Eric V. Denardo

2.6k citations
36 papers · 1.7k · h-index 18

Impact in

Papers in

Eric V. Denardo

35 papers receiving 1.5k citations

Peers

Eric V. Denardo
Comparison fields: 5 of 99
  • Industrial and Manufacturing Engineering 587
  • Management Information Systems 380
  • Management Science and Operations Research 393
  • Statistics and Probability 128
  • General Decision Sciences 27
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Citations per field
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Citations per year

Countries citing papers authored by Eric V. Denardo

Since Specialization
Citations

This map shows the geographic impact of Eric V. Denardo'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 Eric V. Denardo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric V. Denardo more than expected).

Fields of papers citing papers by Eric V. Denardo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Eric V. Denardo. 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 Eric V. Denardo. The network helps show where Eric V. Denardo may publish in the future.

Co-authors

The 12 scholars most cited alongside Eric V. Denardo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Eric V. Denardo Line = papers co-authored together Eric V. Denardo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1967316
2
Dynamic programming: Models and applications
1982289
3 1988234
4 1979129
5 1968112
6 1988100
7 197071
8 197148
9 197942
10 198635
11 199234
12 196832
13 199627
14 197026
15 200721
16 200621
17 197718
18 196817
19 200417
20 198215

About Eric V. Denardo

Eric V. Denardo is a scholar working on Management Science and Operations Research, Management Information Systems, Computer Networks and Communications, Industrial and Manufacturing Engineering and Artificial Intelligence, having authored 36 papers that have together received 1.7k indexed citations. Recurring topics across this work include Supply Chain and Inventory Management (8 papers), Optimization and Search Problems (7 papers), Scheduling and Optimization Algorithms (6 papers), Economic theories and models (6 papers), Advanced Queuing Theory Analysis (5 papers), Auction Theory and Applications (5 papers), Reinforcement Learning in Robotics (5 papers) and Optimization and Packing Problems (3 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (587 citations), Management Information Systems (380 citations), Management Science and Operations Research (393 citations), Statistics and Probability (128 citations) and General Decision Sciences (27 citations). Eric V. Denardo has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Christopher S. Tang, Bennett L. Fox, Uriel G. Rothblum, Chung‐Yee Lee, Brad L. Miller, Eugene A. Feinberg, Ludo Van der Heyden, Chung Yee Lee, Gur Huberman and Arthur J. Swersey. Their work appears in journals such as Operations Research, Mathematics of Operations Research, Management Science, Annals of Operations Research and SIAM Review.

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.

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