependencies, incomplete cost information and variable budget levels. These extensions lead to a multi-objective zero-one linear programming problem with interval-valued objective function coefficients for which all non-dominated solutions are determined by a tailored algorithm. The extended RPM framework permits more comprehensive modeling of portfolio problems and provides support for advanced benefit–cost analyses. It retains the key features of RPM by providing robust project and portfolio recommendations and by identifying projects on which further attention should be focused. The extended framework is illustrated with an example on product release planning.     

Consistency of the instrumental weighted variables

A robust version of method of Instrumental Variables accommodating the idea of an implicit weighting the residuals is proposed and its properties studied. Firstly, it is shown that all solutions of the corresponding normal equations are bounded in probability. Then the weak consistency of them is proved. The algorithm, evaluating the estimate, is described and results of small MC study discussed. Research was supported by grant of GA ČR number 402/06/0408.     

Robustness in multi-objective optimization using evolutionary algorithms

This work discusses robustness assessment during multi-objective optimization with a Multi-Objective Evolutionary Algorithm (MOEA) using a combination of two types of robustness measures. Expectation quantifies simultaneously fitness and robustness, while variance assesses the deviation of the original fitness in the neighborhood of the solution. Possible equations for each type are assessed via application to several benchmark problems and the selection of the most adequate is carried out. Diverse combinations of expectation and variance measures are then linked to a specific MOEA proposed by the authors, their selection being done on the basis of the results produced for various multi-objective benchmark problems. Finally, the combination preferred plus the same MOEA are used successfully to obtain the fittest and most robust Pareto optimal frontiers for a few more complex multi-criteria optimization problems.     

Multicriteria decision analysis (MCDA): Central Porto high-speed railway station

Over recent years, Portugal has been working towards their integration in the trans-European high-speed railway network. While in Spain several new high-speed lines are already under construction or beginning operation, in Portugal the necessary studies are still being carried out. One of these studies concerned the location of one of the high-speed railway stations to be built in the city of Porto, meant to connect the second most populated metropolitan area in Portugal to the high-speed railway network. The goal for the study was to compare alternative siting strategies, defined by the Portuguese high-speed railway authority. These strategies were materialized in a set of location alternatives, which were evaluated from a range of technical, economical, social and environmental criteria. This paper describes the multicriteria decision analysis (MCDA) approach by which the best alternative was identified from the given set of possible alternatives.     

A sensitivity analysis algorithm for hierarchical decision models

In this paper, a comprehensive algorithm is developed to analyze the sensitivity of hierarchical decision models (HDM), including the analytic hierarchy process and its variants, to single and multiple changes in the local contribution matrices at any level of the decision hierarchy. The algorithm is applicable to all HDM that use an additive function to derive the overall contribution vector. It is independent of pairwise comparison scales, judgment quantification techniques and group opinion combining methods. The allowable range/region of perturbations, contribution tolerance, operating point sensitivity coefficient, total sensitivity coefficient and the most critical decision element at a certain level are identified in the HDM SA algorithm. An example is given to demonstrate the application of the algorithm and show that HDM SA can reveal information more significant and useful than simply knowing the rank order of the decision alternatives.     

Robust synchronization of nonlinear SISO systems using sliding mode control

We present a robust algorithm to synchronize two different single-input/single-output (SISO) nonlinear systems connected in a master/slave scheme, where the relative degree of the master system (r _m) is greater than or equal to the relative degree of the slave (r _s). The sliding mode control technique is used to design the coupling signal. This discontinuous controller renders the closed-loop system robust with respect to matched bounded disturbances. The synchronization objective is to match the first r _s normal coordinates. Depending on the characteristics of the involved systems, the closed loop system can display full or partial, identical or generalized synchronization. The performance of the proposed controlled synchronization is illustrated numerically and experimentally.     

Robustness of calibration models based on near infrared spectroscopy for the in-line grading of stonefruit for total soluble solids content

The utility of near infrared spectroscopy as a non-invasive technique for the assessment of internal eating quality parameters of stonefruit (peaches, nectarines and plums) was assessed. Calibration model performance for the attributes of total soluble solids (TSS) was encouraging (typical R² > 0.88, RMSECV 0.53-0.88%TSS, SDR_CV 2.9-3.7). Model performance was acceptable using a combined multi-variety peach-nectarine data set, but it was advantageous to maintain a separate multi-variety plum model. Model robustness to temperature was achieved by including into the calibration set samples scanned at a range of temperatures, with less than 5% of total population required to be treated in this way. Similarly, where models incorporated the range of TSS seen in the validation population, prediction performance was good. Model performance was stable over several seasons in terms of R² (typical R² > 0.8), with bias corrected SEP varying in proportion to population S.D. Prediction bias for new populations could be corrected by model updating or direct bias adjustment.     

Reset control systems: The zero-crossing resetting law

A novel representation of reset control systems with a zero-crossing resetting law, in the framework of hybrid inclusions, is postulated. The well-posedness and stability issues of the resulting hybrid dynamical system are investigated, with a strong focus on how non-deterministic behavior is implemented in control practice. Several stability conditions have been developed by using the eigenstructure of matrices related to the periods of the reset interval sequences and by using Lyapunov function-based conditions.     

QSPR-based prediction of adsorption of halogenated aromatics on yellow-brown soil

Halogenated aromatic compounds exist widely in soil and aqueous environment. The study of their transport and distribution is quite important for pollution control and risk assessment. In the present work, the adsorption coefficients of 28 halogenated benzenes, anilines and phenols on yellow-brown soil were measured with batch equilibrium method, and a prediction model was developed through the quantitative structure–property relationship (QSPR) technique. Then the obtained model was tested with Monte Carlo simulation and Jacknife methods. The results indicated that it was robust enough to estimate soil adsorption behaviors for the tested compounds. Based on the obtained model, it could be deduced that the adsorption of halogenated aromatics on yellow-brown soil was not a simple partitioning process but involved complicated interactions.