80 research outputs found
Aplicación de nanofiltración por membrana y pervaporación para la desalcoholización de vinos comerciales y recuperación de aromas
El objetivo que se planteo a lo largo de este trabajo consistió en la desalcoholización parcial de un vino blanco mediante su nanofiltración y la posterior recuperación de aromas del permeado de la nanofiltración, mediante pervaporación. A partir del retenido de la nanofiltración y de los aromas recuperados por pervaporación se procede a reconstruir el vino de partida del que se espera una reducción sensible del contenido en alcohol asà como una composición aromática y organoléptica, lo más parecida posible al vino original. Para comprobar el adecuado cumplimiento de estos objetivos se realizaron los correspondientes análisis quÃmicos y sensoriales sobre los vinos de partida y el desalcoholizado. A lo largo de esta memoria se discuten los resultados obtenidos y se extraen las conclusiones pertinentes para futuros trabajos en esta dirección.Grado en EnologÃ
A parallelizable algorithmic framework for solving large scale multi-stage stochastic mixed 0-1 problems under uncertainty
Preprint submitted to Computers & Operations Researchmulti-stage stochastic mixed 0-1 optimization, nonsymmetric scenario trees, implicit and explicit nonanticipativity constraints, splitting variable and compact representations, scenario cluster partitioning
Lagrangean decomposition for large-scale two-stage stochastic mixed 0-1 problems
In this paper we study solution methods for solving the dual problem corresponding to the Lagrangean Decomposition of two stage stochastic mixed 0-1 models. We represent the two stage stochastic mixed 0-1 problem by a splitting variable representation of the deterministic equivalent model, where 0-1 and continuous variables appear at any stage. Lagrangean Decomposition is proposed for satisfying both the integrality constraints for the 0-1 variables and the non-anticipativity constraints. We compare the performance of four iterative algorithms based on dual Lagrangean Decomposition schemes, as the Subgradient method, the Volume algorithm, the Progressive Hedging algorithm and the Dynamic Constrained Cutting Plane scheme. We test the conditions and properties of convergence for medium and large-scale dimension stochastic problems. Computational results are reported.Progressive Hedging algorithm, volume algorithm, Lagrangean decomposition, subgradient method
A note on the implementation of the BFC-MSMIP algorithm in C++ by using COIN-OR as an optimization engine
The aim of this technical report is to present some detailed explanations in order to help to understand and use the algorithm Branch and Fix Coordination for solving MultiStage Mixed Integer Problems (BFC- MSMIP). We have developed an algorithmic approach implemented in a C++ experimental code that uses the optimization engine COmputational INfrastructure for Operations Research (COIN-OR) for solving the auxiliary linear and mixed 0-1 submodels. Now, we give the computational and implementational descrip- tion in order to use this open optimization software not only in the implementation of our procedure but also in similar schemes to be developed by the users.nonanticipativity constraints, cluster partitioning, COIN-OR library, branch-and-fix coordination, multi-stage stochastic mixed 0-1 programming
A parallelizable algorithmic framework for solving large scale multi-stage stochastic mixed 0-1 problems under uncertainty
Preprint submitted to Computers & Operations ResearchIn this paper we present a parallelizable scheme of the Branch-and-Fix Coordination algorithm for solving medium and large scale multi-stage mixed 0-1 optimization problems under uncertainty. The uncertainty is represented via a nonsymmetric scenario tree. An information structuring for scenario cluster partitioning of nonsymmetric scenario trees is also presented, given the general model formulation of a multi-stage stochastic mixed 0-1 problem. The basic idea consists of explicitly rewriting the nonanticipativity constraints (NAC) of the 0-1 and continuous variables in the stages with common information. As a result an assignment of the constraint matrix blocks into independent scenario cluster submodels is performed by a so-called cluster splitting-compact representation. This partitioning allows to generate a new information structure to express the NAC which link the related clusters, such that the explicit NAC linking the submodels together is performed by a splitting variable representation. The new algorithm has been implemented in a C++ experimental code that uses the open source optimization engine COIN-OR, for solving the auxiliary linear and mixed 0-1 submodels. Some computational experience is reported to validate the new proposed approach. We give computational evidence of the model tightening effect that have preprocessing techniques in stochastic integer optimization as well, by using the probing and Gomory and clique cuts identification and appending schemes of the optimization engine.This research has been partially supported by the projects ECO2008-00777 ECON from the Ministry of Education and Science, Grupo de Investigación IT-347-10 from the Basque Government, URJC-CM-2008-CET-3703 and
RIESGOS CM from Comunidad de Madrid, and PLANIN MTM2009-14087-C04-01 from Ministry of Science and Innovation, Spain
Lagrangean decomposition for large-scale two-stage stochastic mixed 0-1 problems
