This paper studies three tool replacement/operation sequencing strategies for a flexible manufacturing system over a finite time horizon: (1) failure replacement-replace the tool only upon failure, (2) optimal prevent...
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This paper studies three tool replacement/operation sequencing strategies for a flexible manufacturing system over a finite time horizon: (1) failure replacement-replace the tool only upon failure, (2) optimal preventive tool replacement for a fixed sequence of operations, and (3) joint scheduling of the optimal preventive tool replacement times and the optimal sequence of operations. stochasticdynamic decision models are used for strategies 2 and 3. The optimization criterion for strategies 2 and 3 is the minimization of the total expected cost over the finite time horizon, We will show through numerical studies that, with the same amount of information, the total expected costs can be reduced considerably by choosing an optimal strategy. Our conclusion is that in flexible manufacturing, optimal tool replacement and optimal operations sequencing are not separate issues. They should be considered jointly to minimize the expected total cost. (C) 2000 John Wiley & Sons, Inc.
We assess the potentials of the approximate dynamicprogramming (ADP) approach for process control, especially as a method to complement the model predictive control (MPC) approach. In the artificial intelligence (AI)...
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We assess the potentials of the approximate dynamicprogramming (ADP) approach for process control, especially as a method to complement the model predictive control (MPC) approach. In the artificial intelligence (AI) and operations research (OR) research communities, ADP has recently seen significant activities as an effective method for solving Markov decision processes (MDPs), which represent a type of multi-stage decision problems under uncertainty. Process control problems are similar to MDPs with the key difference being the continuous state and action spaces as opposed to discrete ones. In addition, unlike in other popular ADP application areas like robotics or games, in process control applications first and foremost concern should be on the safety and economics of the on-going operation rather than on efficient learning. We explore different options within ADP design, such as the pre-decision state vs. post-decision state value function, parametric vs. nonparametric value function approximator, batch-mode vs. continuous-mode learning, and exploration vs. robustness. We argue that ADP possesses great potentials, especially for obtaining effective control policies for stochastic constrained nonlinear or linear systems and continually improving them towards optimality. (C) 2010 Elsevier Ltd. All rights reserved.
Electrification of locomotive with hybridized fuel-cell, battery and supercapacitor has drawn much attention from both the academia and industry. Unlike traditional powertrain, hybrid powertrain consists of multiple p...
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ISBN:
(纸本)9780791884287
Electrification of locomotive with hybridized fuel-cell, battery and supercapacitor has drawn much attention from both the academia and industry. Unlike traditional powertrain, hybrid powertrain consists of multiple power sources with a complex drivetrain structure, various efficiency performance, and different dynamics. Therefore, it is necessary to develop a power management strategy to make sure each power source operates under a quasi-optimal condition and maximize the overall powertrain efficiency. This paper presents the development of a power management framework for a novel hybrid locomotive consisting of PEM fuel cell, battery, and supercapacitor. Both the equivalent consumption management strategy (ECMS) and the stochastic dynamic programming (SDP) are applied to solve for the optimal power split strategy. The resulted power management strategy is presented in the form of policy maps, which makes it convenient for real-time in-vehicle implementations. Simulation results indicate that the SDP demonstrates advantages over the ECMS in terms of equivalent hydrogen consumption over typical locomotive driving cycles.
We consider the co-optimization of flexible household consumption, electric vehicle charging, and behind -the meter distributed energy resources under the net energy metering tariff. Using a stochasticdynamic program...
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ISBN:
(纸本)9781665464413
We consider the co-optimization of flexible household consumption, electric vehicle charging, and behind -the meter distributed energy resources under the net energy metering tariff. Using a stochastic dynamic programming formulation, we show that the optimal co -optimization follows a procrastination threshold policy that delays and minimizes electricity purchasing for EV charging. The policy thresholds can be computed off-line, simplifying the continuous action space dynamic optimization. Empirical studies using renewable, consumption, and EV data demonstrate 30 c7t and 65 % improvements in customer surplus over the state-of-the-art for the typical 6 and 8 -hour scheduling horizons, respectively. The impacts of net energy metering parameters on the household surplus are also demonstrated.
