We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforcement learning as a special case. Previous distributional DP method...
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We introduce a new algorithm to solve constrained nonlinear optimal control problem, with an emphasis on low-thrust trajectory in highly nonlinear dynamics. The algorithm, dubbed Pontryagin-Bellman Differential Dynami...
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In this paper, under the background of carbon trading, a low-carbon economic operation optimization model of integrated energy systems considering the characteristics of flexible load is established. This model not on...
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This study investigates the quality inspection and optimal decision-making in production processes using a dynamic programming algorithm. The primary objective is to develop a sampling inspection plan for components p...
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ISBN:
(数字)9798331536169
ISBN:
(纸本)9798331536176
This study investigates the quality inspection and optimal decision-making in production processes using a dynamic programming algorithm. The primary objective is to develop a sampling inspection plan for components provided by suppliers, aiming to detect whether the defect rate of components is within the specified nominal value under different confidence levels (95% and 90%) while minimizing inspection costs. We propose a sampling model based on the Binomial Distribution and the approximate Normal Distribution to conduct hypothesis testing for defect rate detection. Additionally, a dynamic programming model is constructed to optimize decision-making in the production process, considering factors such as inspection costs, potential risk costs, and losses from defective products entering the market. The results show that the proposed models effectively balance inspection costs and quality control, leading to optimal decisions that maximize profit and minimize losses. Through Python-based simulations, we visualize the decision outcomes and validate the feasibility of the proposed approach.
Stable and efficient video object trajectory tracking can fully reflect video information and has been widely used in different fields. As a novel study, this research proposes the data capture method of sports video ...
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ISBN:
(数字)9798331509828
ISBN:
(纸本)9798331509835
Stable and efficient video object trajectory tracking can fully reflect video information and has been widely used in different fields. As a novel study, this research proposes the data capture method of sports video trajectory based on dynamic programming algorithm. The innovation of the study are: 1. Improve the traditional monocular vision imaging model (NVI) from the imaging principle to reduce the imaging noise caused by lens angle and internal parameter errors, so as to obtain video information stably and efficiently. 2. Combined with the improved kernel correlation filter (KCF) algorithm and the innovative fusion of multi-channel HOG features, the target position is accurately tracked in the video frame sequence, thereby achieving trajectory tracking goals. 3. A multi-line position distance method is designed to effectively estimate the similarity of trajectories by calculating the weighted polygon area of the area enclosed by two trajectories. 4. In terms of trajectory optimization, a dynamic programming algorithm based on a clipping window is used, and grid division and clustering mechanisms are integrated to protect the information. The experiment is conducted under different video schemes and the results have shown the proposed algorithm can obtain the video trajectory effectively.
We present a dynamic programming algorithm for selecting a representative subset of size k from a given set with n points such that the Riesz s-energy is near minimized. While NP-hard in general dimensions, the one-di...
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This study develops an advanced dynamic optimization model that leverages Mixed-Integer Linear programming (MILP) alongside the Genetic Algorithm (GA) to tackle complex resource allocation and configuration challenges...
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ISBN:
(数字)9798331536169
ISBN:
(纸本)9798331536176
This study develops an advanced dynamic optimization model that leverages Mixed-Integer Linear programming (MILP) alongside the Genetic Algorithm (GA) to tackle complex resource allocation and configuration challenges under a multitude of constraints. By incorporating uncertain variables and system constraints, the model adeptly handles multidimensional data, employing intelligent optimization algorithms to enhance its precision and robustness. It uniquely considers the interdependencies among parameters, such as complementarity and substitutability effects, ensuring a more accurate representation of real-world dynamics. To validate its efficacy, sensitivity analysis is conducted, showcasing the model's stability and adaptability across various scenarios. The findings underscore the potential of combining MILP and GA in addressing high-dimensional complex systems, providing a solid scientific foundation for future applications and refinements in optimization modeling techniques. This research not only advances the theoretical understanding but also offers practical insights into optimizing resource management in diverse fields.
With the intensification of the global energy crisis and environmental pollution, the research and development of intelligent connected new energy vehicles have become a hot topic. This study focuses on the hierarchic...
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The growing prevalence of electric vehicles (EVs) requires efficient charging management strategies to tackle the challenges associated with their integration into the power grid. This requirement is particularly true...
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A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the ...
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A method of minimizing rankings inconsistency is proposed for a decision-making problem with rankings of alternatives given by multiple decision makers according to multiple criteria. For each criteria, at first, the total inconsistency between the rankings of all alternatives for the group and the ones for every decision maker is defined after the decision maker weights in respect to the criteria are considered. Similarly, the total inconsistency between their final rankings for the group and the ones under every criteria is determined after the criteria weights are taken into account. Then two nonlinear integer programming models minimizing respectively the two total inconsistencies above are developed and then transformed to two dynamic programming models to obtain separately the rankings of all alternatives for the group with respect to each criteria and their final rankings. A supplier selection case illustrated the proposed method, and some discussions on the results verified its effectiveness. This work develops a new measurement of ordinal preferences’ inconsistency in multi-criteria group decision-making （MCGDM） and extends the cook-seiford social selection function to MCGDM considering weights of criteria and decision makers and can obtain unique ranking result.
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