Some Fellows were recognized for fundamental methodological work, including contributions to integer optimization, optimization under uncertainty, applied probability and stochastic systems. Others were honored for applying operations research to airline scheduling, revenue management, healthcare, sports, infrastructure, transportation marketplaces and other practical problems. The citations also emphasize teaching, mentoring, publications, open-source software, institutional leadership and sustained service.

INFORMS said the collective impact of the 2026 class extends beyond individual research through students and professionals mentored, organizations advised and professional communities strengthened. Ali E. Abbas, a professor in the Department of Industrial and Systems Engineering at the University of Southern California, is being recognized for distinguished contributions to decision analysis, including influential papers and textbooks.

The citation also credits him with serving as a public spokesperson for the profession and leading work studying and applying ethical decision-making. Jose H. Blanchet, a professor in Stanford University’s Management Science & Engineering Department, is honored for contributions to applied probability and stochastic systems.

His recognized work includes rare-event simulation, exact sampling and the use of optimal transport in distributionally robust optimization, together with sustained service to the community. Timothy C.Y. Chan, an associate vice president and vice provost for strategic initiatives and a professor in the Department of Mechanical & Industrial Engineering at the University of Toronto, is recognized for contributions to inverse optimization.

INFORMS also cites his applications of operations research in healthcare and sports, along with an exceptional record of educational innovation, student mentorship and service. Oktay Günlük, a professor of industrial and systems engineering at Georgia Tech, is honored for fundamental contributions to integer optimization and technical leadership in large-scale industrial optimization applications. His citation additionally recognizes service to INFORMS and to the broader optimization community.

Industry practice is represented prominently by Timothy L. Jacobs, vice president of Applied AI and Machine Learning at Walmart. INFORMS says he advanced operations research practice by developing and deploying optimization systems used in airline scheduling, revenue management, air logistics and transportation marketplaces.

The association states that these efforts delivered billions in documented industry impact. Robin Lougee, founder and principal of Lougee Decision Intelligence, is being recognized for practical applications of operations research and advanced analytics models across diverse industries. He is also honored for developing and promoting open-source optimization through COIN-OR and for sustained professional service to INFORMS.

Elise Miller-Hooks, the Bill and Eleanor Hazel Endowed Chair in Infrastructure Engineering and department chair at George Mason University, is recognized for contributions to stochastic and dynamic network optimization. Her citation also covers transportation systems and infrastructure resilience, and associates her work with disaster preparedness, sustainable infrastructure and transportation practice. Johannes O.

Royset, a professor in the Daniel J. Epstein Department of Industrial and Systems Engineering at the University of Southern California, is honored for extensive contributions to optimization under uncertainty through innovative research, expository books and tutorials. INFORMS additionally cites his applications of operations research techniques to important practical problems and his service to the association.

Huseyin Topaloglu, a professor in Cornell University’s School of Operations Research and Information Engineering, is recognized for deep theoretical advances in operations management, particularly in revenue management and assortment optimization. The citation also credits him with a transformative impact on industry practice, distinguished teaching and dedicated service to the profession. Amy R.

Ward, the Rothman Family Professor of Operations Management and Charles M. Harper Faculty Fellow at the University of Chicago, is honored for distinguished service to INFORMS publications and the Applied Probability Society. Her recognized scholarly contributions lie at the intersection of stochastics and operations management, particularly in queueing theory.

Yuehwern Yih, the Tompkins Distinguished Professor at Purdue University’s Edwardson School of Industrial Engineering, is being recognized for pioneering contributions to healthcare engineering and humanitarian logistics. INFORMS also cites his advancement of the deployment of operations research in complex real-world environments and associates that work with global societal impacts. Bert Zwart, group leader and professor of stochastics at the Center for Mathematics & Computer Science in Amsterdam and a professor at Eindhoven University of Technology, is honored for sustained fundamental contributions to applied probability.

The citation singles out his analysis of heavy-tailed stochastic systems and their applications to computer, communication and electricity networks. INFORMS CEO and executive director Elena Gerstmann said the class reflects the best of the field through rigorous science, real-world impact and a commitment to the people and community behind it. In her announcement, she said Fellows across theory and practice, academia and industry have helped make better decisions while strengthening the profession and the organization through leadership, mentorship and service.

