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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: Based on a stochastic programming with recourse framework, the authors incorporates different probabilistic scenarios in the rolling horizon decision process to recognize the input data uncertainty associated with predicted segment running times and segment recovery times and the possibilities of rescheduling decisions after receiving status updates.
Abstract: After a major service disruption on a single-track rail line, dispatchers need to generate a series of train meet-pass plans at different decision times of the rescheduling stage. The task is to recover the impacted train schedule from the current and future disturbances and minimize the expected additional delay under different forecasted operational conditions. Based on a stochastic programming with recourse framework, this paper incorporates different probabilistic scenarios in the rolling horizon decision process to recognize (1) the input data uncertainty associated with predicted segment running times and segment recovery times and (2) the possibilities of rescheduling decisions after receiving status updates. The proposed model periodically optimizes schedules for a relatively long rolling horizon, while selecting and disseminating a robust meet-pass plan for every roll period. A multi-layer branching solution procedure is developed to systematically generate and select meet-pass plans under different stochastic scenarios. Illustrative examples and numerical experiments are used to demonstrate the importance of robust disruption handling under a dynamic and stochastic environment. In terms of expected total train delay time, our experimental results show that the robust solutions are better than the expected value-based solutions by a range of 10–30%.

179 citations

Journal ArticleDOI
TL;DR: In this article, the economic value of heat pumps and electric boilers is assessed by simulating their day-to-day market performance using a novel operational strategy based on two-stage stochastic programming.

179 citations

Journal ArticleDOI
TL;DR: In this article, a bi-level, three-stage Stochastic Mathematical Program with Equilibrium Constraints (SMPEC) is proposed for quantifying and optimizing travel time resilience in roadway networks under non-recurring natural or human-induced disaster events.
Abstract: A bi-level, three-stage Stochastic Mathematical Program with Equilibrium Constraints (SMPEC) is proposed for quantifying and optimizing travel time resilience in roadway networks under non-recurring natural or human-induced disaster events. At the upper-level, a sequence of optimal preparedness and response decisions is taken over pre-event mitigation and preparedness and post-event response stages of the disaster management life cycle. Assuming semi-adaptive user behavior exists shortly after the disaster and after the implementation of immediate response actions, the lower-level problem is formulated as a Partial User Equilibrium, where only affected users are likely to rethink their routing decisions. An exact Progressive Hedging Algorithm is presented for solution of a single-level equivalent, linear approximation of the SMPEC. A recently proposed technique from the literature that uses Schur’s decomposition with SOS1 variables in creating a linear equivalent to complementarity constraints is employed. Similarly, recent advances in piecewise linearization are exploited in addressing nonseparable link travel time functions. The formulation and solution methodology are demonstrated on an illustrative example.

179 citations

Proceedings ArticleDOI
10 Dec 1997
TL;DR: A hierarchical algorithm approach for efficient solution of sensor scheduling problems with large numbers of objects, based on a combination of stochastic dynamic programming and nondifferentiable optimization techniques is described.
Abstract: This paper studies the problem of dynamic scheduling of multi-mode sensor resources for the problem of classification of multiple unknown objects. Because of the uncertain nature of the object types, the problem is formulated as a partially observed Markov decision problem with a large state space. The paper describes a hierarchical algorithm approach for efficient solution of sensor scheduling problems with large numbers of objects, based on a combination of stochastic dynamic programming and nondifferentiable optimization techniques. The algorithm is illustrated with an application involving classification of 10,000 unknown objects.

179 citations

Journal ArticleDOI
TL;DR: The proposed ISO-based surface modeling procedure and the PSO-based two-dimensional hedging rule showed superior overall performance as compared with the neuro-fuzzy approach.

179 citations


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