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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.


Papers
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Journal ArticleDOI
TL;DR: This paper finds that due to the ability of the stochastic programming model to adapt to the information in the scenario tree, it dominates the fixed mix approach to asset liability management.

100 citations

Book
01 Aug 1986
TL;DR: In this article, the authors present a dual of a dynamic inventory control model: the deterministic case and the stochastic case, and present a list of optimization problems for both cases.
Abstract: 1 Introduction and Summary.- 2 Mathematical Programming and Duality Theory.- 3 Stochastic Linear Programming Models.- 4 Some Linear Programs in Probabilities and Their Duals.- 5 On Integrated Chance Constraints.- 6 On The Behaviour of the Optimal Value Operator of Dynamic Programming.- 7 Robustness against Dependence in Pert.- 8 A Dual of a Dynamic Inventory Control Model: The Deterministic and the Stochastic Case.- List of Optimization Problems.

99 citations

Journal ArticleDOI
TL;DR: A heuristics based on the sample average approximation, involving the Monte Carlo sampling methods, is proposed to solve the problem of evaluating impacts of randomness related to recovery, processing and demand volumes on the design decisions.

99 citations

Journal ArticleDOI
TL;DR: A novel real-time autonomous energy management strategy for a residential MES is proposed using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy.
Abstract: Multi-energy systems (MES) are attracting increasing attention driven by its potential to offer significant flexibility in future smart grids. At the residential level, the roll-out of smart meters and rapid deployment of smart energy devices call for autonomous multi-energy management systems which can exploit real-time information to optimally schedule the usage of different devices with the aim of minimizing end-users’ energy costs. This paper proposes a novel real-time autonomous energy management strategy for a residential MES using a model-free deep reinforcement learning (DRL) based approach, combining state-of-the-art deep deterministic policy gradient (DDPG) method with an innovative prioritized experience replay strategy. This approach is tailored to align with the nature of the problem by posing it in multi-dimensional continuous state and action spaces, facilitating more cost-effective control strategies to be devised. The superior performance of the proposed approach in reducing end-user’s energy cost while coping with the MES uncertainties is demonstrated by comparing it against state-of-the-art DRL methods as well as conventional stochastic programming and robust optimization methods in numerous case studies in a real-world scenario.

99 citations

Journal ArticleDOI
TL;DR: In this article, formal optimal decision approaches for a multi-period asset/liability management model for a pension fund are studied. But the authors focus on the problem of finding the optimal allocation proportions for a large number of instruments and scenarios.
Abstract: This article studies formal optimal decision approaches for a multi‐period asset/liability management model for a pension fund. The authors use Conditional Value‐at‐Risk (CVaR) as a risk measure, the weighted average of the Value‐at‐Risk (VaR) and those losses exceeding VaR. The model is based on sample‐path simulation of the liabilities and returns of financial instruments in the portfolio. The same optimal decisions are made for groups of sample‐paths, which exhibit similar performance characteristics. Since allocation proportions are time‐dependent, these techniques are more flexible than more standard allocation procedures, e.g. “constant proportions.” Optimization is conducted using linear programming. Compared with traditional stochastic programming algorithms (for which the problem dimension increases exponentially in the number of time stages), this approach exhibits a linear growth of the dimension. Therefore, this approach allows the solution of problems with very large numbers of instruments and scenarios.

99 citations


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