The announcement itself does not present a new experiment, clinical trial, product evaluation or policy assessment. Instead, it documents the specific work, applications, institutional roles and professional contributions for which each of the 12 people was selected. Within the class, the technical methods range from decision analysis and integer optimization to inverse optimization, stochastic systems, rare-event simulation, exact sampling, optimal transport, distributionally robust optimization, optimization under uncertainty, queueing theory, revenue management, assortment optimization and dynamic network optimization.

The application areas likewise vary, encompassing healthcare delivery and healthcare engineering, sports, humanitarian logistics, disaster preparedness, sustainable infrastructure, industrial operations, airline scheduling, air logistics, transportation marketplaces, and computer, communication and electricity networks. INFORMS said its membership network includes more than 12,000 people spanning academia, industry and government. The 2026 Fellows announcement therefore represents a cross-section of the field, combining lifetime research achievement with implementation, education, mentorship and organizational service rather than a single unified scientific finding.

Subject of Research: INFORMS recognition of 12 leaders in operations research, analytics and decision science

Article Title: INFORMS names 12 operations research and analytics leaders as 2026 Fellows

Article References: INFORMS names 12 operations research and analytics leaders as 2026 Fellows. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: operations research, INFORMS Fellows, optimization, stochastic systems, decision analysis, revenue management, network optimization, healthcare engineering, applied probability, transportation systems, INFORMS, names

Williams is an internationally recognized leader in stochastic systems whose achievements have advanced operations research, applied probability and the mathematical sciences. Her research has also extended the reach and impact of operations research into telecommunications, manufacturing and biology. INFORMS said these contributions have enriched the broader OR/MS community through research with far-reaching applications, distinguished professional service and a career-long commitment to developing and supporting future generations of scholars.

The recognition also reflects Williams’s service across the mathematical sciences. She has held numerous leadership and service roles, including a term as president of the Institute of Mathematical Statistics. In a statement announcing the award, INFORMS President Mark Lewis said Williams’s contributions to stochastic systems have given researchers across disciplines new tools for understanding complexity. He also emphasized how she has shared her knowledge as a mentor, advocate and role model for scholars who will define the next era of the field.

Williams has influenced and supported students, postdoctoral scholars and faculty, particularly women pursuing careers in mathematics, probability and operations research. Her mentorship and advocacy have been central to advancing opportunities for future generations of scholars and building a stronger professional community. Lewis said the award recognizes not only Williams’s exceptional career but also the community she has helped create through those efforts.

Williams’s academic and professional standing has been recognized through election to the U.S. National Academy of Sciences and the American Academy of Arts and Sciences, as well as election as a Corresponding Member of the Australian Academy of Science. She is a Fellow of INFORMS, SIAM, the Institute of Mathematical Statistics, the American Mathematical Society and the American Association for the Advancement of Science. Her honors include the INFORMS John von Neumann Theory Prize, the 2017 INFORMS Award for the Advancement of Women in Operations Research and the Management Sciences, and honorary doctorates from the University of Melbourne and La Trobe University. INFORMS is an international association for professionals and students in operations research, AI, analytics, data science and related disciplines, reporting a network of more than 12,000 members across academia, industry and government.

Subject of Research: Recognition of Ruth J. Williams for stochastic-systems research, professional service and mentorship

Article Title: Ruth J. Williams honored with 2026 INFORMS President’s Award

Article References: Ruth J. Williams honored with 2026 INFORMS President’s Award. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: Ruth J. Williams, INFORMS, President’s Award, stochastic systems, operations research, applied probability, mathematical sciences, mentorship, professional service, Ruth, Williams, honored

Jacobson, a Founder Professor at the University of Illinois Urbana-Champaign, has been an INFORMS member since 1985. His prior organizational service includes serving as Board of Directors treasurer and as a member of the Fellows Selection Committee, in addition to other leadership roles. He is a recipient of the INFORMS George E. Kimball Medal, Impact Prize and President’s Award. INFORMS reports that Jacobson has authored more than 210 peer-reviewed articles and 670 publications, including contributions to outlets such as The Washington Post, The Chicago Tribune and The Hill.

Behzad, who will serve as Treasurer, is chair and professor of information systems at the College of Business at California State University, Long Beach. An INFORMS member since 2010, she has chaired the Professional Recognition Committee, served on the Finance Committee and previously served as president of Women in OR/MS. She is a recipient of the INFORMS Moving Spirit for Forums Award and the President’s Award for Outstanding Faculty Achievement at Cal State Long Beach. Proano, the incoming Vice President of Education, is associate professor and graduate director in the Department of Industrial and Systems Engineering at Rochester Institute of Technology. He has been an INFORMS member since 2007, previously served as president of the Minority Issues Forum and currently serves on the Strategic Planning Committee. He is a 2025 Minority Issues Forum Fellow and an INFORMS Senior Member.

