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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


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Journal ArticleDOI
TL;DR: The results show that the ING method finds optimal or close to optimal solutions for the problems presented, and has a wider range of potential application areas than conventional techniques in behavioural modelling.
Abstract: Even though individual-based models (IBMs) have become very popular in ecology during the last decade, there have been few attempts to implement behavioural aspects in IBMs. This is partly due to lack of appropriate techniques. Behavioural and life history aspects can be implemented in IBMs through adaptive models based on genetic algorithms and neural networks (individual-based-neural network-genetic algorithm, ING). To investigate the precision of the adaptation process, we present three cases where solutions can be found by optimisation. These cases include a state-dependent patch selection problem, a simple game between predators and prey, and a more complex vertical migration scenario for a planktivorous fish. In all cases, the optimal solution is calculated and compared with the solution achieved using ING. The results show that the ING method finds optimal or close to optimal solutions for the problems presented. In addition it has a wider range of potential application areas than conventional techniques in behavioural modelling. Especially the method is well suited for complex problems where other methods fail to provide answers.

96 citations

Journal ArticleDOI
TL;DR: This paper proposes, for the first time, a very simple modification of the Lagrangian optimization scheme capable of dealing with robustness, and is applied to the well-known aperiodic train timetabling problem on a corridor and is computationally analyzed on real-world test cases from the Italian Railways.
Abstract: Finding robust yet efficient solutions to optimization problems is a major practical issue that received large attention in recent years. Starting with stochastic programming, many of the approaches to robustness lead to a significant change in the problem formulation with respect to the nonrobust (nominal) case. Besides requiring a much larger computational effort, this often results into major changes of the associated software. Lagrangian heuristics form a wide family of methods that work well in finding efficient (i.e., low-cost) solutions for many problems. These methods approximately solve a relaxation of the problem at hand through an iterative Lagrangian optimization scheme and apply several times a basic heuristic driven by the Lagrangian dual information (typically, the current Lagrangian costs) so as to update the current best feasible solution. In this context, the underlying Lagrangian optimization iterative method has the main purpose of producing “increasingly reliable” Lagrangian costs, while diversifying the search in the last iterations, when the Lagrangian bound is very close to convergence. The purpose of this paper is to propose, for the first time, to the best of our knowledge, a very simple modification of the Lagrangian optimization scheme capable of dealing with robustness. This modification is based on two simple features: (a) We modify the problem formulation by introducing artificial parameters intended to “control” the solution robustness and (b) during Lagrangian optimization, we dynamically change the weight of the control parameters to produce subproblems where robustness becomes more and more important. In this way, during the process we can easily collect a set of, roughly speaking, “Pareto optimal” heuristic solutions that have a different trade-off between robustness and efficiency and leave the final user the choice of the ones to analyze in more details---e.g., through a time-consuming validation tool. As a proof-of-concept, our approach is applied to the well-known aperiodic train timetabling problem on a corridor and is computationally analyzed on real-world test cases from the Italian Railways, showing that it produces within much shorter computing times solutions whose quality is comparable with those produced by existing approaches to robustness. This proves the effectiveness for the specific application, suggesting that a simple modification of existing Lagrangian heuristics is a very promising way to deal with robustness in many other cases.

96 citations

Journal ArticleDOI
TL;DR: It is found that the impacts of operational constraints on real asset valuation are dependent on both the model specification and the nature of operating characteristics.
Abstract: We describe a stochastic dynamic programming approach for “real option”-based valuation of electricity generation capacity incorporating operational constraints and start-up costs. Stochastic prices of electricity and fuel are represented by recombining multinomial trees. Generators are modeled as a strip of cross-commodity call options with a delay and a cost imposed on each option exercise. We illustrate implications of operational characteristics on the valuation of generation assets under different modeling assumptions about the energy commodity prices. We find that the impacts of operational constraints on real asset valuation are dependent on both the model specification and the nature of operating characteristics.

96 citations

Book ChapterDOI
01 Jan 2003
TL;DR: This paper analyzes the structure of stochastic integer programs using basics from parametric integer programming and probability theory and reviews solution techniques from integer programming to discuss their impact on the specialized structures met in Stochastic programming.
Abstract: When introducing integer variables into traditional linear stochastic programs structural properties and algorithmic approaches have to be rethought from the very beginning. Employing basics from parametric integer programming and probability theory we analyze the structure of stochastic integer programs. In the algorithmic part of the paper we review solution techniques from integer programming and discuss their impact on the specialized structures met in stochastic programming.

96 citations

Journal ArticleDOI
TL;DR: The possibility of using alternative models for stochastic programming is studied on a small-size but meaningful example connected with water management of a real-life water resource system in Eastern Czechoslovakia.

96 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532