Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Approximate Dynamic Programming
20111.4k citationsWarren B. PowellWiley series in probability and statisticsprofile →
Approximate Dynamic Programming
20071.1k citationsWarren B. PowellWiley series in probability and statisticsprofile →
Handbook of Learning and Approximate Dynamic Programming
Countries citing papers authored by Warren B. Powell
Since
Specialization
Citations
This map shows the geographic impact of Warren B. Powell's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Warren B. Powell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Warren B. Powell more than expected).
Fields of papers citing papers by Warren B. Powell
This network shows the impact of papers produced by Warren B. Powell. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Warren B. Powell. The network helps show where Warren B. Powell may publish in the future.
Co-authorship network of co-authors of Warren B. Powell
This figure shows the co-authorship network connecting the top 25 collaborators of Warren B. Powell.
A scholar is included among the top collaborators of Warren B. Powell based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Warren B. Powell. Warren B. Powell is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Han, et al.. (2016). A lasso-based sparse knowledge gradient policy for sequential optimal learning. International Conference on Artificial Intelligence and Statistics. 417–425.1 indexed citations
5.
Wang, Yingfei, Chu Wang, & Warren B. Powell. (2016). The knowledge gradient for sequential decision making with stochastic binary feedbacks. International Conference on Machine Learning. 1138–1147.5 indexed citations
6.
Coulon, Michael, Javad Khazaei, & Warren B. Powell. (2015). SMART-SREC: A Stochastic Model of the New Jersey Solar Renewable Energy Certificate Market. SSRN Electronic Journal.
7.
Powell, Warren B. & Ilya O. Ryzhov. (2012). Optimal Learning. Wiley series in probability and statistics.113 indexed citations
Hannah, Lauren A., Warren B. Powell, & David M. Blei. (2010). Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable. Neural Information Processing Systems. 23. 820–828.21 indexed citations
George, Abraham, Warren B. Powell, & Sanjeev R. Kulkarni. (2008). Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management. Journal of Machine Learning Research. 9(68). 2079–2111.32 indexed citations
17.
Powell, Warren B.. (2003). Dynamic Models of Transportation Operations.. Supply Chain Management An International Journal. 677–756.2 indexed citations
18.
Powell, Warren B., et al.. (1990). APPLICATION OF OPTIMIZATION BASED MODELS ON VEHICLE ROUTING AND SCHEDULING PROBLEMS WITH TIME WINDOW CONSTRAINTS. Journal of Business Logistics. 11(2).7 indexed citations
19.
Powell, Warren B., et al.. (1988). SENSITIVITY ANALYSIS OF DYNAMIC NETWORKS: AN APPLICATION TO PRICING AND LOAD EVALUATION FOR TRUCKLOAD MOTOR CARRIERS.1 indexed citations
20.
Sheffi, Yosef, Hani S. Mahmassani, & Warren B. Powell. (1981). Evacuation studies for nuclear power plant sites: A new challenge for transportation engineers. ITE journal. 51(6). 25–28.21 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.