1,721,107 research outputs found
Optimizing the ageing, retirement and pension dilemma
No full textIl libro descrive le problematiche legate ad una corretta implementazione delle strategie di pensionamento. Vengono toccati i problemi legati alle diverse strutture di pensionamento, all'invecchiamento demografico, all'inflazione e ai tassi di interesse così come i possibili investimenti. Il problema viene analizzato come una gestione di attivi e passivi non solo dal punto di vista delle istituzioni ma anche dei singoli individui
Preface [to Special Issue on Stochastic Dynamic Modeling of Investments and Risks in Financial Markets]
No full textOptimal kinematics of a looped filament
No full textNew kinematics of supercoiling of closed filaments as solutions of the elastic energy minimization are proposed. The analysis is based on the thin rod approximation of the linear elastic theory, under conservation of the self-linking number. The elastic energy is evaluated by means of bending contribution and torsional influence. Time evolution functions are described by means of piecewise polynomial transformations based on cubic spline functions. In contrast with traditional interpolation, the parameters, which define the cubic splines representing the evolution functions, are considered as the unknowns in a nonlinear optimization problem. We show how the coiling process is associated with conversion of mean twist energy into bending energy through the passage by an inflexional configuration in relation to geometric characteristics of the filament evolution. These results provide new insights on the folding mechanism and associated energy contents and may find useful applications in folding of macromolecules and DNA packing in cell biology
Sensitivity analysis of a bond portfolio model for the Italian market
No full textManagement of bond portfolio is formulated as a multi period scenario-based stochastic program with random recourse. The former results on sensitivity analysis of its optimal value with respect to the strategy applied in selection of input scenarios are extended and applied to a real life problem from the Italian bond market. The numerical study provides details on this application and illustrates also the impact of the utility function chosen and of
the size of transaction costs
The value of information in multi-stage linear stochastic programming
Full text linkMultistage stochastic programs, which involve sequences of decisions over time, are usually hard to solve in realistically sized problems. In the two-stage case, several approaches based on different levels of available information has been adopted in literature such as the Expected Value Problem, EV, the Sum of Pairs Expected Values, SPEV, the Expectation of Pairs Expected Value, EPEV, solving series of sub-problems more computationally tractable than the initial one, or the Expected Skeleton Solution Value, ESSV and the Expected Input Value, EIV which evaluate the quality of the deterministic solution in term of its structure and upgradability. Chains of inequalities among the new quantities are proved to evaluate if it is worth the additional computations for the stochastic program versus the simplified approaches proposed. Numerical results on a simple transportation problem are shown. In this paper we generalize the definition of the above quantities to the multistage stochastic framework introducing the Multistage Expected Value of the Reference Scenario, MEV RS, the Multistage Sum of Pairs Expected Values, MSPEV and the Multistage Expectation of Pairs Expected Value, MEPEV by means of the new concept of auxiliary scenario and redefinition of pairs subproblems probability. Measures of quality of the average solution such as the Multistage Loss Using Skeleton Solution, MLUSSt and the Multistage Loss of Upgrading the Deterministic Solution, MLUDSt are introduced too and related to the standard Value of Stochastic Solution, V SSt at stage t
Testing the structure of multistage stochastic programs
No full textA fixed topology of stages and/or a fixed branching scheme are common assumptions for applications and numerical solution of scenario based multistage stochastic programs. Using contamination technique to test this structure, we extend the results of Dupačová (Contamination for multistage stochastic programs. In: Hušková M, Janžura M (eds) Prague stochastics. Matfyzpress, Praha, pp 91–101, 2006a) to stochastic programs with multistage polyhedral risk objectives. The ideas are exemplified by bond portfolio management problems and complemented by illustrative numerical results
Measures of information in multi-stage stochastic programming
No full textMultistage stochastic programs, which involve sequences of decisions over time, are usually hard to solve in realistically sized problems. In the two-stage case, several approaches based on different levels of available information has been adopted in literature such as the Expected Value Problem, EV , the Sum of Pairs Expected Values, SP EV , the Expectation of Pairs Expected Value, EP EV, solving series of sub-problems more computationally tractable than the initial one, or the Expected Skeleton Solution Value, ESSV and the Expected Input Value, EIV which evaluate the quality of the deterministic solution in term of its structure and upgradeability. In this paper we generalize the definition of the above quantities to the multistage stochastic for- mulation when the right hand side of constraints are stochastic: we introduce the Multistage Expected Value of the Reference Scenario, M EV RS, the Multistage Sum of Pairs Expected Values, M SP EV and the Multistage Expectation of Pairs Expected Value, M EP EV by means of the new concept of auxiliary scenario and redefinition of pairs subproblems probability. We show that theorems proved in [2] and [3] for two stage case are valid also in the multi-stage case. Measures of quality of the average solution such as the Multistage Loss Using Skeleton Solution, M LU SSt and the Multistage Loss of Upgrading the Deterministic Solution, M LU DSt are introduced and related to the standard Value of Stochastic Solution, V SSt at stage t. A set of theorems providing chains of inequalities among the new quantities are proved. These bounds may help in evaluating whether it is worth the additional computations for the stochastic program versus the simplified approaches proposed. Numerical results on a case study related to a simple transportation problem are shown
A scenario-based framework for supply planning under uncertainty: stochastic programming versus robust optimization approaches
Full text linkIn this paper we analyze the effect of two modelling approaches for supplyplanningproblemsunderuncertainty: two-stage stochastic programming(SP) and robust optimization (RO). The comparison between the two approaches is performed through a scenario-based framework methodology, which can be applied to any optimization problem affected by uncertainty. For SP we compute the minimum expected cost based on the specific probability distribution of the uncertain parameters related to a set of scenarios. For RO we consider static approaches where random parameters belong to box or ellipsoidal uncertainty sets in compliance with the data used to generate SP scenarios. Dynamic approaches for RO, via the concept of adjustable robust counterpart, are also considered. The efficiency of the methodology has been illustrated for a supply planning problem to optimize vehicle-renting and procurement transportation activities involving uncertainty on demands and on buying costs for extra-vehicles. Numerical experiments through the scenario-based framework allow a fair comparison in real case instances. Advantages and disadvantages of RO and SP are discussed
Bounds in multistage linear stochastic programming
No full textMultistage stochastic programs, which involve sequences of decisions over time, are usually hard to solve in realistically sized problems. Providing bounds for optimal solution may help in evaluating whether it is worth the additional computations for the stochastic program vs. simplified approaches. In this paper we generalize measures from the two-stage case, based on different levels of available information, to the multistage stochastic programming problems. A set of theorems providing chains of inequalities among the new quantities are proved. Numerical results on a case study related to a simple transportation problem illustrate the described relationships
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