4,691 research outputs found
On Stochastic Model Predictive Control with Bounded Control Inputs
This paper is concerned with the problem of Model Predictive Control and
Rolling Horizon Control of discrete-time systems subject to possibly unbounded
random noise inputs, while satisfying hard bounds on the control inputs. We use
a nonlinear feedback policy with respect to noise measurements and show that
the resulting mathematical program has a tractable convex solution in both
cases. Moreover, under the assumption that the zero-input and zero-noise system
is asymptotically stable, we show that the variance of the state, under the
resulting Model Predictive Control and Rolling Horizon Control policies, is
bounded. Finally, we provide some numerical examples on how certain matrices in
the underlying mathematical program can be calculated off-line.Comment: 8 page
Dynamic Rolling Horizon-Based Robust Energy Management for Microgrids Under Uncertainty
Within the last few years, the trend towards more distributed, renewable
energy sources has led to major changes and challenges in the electricity
sector. To ensure a stable electricity distribution in this changing
environment, we propose a robust energy management approach to deal with
uncertainty occurring in microgrids. For this, we combine robust optimization
with a rolling horizon framework to obtain an algorithm that is both, tractable
and can deal with the considered uncertainty. The main contribution of this
work lies within the development and testing of a dynamic scheduling tool,
which identifies good starting time slots for the rolling horizon. Combining
this scheduling tool with the rolling horizon framework results in a dynamic
rolling horizon model, which better integrates uncertainty forecasts and
realizations of uncertain parameters into the decision-making process. A case
study reveals that the dynamic rolling horizon model outperforms the classical
version by up to 57% in costs and increases the local use of PV by up to 11%.Comment: 29 pages, 6 figure
Threshold-Based Algorithms for an Online Rolling Horizon Framework Under Uncertainty -- With an Application to Energy Management
Decision problems encountered in practice often possess a highly dynamic and
uncertain nature. In particular fast changing forecasts for parameters (e.g.,
photovoltaic generation forecasts in the context of energy management) pose
large challenges for the classical rolling horizon framework. Within this work,
we propose an online scheduling algorithm for a rolling horizon framework,
which directly uses short-term forecasts and observations of the uncertainty.
The online scheduling algorithm is based on insights and results from
combinatorial online optimization problems and makes use of key properties of
robust optimization. Applied within a robust energy management approach, we
show that the online scheduling algorithm is able to reduce the total
electricity costs within a local microgrid by more than 85% compared to a
classical rolling horizon framework and by more than 50% compared to a
tailor-made dynamic, yet still offline rolling horizon framework. A detailed
analysis provides insights into the working of the online scheduling algorithm
under different underlying forecast error distributions.Comment: 40 pages, 14 figure
A Benders Based Rolling Horizon Algorithm for a Dynamic Facility Location Problem
This study presents a well-known capacitated dynamic facility location problem (DFLP) that satisfies the customer demand at a minimum cost by determining the time period for opening, closing, or retaining an existing facility in a given location. To solve this challenging NP-hard problem, this paper develops a unique hybrid solution algorithm that combines a rolling horizon algorithm with an accelerated Benders decomposition algorithm. Extensive computational experiments are performed on benchmark test instances to evaluate the hybrid algorithm’s efficiency and robustness in solving the DFLP problem. Computational results indicate that the hybrid Benders based rolling horizon algorithm consistently offers high quality feasible solutions in a much shorter computational time period than the stand-alone rolling horizon and accelerated Benders decomposition algorithms in the experimental range
Population seeding techniques for Rolling Horizon Evolution in General Video Game Playing
While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative. However, there is little attention paid to population initialization techniques in the setting of general real-time video games. Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length). An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution. In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm. The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search
A multiparametric programming rolling horizon scheduling framework: application in a network of combined heat and power systems
We introduce a new approach for the reactive scheduling of production systems with
uncertain parameters of bounded form. The proposed method follows a state-space representation
for the scheduling problem, and relies on the use of a rolling horizon framework and multiparametric
programming (mp) techniques. We show that by considering as uncertain parameters the set of variables
that describe the state of the system at the beginning of the prediction horizon, we can effectively formulate
a set of state-space mp problems that are solved just once and offline. In contrast to existing methods,
the repetitive solution of a new mp after each disruptive event is avoided. The results of the parametric
optimization are used in a rolling horizon basis without the need for online optimization. The proposed
mp rolling horizon (mpRH) approach is applied in the scheduling of a network of combined heat and
power (CHP) units
Rolling Horizon NEAT for General Video Game Playing
This paper presents a new Statistical Forward Planning (SFP) method, Rolling
Horizon NeuroEvolution of Augmenting Topologies (rhNEAT). Unlike traditional
Rolling Horizon Evolution, where an evolutionary algorithm is in charge of
evolving a sequence of actions, rhNEAT evolves weights and connections of a
neural network in real-time, planning several steps ahead before returning an
action to execute in the game. Different versions of the algorithm are explored
in a collection of 20 GVGAI games, and compared with other SFP methods and
state of the art results. Although results are overall not better than other
SFP methods, the nature of rhNEAT to adapt to changing game features has
allowed to establish new state of the art records in games that other methods
have traditionally struggled with. The algorithm proposed here is general and
introduces a new way of representing information within rolling horizon
evolution techniques.Comment: 8 pages, 5 figures, accepted for publication in IEEE Conference on
Games (CoG) 202
Real-Time Scheduling Approaches for Vehicle-Based Internal Transport Systems
In this paper, we study the problem of scheduling and dispatching vehicles in vehicle-based internal transport systems within warehouses and production facilities. We develop and use two rolling horizon policies to solve real-time vehicle scheduling problems. To solve static instances of scheduling problems, we propose two new heuristics: combined and column-generation heuristics. We solve a real-time scheduling problem by applying a heuristic to dynamically solve a series of static instances under a rolling horizon policy. A rolling horizon can be seen either as a fixed-time interval in which advance information about loads’ arrivals is available, or as a fixed number of loads which are known to become available in the near future. We also propose a new look-ahead dynamic assignment algorithm, a different dynamic vehicle-scheduling approach. We evaluate these dynamic scheduling strategies by comparing their performance with that of two of the best online vehicle dispatching rules mentioned in the literature. Experimental results show that the new look-ahead dynamic assignment algorithm and dynamic scheduling approaches consistently outperform vehicle dispatching rules
New heuristics for the stochastic tactical railway maintenance problem
Efficient methods have been proposed in the literature for the management of a set of railway maintenance operations. However, these methods consider maintenance operations as deterministic and known a priori. In the stochastic tactical railway maintenance problem (STRMP), maintenance operations are not known in advance. In fact, since future track conditions can only be predicted, maintenance operations become stochastic. STRMP is based on a rolling horizon. For each month of the rolling horizon, an adaptive plan must be addressed. Each adaptive plan becomes deterministic, since it consists of a particular subproblem of the whole STRMP. Nevertheless, an exact resolution of each plan along the rolling horizon would be too time-consuming. Therefore, a heuristic approach that can provide efficient solutions within a reasonable computational time is required. Although STRMP has already been introduced in the literature, little work has been done in terms of solution methods and computational results. The main contributions of this paper include new methodology developments, a linear model for the deterministic subproblem, three efficient heuristics for the fast and effective resolution of each deterministic subproblem, and extensive computational results
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