A review of approximate dynamic programming applications within military operations research

Rempel, Mark; Cai, Jialun

doi:10.1016/j.orp.2021.100204

Cited by 5 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to its interest, we will analyze this last group in more detail. Firstly, we have found recent review works on specific optimization techniques, such as dynamic programming and its application to the military field [34].…”

Section: Related Workmentioning

confidence: 99%

Military Applications of Machine Learning: A Bibliometric Perspective

2022

View full text Add to dashboard Cite

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.

show abstract

Section: Related Workmentioning

confidence: 99%

Military Applications of Machine Learning: A Bibliometric Perspective

2022

View full text Add to dashboard Cite

show abstract

“…Its basic principle is the optimization principle proposed by Bellman in the book "Dynamic Programming" [1], that is, the optimal decision-making process of multiple stages. As mentioned in the article "A review of approximate dynamic programming applications within military operations research" [2], Dynamic Programming is based on two fundamental principles: optimal substructure and overlapping subproblems [3]. Given a problem and splits the problem into sub-problems until the sub-problems can be solved directly.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Operations Management and Business Analytics Strategies under Uncertainty: Dynamic Programming

2023

AEMPS

View full text Add to dashboard Cite

Dynamic programming is a method used in mathematics, management science, computer science, economics, and bioinformatics to solve complex problems by decomposing the original problem into relatively simple sub-problems. Dynamic programming is often applicable to problems with overlapping subproblems and optimal substructure properties. This paper employs a comprehensive research approach of literature review, as well as empirical analysis and case studies to investigate the topic and demonstrate the practicality and effectiveness of dynamic programming in solving complex decision-making problems. However, the curse of dimensionality poses challenges when dealing with high-dimensional decision spaces, requiring approximate dynamic programming methods, including reinforcement learning algorithms. Therefore, when dealing with more complex dynamic programming problems, it is also essential to use program construction tools such as Python to help design a program that can optimize the problem. Therefore, in the learning of dynamic programming, in addition to the correct understanding of basic concepts and methods, specific problems must be analyzed and dealt with in detail, models should be built with rich imagination, and solutions should be solved with creative skills.

show abstract