Shmoys, elected Vice President of Emerging Fields and Technologies, is the Laibe/Acheson Professor and director of the Center for Data Science for Enterprise & Society at Cornell University. He has been an INFORMS member since 1995 and has served on the INFORMS AI Ad Hoc Committee, NSF Liaison Committee and U.S. Federal Research Engagement Committee. His listed distinctions include the INFORMS Kimball Medal, Optimization Society Khachiyan Prize, Wagner Prize and Lanchester Prize, and he was named an AAAS Fellow in 2024. Ekşioğlu, the incoming Vice President of Meetings, is the Hefley Professor in Logistics and Entrepreneurship in the Department of Industrial Engineering at the University of Arkansas. An INFORMS member since 1997, she has served as president of the Energy, Natural Resources, and the Environment Section and chaired the Railway Applications Section. She is an INFORMS Senior Member, an IISE Fellow and a recipient of the INFORMS Moving Spirit Award for Chapters.

Kazaz, elected Vice President of Publications, is the Becker Professor of Supply Chain Management and director of the Brethen Institute at Syracuse University. He has been an INFORMS member since 1995, has served on the INFORMS Publication Committee since 2022 and chaired the Committee for AI Guidelines. His listed honors include the MSOM Society Distinguished Award in 2026, the Wickham Skinner Prize and the INFORMS President’s Pick for best publication. Hollywood, elected Vice President of Sections and Societies, is a senior operations researcher at the RAND Corporation and previously taught strategic management, systems engineering and predictive analytics at the RAND School of Public Policy. He previously directed the National Institute of Justice’s Information and Geospatial Technologies Center, operated by RAND under a cooperative agreement, and managed RAND’s Center for Quality Policing. He is also a long-time member of the INFORMS Subdivisions Council and was founding president of the INFORMS Public Programs & Processes Section, now part of the Public and Societal Operations Research Section.

The newly elected board members will join a group of returning members that includes Wedad Elmaghraby as President, Mark Lewis as Past-President, Pelin Pekgün as Secretary, Tinglong Dai as Vice President of Marketing, Communications and Outreach, Pooja Dewan as Vice President of Practice, Gina Galindo as Vice President of International Activities, Anahita Khojandi as Vice President of Membership and Professional Recognition, and Les Servi as Vice President of Chapters and Forums. Elena Gerstmann will continue as Chief Executive Officer and Executive Director. The announcement concerns leadership selection and planned board service beginning in 2027; it does not report new scientific findings, experimental results or changes to INFORMS policies.

Subject of Research: INFORMS leadership election for the board term beginning in 2027

Article Title: INFORMS announces 2026 Board of Directors Election results

Article References: INFORMS announces 2026 Board of Directors Election results. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: INFORMS, board election, operations research, professional association, organizational leadership, education, publications, emerging technologies, sections and societies, announces, Board, Directors

The book examines the Traveling Salesman Problem, a mathematical challenge that asks how to identify the shortest route visiting a set of locations and returning to the starting point. The question may appear simple, but the difficulty of finding optimal solutions increases substantially as the number of locations grows. The problem has important connections to transportation, logistics, manufacturing and other applications requiring efficient routing and resource allocation. Traub and Vygen address multiple variations of the problem rather than presenting the Traveling Salesman Problem as a single scenario, bringing together advances in approximation algorithms for these related formulations.

INFORMS said the book does more than provide a comprehensive review of existing research. It introduces original contributions, strengthens previous results and presents sophisticated mathematical techniques with exceptional clarity. By unifying advances across multiple variations of the Traveling Salesman Problem, the authors have created a resource intended for researchers, educators and students. The recognition concerns both the technical content of the publication and its accessibility: the announcement specifically credited the book with making complex optimization methods accessible to future generations.

Traub is an associate professor in the Department of Computer Science at ETH Zurich and a mathematician and theoretical computer scientist whose research includes approximation algorithms and combinatorial optimization. Vygen is a professor of discrete mathematics at the University of Bonn whose extensive contributions to combinatorial optimization include applications in chip design and vehicle routing. Their work addresses a central problem in operations research while also reflecting their respective backgrounds in theoretical computer science, discrete mathematics and combinatorial optimization.