This paper is concerned with the problem of production planning in a flexible manufacturing system consisting of a single or parallel failure-prone machines producing a number of different products. The objective is t...
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ISBN:
(纸本)0780336852
This paper is concerned with the problem of production planning in a flexible manufacturing system consisting of a single or parallel failure-prone machines producing a number of different products. The objective is to choose the rates of production of the various products over time in order to meet their demands at the minimum long-run average cost of production and surplus. It is shown using the vanishing discount approach for the average cost problem that the Hamilton-Jacobi-Bellman equation in terms of directional derivatives has a solution consisting of the minimal average cost and the so-called potential function. The result helps in establishing a verification theorem, and in specifying an optimal control policy in terms of the potential function. The results settle a hitherto open problem as well as generalize known results.
We present a formulation, solution method, and program acceleration techniques for two dynamic control scenarios, both with the common goal of optimizing resource allocations. These approaches allocate resources in a ...
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We present a formulation, solution method, and program acceleration techniques for two dynamic control scenarios, both with the common goal of optimizing resource allocations. These approaches allocate resources in a non-myopic way, accounting for long-term impacts of current control decisions via nominal belief-state optimization (NBO). In both scenarios, the solution techniques are parallelized for reduced execution time. A novel aspect is included in the second scenario: dynamically allocating the computational resources in an online fashion which is made possible through constant aspect ratio tiling (CART).
This paper presents a sub-optimal energy management strategy, based on stochastic dynamic programming (SDP), for efficient powersplit of a Hybrid Electric Vehicle (HEV). An optimal energy management strategy is propos...
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ISBN:
(纸本)9783030112929;9783030112912
This paper presents a sub-optimal energy management strategy, based on stochastic dynamic programming (SDP), for efficient powersplit of a Hybrid Electric Vehicle (HEV). An optimal energy management strategy is proposed, permitting to have simultaneous speed profile and powersplit optimization of the HEV. Formulated as a multi-objective optimization problem, an epsilon-constraint method has been used to find the Pareto front of the energy optimization task. Traffic conditions and driver behavior could be assimilated to a stochastic nature, thus, it is proposed in this paper to address the vehicle power as Markov Decision Process. A stochastic Database is used to store Transition Probability and Reward Matrices, corresponding to suitable vehicle actions w.r.t. specific states. They are used afterwards to calculate sub-optimal powersplit policy for the vehicle via an infinite-horizon SDP approach. Simulation results demonstrate the effectiveness of the proposed approach compared to a deterministic strategy given in [1]. The present work is conducted on a dedicated high-fidelity model of the HEV that was developed on MATLAB/TruckMaker software.
This paper presents the optimal operation of energy storage system (ESS) applied for renewable utilization enhancement. The problem is formulated with stochastic dynamic programming (SDP) framework, which explicitly i...
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ISBN:
(纸本)9781509041688
This paper presents the optimal operation of energy storage system (ESS) applied for renewable utilization enhancement. The problem is formulated with stochastic dynamic programming (SDP) framework, which explicitly incorporates the system uncertainties. The transmission network modeling is particularly considered from the viewpoint of Independent System Operator (ISO). The SDP-based operation policy for ESS, renewable and conventional generation dispatch can accurately adapt to hourly uncertainties in wind and load demand. It is demonstrated to considerably increase wind utilization and reduce energy production cost. Additionally, comparison result shows that the proposed SDP model outperforms look-ahead optimization model as a commonly used approach for operation problems under uncertainty.
This paper proposes an operational policy for long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models using an open-loop feedback control framework. The optim...
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ISBN:
(纸本)9781424422340
This paper proposes an operational policy for long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models using an open-loop feedback control framework. The optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, then disaggregating it on a monthly basis. In order to focus on the ability of the approach to handle the stochastic nature of the problem, a case study with a single-reservoir system is considered. The performance of the proposed approach is evaluated by simulation over the historical inflow records and compared to that of the stochastic dynamic programming approach. The results show that the proposed approach leads to a better operational performance of the plant, providing lower spillages and higher average hydropower efficiency and generation.
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