Untitled

Consigli, Giorgio; Dempster, M. A. H.

doi:10.1023/a:1018992620909

Cited by 154 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2). Methods generating a scenario tree can be summarized as aggregating nodes and stages, and trimming or refining trees (Consigli and Dempster, 1998;Dempster, 2006;Dempster and Thompson, 1999;Frauendorfer and Marohn, 1998;Klaassen, 1998) differing only in the algorithm of selecting nodes and paths to aggregate.…”

Section: General Methodologymentioning

confidence: 99%

Stochastic-dynamic modelling of farm-level investments under uncertainty

Spiegel

Britz

Djanibekov

et al. 2020

Environmental Modelling & Software

View full text Add to dashboard Cite

Section: General Methodologymentioning

confidence: 99%

Stochastic-dynamic modelling of farm-level investments under uncertainty

Spiegel

Britz

Djanibekov

et al. 2020

Environmental Modelling & Software

View full text Add to dashboard Cite

“…Nielsen and Zenios (1996) study how to apply a multi-period stochastic program using government bonds, mortgage-backed securities, and derivative products. Consigli and Dempster (1998) develop a pension fund problem with uncertainty. , are a series of publications that describe an ALM model adapted to the complexities of the Japanese regulations, such as legal or taxes limitations.…”

Section: Literature Review On Almmentioning

confidence: 99%

Combining a Matheuristic with Simulation for Risk Management of Stochastic Assets and Liabilities

Bayliss

Serra

Nieto

et al. 2020

Risks

View full text Add to dashboard Cite

Specially in the case of scenarios under uncertainty, the efficient management of risk when matching assets and liabilities is a relevant issue for most insurance companies. This paper considers such a scenario, where different assets can be aggregated to better match a liability (or the other way around), and the goal is to find the asset-liability assignments that maximises the overall benefit over a time horizon. To solve this stochastic optimisation problem, a simulation-optimisation methodology is proposed. We use integer programming to generate efficient asset-to-liability assignments, and Monte-Carlo simulation is employed to estimate the risk of failing to pay due liabilities. The simulation results allow us to set a safety margin parameter for the integer program, which encourage the generation of solutions satisfying a minimum reliability threshold. A series of computational experiments contribute to illustrate the proposed methodology and its utility in practical risk management.

show abstract

“…Stochastic programming and related techniques, as powerful modeling paradigms, have found uses in various applications in financial management. Applications in this field include portfolio optimization and asset−liability management for banks, for insurance companies, − for pension funds, , and for fixed income portfolios report on a collection of the financial models for asset and liability management.…”

Section: Classification and Literature Reviewmentioning

confidence: 99%

Multiobjective Decision Processes under Uncertainty: Applications, Problem Formulations, and Solution Strategies

Cheng¹,

Subrahmanian²,

Westerberg³

2005

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

We consider the decision-making problems that firms face when operating in a changing and uncertain environment. Problems of this type arise in many important decision contexts in various industries and pose challenges for both practitioners and researchers. This paper is a contribution to the development of a general framework for formulating and solving this class of problems through an investigation of current applications and solution methodologies. We consider a general class of problems: multiobjective decision processes under uncertainty, with concentration on its application areas, problem formulations, and solution strategies. A morphology classifies the relevant literature by projecting the problems reported onto a multidimensional problem space. A problem of coordinated capacity planning and inventory control serves as an example of this problem class to illustrate the issues related to formulations and solutions throughout the paper. We develop an approximation architecture that decomposes the planning horizon in time into several subhorizons and constructs a distinct decision model based on the differences in information available for each subhorizon. We link these models through states at boundaries and solve them sequentially backward in time based on the principle of optimality. An iterative solution process, i.e., proposing states forward and propagating Pareto fronts backward, searches for the optimal first stage(s) decisions. We investigate and compare combinations of different approaches in solving the example problem. Numerical results demonstrate the advantages of the proposed approximation scheme and emphasize the importance of tailoring solution strategies to specific problems.

show abstract

Untitled

Cited by 154 publications

References 41 publications

Stochastic-dynamic modelling of farm-level investments under uncertainty

Stochastic-dynamic modelling of farm-level investments under uncertainty

Combining a Matheuristic with Simulation for Risk Management of Stochastic Assets and Liabilities

Multiobjective Decision Processes under Uncertainty: Applications, Problem Formulations, and Solution Strategies

Contact Info

Product

Resources

About