The Lanchester Prize is named for Frederick W. Lanchester, a British engineer whose pioneering use of mathematical models helped lay the foundations for modern operations research. INFORMS describes the 2026 award as recognition for an exceptional published contribution that advances both the theory and practice of the discipline. The announcement did not claim that the book resolves the Traveling Salesman Problem in every case, nor did it specify a guaranteed level of route quality. Its stated contributions were the original material, the strengthening of previous results, the unification of advances across different variations of the problem and the clear presentation of sophisticated mathematical methods. The prize therefore honors a published work that advances research in approximation algorithms and combinatorial optimization while serving as a teaching and reference resource for the field.

Subject of Research: A prize-winning book on approximation algorithms for Traveling Salesman Problems

Article Title: 2026 INFORMS Lanchester Prize honors groundbreaking book on the Traveling Salesman Problem

Article References: 2026 INFORMS Lanchester Prize honors groundbreaking book on the Traveling Salesman Problem. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: INFORMS, Lanchester Prize, Traveling Salesman Problem, approximation algorithms, combinatorial optimization, operations research, routing, theoretical computer science, mathematics, ETH Zurich, University of Bonn, Lanchester

Named in honor of George E. Kimball, a pioneer in operations research, the medal is one of INFORMS’ most distinguished service honors. According to the association, it recognizes individuals whose volunteer leadership and dedication have strengthened the profession, supported its community and advanced INFORMS’ mission. INFORMS describes itself as the world’s largest association for professionals and students in operations research, analytics, artificial intelligence and data science. Its network includes more than 12,000 members spanning academia, industry and government. The awards place Cochran and Yücesan alongside a service honor intended to recognize sustained contributions to the professional community rather than a single research result.

Cochran, a professor at the University of Alabama, has served INFORMS and the operations research and analytics profession for nearly three decades. A defining contribution in his record was his leadership as general chair of the 2005 INFORMS Annual Meeting, which had been scheduled for New Orleans. After Hurricanes Katrina and Rita devastated the city, Cochran oversaw the meeting’s relocation to San Francisco in less than 10 weeks. He has also worked to develop educational programs, professional initiatives and collaborative communities that expand access to operations research, particularly in developing nations and underserved regions.

Cochran co-founded Statistics Without Borders and INFORMS Pro Bono Analytics, connecting analytics expertise with humanitarian, public-health and educational challenges. His service has included editing INFORMS Transactions on Education and leading development of the eight-volume Encyclopedia of Operations Research and Management Science. These efforts span teaching, scholarly communication, professional participation and the use of analytical methods in public-service settings. INFORMS said they have strengthened the profession and broadened public understanding of operations research, analytics and statistics. The medal citation therefore encompasses not only his professional leadership within INFORMS but also efforts to build collaborative networks and connect specialized expertise with wider social needs.

Yücesan, a professor at INSEAD, was recognized for sustained service through scholarly leadership, professional service and efforts to broaden the field’s reach. His editorial work at Management Science, the INFORMS Journal on Computing and IISE Transactions helped shape scholarship in simulation and stochastic modeling. He also contributed through leadership in the INFORMS Simulation Society and the Winter Simulation Conference, where his work helped extend the conference’s international reach into Asia. As program co-chair of the 2022 INFORMS Annual Meeting, he helped improve the meeting’s structure and accessibility. INFORMS CEO and Executive Director Elena Gerstmann said Cochran and Yücesan exemplify the extraordinary impact dedicated volunteers can have on a professional community. Their leadership, generosity and commitment to service, she said, created opportunities for others, strengthened professional connections and extended the reach of operations research around the world.

Subject of Research: Awards recognizing long-term service to the operations research profession

Article Title: INFORMS awards 2026 George E. Kimball Medal to James J. Cochran and Enver Yücesan

Article References: INFORMS awards 2026 George E. Kimball Medal to James J. Cochran and Enver Yücesan. Original publication (Bibliographic details unavailable.)

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DOI: Not provided

Keywords: INFORMS, operations research, George E. Kimball Medal, James J. Cochran, Enver Yücesan, analytics, professional service, education, publishing, international collaboration, awards, George

INFORMS said the award recognizes fundamental, sustained contributions that have stood the test of time, using significance, innovation, depth and scientific excellence as its criteria. Ben-Tal is an internationally recognized expert in continuous optimization whose technical work has extended across robust optimization, convexity theory, duality, optimal decision-making under uncertainty, structural design, semidefinite optimization and conic optimization. Ben-Tal helped create robust optimization through a long series of papers and a monograph written with Arkadi Nemirovski and Laurent El Ghaoui.