In this paper we study solution methods for solving the dual problem corresponding to the Lagrangean Decomposition of two stage stochastic mixed 0-1 models. We represent the two stage stochastic mixed 0-1 problem by a splitting variable representation of the deterministic equivalent model, where 0-1 and continuous variables appear at any stage. Lagrangean Decomposition is proposed for satisfying both the integrality constraints for the 0-1 variables and the non-anticipativity constraints. We compare the performance of four iterative algorithms based on dual Lagrangean Decomposition schemes, as the Subgradient method, the Volume algorithm, the Progressive Hedging algorithm and the Dynamic Constrained Cutting Plane scheme. We test the conditions and properties of convergence for medium and large-scale dimension stochastic problems. Computational results are reported.This research has been partially supported by the projects ECO2008-00777 ECON from the Ministry of Education and Science, Grupo de Investigación IT-347-10 from the Basque Government, grant FPU ECO-2006 from the Ministry of Education and Science, grants RM URJC-CM-2008-CET-3703 and RIESGOS CM from Comunidad de Madrid, and PLANIN MTM2009-14087-C04-01 from Ministry of Science and Innovation, Spain
Scenario Cluster Lagrangian Decomposition in two stage stochastic mixed 0-1 optimization
In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the
(optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim
consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the
nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a
set of scenario clusters. We compare the computational performance of
the four Lagrange multiplier updating procedures, namely
the Subgradient Method, the Volume Algorithm, the Progressive Hedging
Algorithm and the Dynamic Constrained Cutting Plane scheme
for different numbers
of scenario clusters and different dimensions of the original
problem. Our computational experience shows that
the CLD bound and its computational effort depend on the number of
scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single
scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental
code. A broad computational experience is reported on a test of
randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1
cluster submodels, this last solver within
the open source engine COIN-OR. We also give computational evidence of
the model tightening effect that the preprocessing techniques, cut
generation and appending and parallel computing tools have in
stochastic integer optimization. Finally, we have observed that the
plain use of both solvers does not provide the optimal solution of
the instances included in the testbed with which we have experimented but
for two toy instances in affordable elapsed time. On the other hand the proposed
procedures provide strong lower bounds (or the same solution
value) in a considerably shorter elapsed time for the quasi-optimal
solution obtained by other means for the original stochastic problem
A note on the implementation of the BFC-MSMIP algorithm in C++ by using COIN-OR as an optimization engine
The aim of this technical report is to present some detailed explanations in order to help to understand and use the algorithm Branch and Fix Coordination for solving MultiStage Mixed Integer Problems (BFC- MSMIP). We have developed an algorithmic approach implemented in a C++ experimental code that uses the optimization engine COmputational INfrastructure for Operations Research (COIN-OR) for solving the auxiliary linear and mixed 0-1 submodels. Now, we give the computational and implementational descrip- tion in order to use this open optimization software not only in the implementation of our procedure but also in similar schemes to be developed by the users.This research has been partially supported by the project ECO2008-00777 ECON from the Spanish Ministry of Education and Science, and Grupo de Investigación IT-347-10 from the Basque Government
On solving two stage stochastic linear problems by using a new approach, Cluster Benders Decomposition
The optimization of stochastic linear problems, via scenario analysis, based on Benders decomposition requires to appending feasibility and/or optimality cuts to the master problem until the iterative procedure reaches the optimal solution. The cuts are identified by solving the auxiliary submodels attached to the scenarios. In this work, we propose a so-called scenario cluster decomposition approach for dealing with the feasibility cut identification in the Benders method for solving large-scale two stage stochastic linear problems. The scenario tree is decomposed into a set of scenario clusters and tighter feasibility cuts are obtained by solving the auxiliary submodel for each cluster instead of each individual scenario. Then, this scenario cluster based scheme allows us to define tighter feasibility cuts that yield feasible second stage decisions in reasonable time consuming. Some computational experience by using the free software COIN-OR is reported to show the favorable performance of the new approach over traditional Benders decomposition.This research has been partially supported by the projects ECO2008-00777/ECON from the Ministry of Education and Science, PLANIN, MTM14087-C04-01 from the Ministry of Science and Innovation, Grupo de Investigación IT-347-10 from the Basque Government, and the project RIESGOS CM from the Comunidad de Madrid, Spain
A two-stage stochastic integer programming approach
We present an algorithmic approach for solving two-stage stochastic mixed 0-1 problems. The first stage constraints of the Deterministic Equivalent Model have 0--1 variables and continuous variables. The approach uses the Twin Node Family (TNF) concept within the algorithmic framework so-called {Branch-and-Fix Coordination} for satisfying the {nonanticipativity} constraints, jointly with a Benders Decomposition scheme for solving a given {LP} model at each {TNF} integer set. As an illustrative case, the structuring of a portfolio of Mortgage-Backed Securities under uncertainty in the interest rate path along a given time horizon is used. Some computational experience is reported.This research has been partially support by the grant Grupo consolidado de alto rendimiento 9/UPV 00038.321-13631/2001 from UPV, the project MEC2001-0636 from the DGCIT, the Researchers’ Education grant program 2000 from Gobierno Vasco, and the grant GRUPOS79/04 from the Generalitat Valenciana, Spain
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