Andrew J. Schaefer

4.5k total citations
130 papers, 2.9k citations indexed

About

Andrew J. Schaefer is a scholar working on Management Science and Operations Research, Surgery and Management Information Systems. According to data from OpenAlex, Andrew J. Schaefer has authored 130 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Management Science and Operations Research, 24 papers in Surgery and 22 papers in Management Information Systems. Recurrent topics in Andrew J. Schaefer's work include Healthcare Operations and Scheduling Optimization (16 papers), Vehicle Routing Optimization Methods (16 papers) and Supply Chain and Inventory Management (15 papers). Andrew J. Schaefer is often cited by papers focused on Healthcare Operations and Scheduling Optimization (16 papers), Vehicle Routing Optimization Methods (16 papers) and Supply Chain and Inventory Management (15 papers). Andrew J. Schaefer collaborates with scholars based in United States, Canada and Germany. Andrew J. Schaefer's co-authors include Mark S. Roberts, Oğuzhan Alagöz, Lisa M. Maillart, Steven M. Shechter, Oleg A. Prokopyev, Nan Kong, George L. Nemhauser, Anton J. Kleywegt, Brian T. Denton and Matthew D. Bailey and has published in prestigious journals such as Annals of Internal Medicine, PLoS ONE and Cancer.

In The Last Decade

Andrew J. Schaefer

126 papers receiving 2.8k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Andrew J. Schaefer United States 28 722 478 461 444 422 130 2.9k
Oğuzhan Alagöz United States 39 493 0.7× 183 0.4× 380 0.8× 214 0.5× 904 2.1× 122 4.6k
Stefanos Zenios United States 33 149 0.2× 484 1.0× 345 0.7× 322 0.7× 676 1.6× 67 3.3k
Gregory S. Zaric Canada 33 856 1.2× 104 0.2× 130 0.3× 259 0.6× 802 1.9× 138 3.2k
Martin Pitt United Kingdom 26 250 0.3× 197 0.4× 267 0.6× 630 1.4× 532 1.3× 67 2.6k
María E. Mayorga United States 29 359 0.5× 94 0.2× 173 0.4× 190 0.4× 122 0.3× 107 2.6k
Joseph S. Pliskin Israel 25 364 0.5× 156 0.3× 437 0.9× 175 0.4× 1.7k 4.0× 107 4.5k
Jonathan Karnon Australia 37 899 1.2× 214 0.4× 691 1.5× 562 1.3× 2.3k 5.4× 288 7.1k
Brian T. Denton United States 33 124 0.2× 644 1.3× 993 2.2× 2.3k 5.1× 893 2.1× 105 4.4k
Nan Kong United States 24 135 0.2× 256 0.5× 92 0.2× 183 0.4× 186 0.4× 135 1.8k
James E. Stahl United States 24 312 0.4× 84 0.2× 425 0.9× 406 0.9× 583 1.4× 53 2.1k

Countries citing papers authored by Andrew J. Schaefer

Since Specialization
Citations

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

Fields of papers citing papers by Andrew J. Schaefer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew J. Schaefer. 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 Andrew J. Schaefer. The network helps show where Andrew J. Schaefer may publish in the future.

Co-authorship network of co-authors of Andrew J. Schaefer

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew J. Schaefer. A scholar is included among the top collaborators of Andrew J. Schaefer 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 Andrew J. Schaefer. Andrew J. Schaefer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mohamed, Abdallah, Jacob G. Scott, James E. Bates, et al.. (2025). Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints. Physics and Imaging in Radiation Oncology. 33. 100715–100715.
2.
Hemmati, Mehdi, et al.. (2024). Markov models for clinical decision‐making in radiation oncology: A systematic review. Journal of Medical Imaging and Radiation Oncology. 68(5). 610–623. 1 indexed citations
3.
Brown, Seth N., et al.. (2024). Markov decision process design: A framework for integrating strategic and operational decisions. Operations Research Letters. 54. 107090–107090. 3 indexed citations
4.
Mildebrath, David, et al.. (2023). Characterizing Rational Transplant Program Response to Outcome-Based Regulation. Operations Research. 72(4). 1421–1437.
5.
Ajayi, Temitayo, et al.. (2023). Code and Data Repository for Combination Chemotherapy Optimization with Discrete Dosing. INFORMS journal on computing. 1 indexed citations
6.
Pang, Guodong, et al.. (2023). Acuity-Based Allocation of ICU-Downstream Beds with Flexible Staffing. INFORMS journal on computing. 35(2). 403–422. 2 indexed citations
7.
Schaefer, Andrew J., et al.. (2023). Relaxations and duality for multiobjective integer programming. Mathematical Programming. 207(1-2). 577–616. 1 indexed citations
8.
Ajayi, Temitayo, et al.. (2023). Combination Chemotherapy Optimization with Discrete Dosing. INFORMS journal on computing. 36(2). 434–455. 1 indexed citations
9.
Ajayi, Temitayo, et al.. (2023). Evaluating mixed-integer programming models over multiple right-hand sides. Operations Research Letters. 51(4). 414–420. 2 indexed citations
10.
Patel, Ankit, et al.. (2022). Reinforcement learning of simplex pivot rules: a proof of concept. Optimization Letters. 16(8). 2513–2525. 3 indexed citations
11.
Ajayi, Temitayo, Taewoo Lee, & Andrew J. Schaefer. (2022). Objective Selection for Cancer Treatment: An Inverse Optimization Approach. Operations Research. 70(3). 1717–1738. 6 indexed citations
12.
Ajayi, Temitayo, Christopher Thomas, & Andrew J. Schaefer. (2021). The Gap Function: Evaluating Integer Programming Models over Multiple Right-Hand Sides. Operations Research. 70(2). 1259–1270. 3 indexed citations
13.
Maillart, Lisa M., et al.. (2021). Optimal Pooling, Batching, and Pasteurizing of Donor Human Milk. Service Science. 14(1). 13–34. 3 indexed citations
14.
Maillart, Lisa M., et al.. (2019). Mitigating Information Asymmetry in Liver Allocation. INFORMS journal on computing. 1 indexed citations
15.
Prokopyev, Oleg A., et al.. (2019). Solving Stochastic and Bilevel Mixed-Integer Programs via a Generalized Value Function. Operations Research. 67(6). 1659–1677. 11 indexed citations
16.
Schaefer, Andrew J., et al.. (2012). The case against utilization: deceptive performance measures in inpatient care capacity models. Winter Simulation Conference. 76. 4 indexed citations
17.
Schaefer, Andrew J., et al.. (2012). Sensitivity analysis of an ICU simulation model. Winter Simulation Conference. 83. 1 indexed citations
18.
Shylo, Oleg V., et al.. (2011). Managing patient backlog in a surgical suite that uses a block-booking scheduling system. Winter Simulation Conference. 1319–1329. 2 indexed citations
19.
Schaefer, Andrew J., et al.. (2009). A simulation model of HIV treatment under drug scarcity constraints. Winter Simulation Conference. 2090–2095. 1 indexed citations
20.
Schaefer, Andrew J., et al.. (2002). Methods for special applications: incorporating biology into discrete event simulation models of organ allocation. Winter Simulation Conference. 532–536. 2 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.

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