According to the announcement, his contributions span the field’s core theory, including tractability, and extended the area from static linear problems to multistage decision-making. He has also worked on applications of robust optimization in machine learning. INFORMS Chief Executive Officer and Executive Director Elena Gerstmann said Ben-Tal’s work on robust optimization was among the field’s most consequential contributions because it gave operations research a rigorous framework for making sound decisions when data are uncertain. “He didn’t just advance an area of research.

He helped create one, and the field has been building on it ever since,” Gerstmann said. Beyond robust optimization, Ben-Tal has conducted foundational work in convexity theory, duality and optimal decision-making under uncertainty. He also contributed to structural design by helping develop the theory and computational toolbox for semidefinite and conic optimization.

The announcement describes these achievements as influential, long-running contributions to operations research and the management sciences rather than as results of a single study. This is an award announcement based on the published body of research and the association’s assessment of its significance; it does not report a new experiment, clinical trial or fresh quantitative evaluation. Ben-Tal’s connection to the field has included teaching, student supervision, editorial work and professional service in addition to research.

The prize will be presented at the 2026 INFORMS Annual Meeting in San Francisco, scheduled for November 1–4, 2026. It includes a $5,000 cash prize, a medal and a formal citation. INFORMS, which describes itself as the world’s largest association for professionals and students in operations research, AI, analytics, data science and related disciplines, has more than 12,000 members across academia, industry and government.

Subject of Research: Recognition of Aharon Ben-Tal’s sustained contributions to robust optimization and operations research theory

Article Title: Aharon Ben-Tal awarded 2026 INFORMS John von Neumann Theory Prize

Article References: Aharon Ben-Tal awarded 2026 INFORMS John von Neumann Theory Prize. Original publication (Bibliographic details unavailable.)

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DOI: Not provided

Keywords: Aharon Ben-Tal, INFORMS, John von Neumann Theory Prize, robust optimization, operations research, continuous optimization, convexity theory, duality, multistage optimization, machine learning, semidefinite optimization, Aharon

The proposed intervention is known as a targeted priority mechanism, and its intellectual ancestor already operates in Europe. The Eurotransplant Senior Program demonstrated that kidneys from older donors, which younger recipients and transplant centers often decline, can produce excellent outcomes when directed to older recipients whose expected remaining lifespan better matches the organ’s expected functional years. The American version studied by the Virginia Tech team would be voluntary rather than mandatory. Waitlisted candidates aged 65 or older could choose to enroll, agreeing in advance to accept so-called marginal kidneys, such as those from older adults, in exchange for priority. Enrollment moves a patient to the front of the queue for that specific pool of harder-to-place organs, while the patient gives up access to kidneys outside the group. The trade is explicit, transparent, and entirely in the hands of the patient.

Sait Tunc, assistant professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech, who led the multi-institutional research team, emphasized that the mechanism works through incentives rather than coercion. According to Tunc, the design encourages more efficient matching without forcing patients to accept organ offers or requiring a complete overhaul of the existing allocation infrastructure. In his characterization, the program benefits not only those who enroll but also younger patients who remain outside it, because higher-quality organs can then be better matched to the recipients with the greatest post-transplant life expectancy. He described the outcome as close to a win-win situation, a rare property in allocation problems where prioritizing one group typically comes at another group’s expense.

The scale of inefficiency in the current system is striking. Roughly 20 percent of donated kidneys never find a recipient at all. The mismatch arises at several points in the pipeline: organs are paired poorly with patients, transplant centers decline offers that could have been used, and individual providers apply acceptance heuristics shaped by their own clinical experience, including assumptions and biases that may or may not reflect the underlying data. Matthew J. Ellis, medical director of the Kidney and Pancreas Transplant Programs at Duke University School of Medicine and a co-author of the study, explained that providers making acceptance decisions often rely on rules of thumb grounded partly in concrete evidence and partly in institution-specific judgment. The consequence, he noted, is that many usable kidneys are never transplanted, deepening the supply-and-demand misalignment that defines American organ transplantation.

Why does this misalignment happen in the first place? A donated kidney is not a standardized commodity. Its expected years of remaining function vary with donor age, medical history, and retrieval circumstances, and the value of any given kidney depends critically on who receives it. A kidney projected to function for ten years offers modest benefit to an 80-year-old candidate but potentially decades of dialysis-free life to a 40-year-old. Conversely, a kidney projected to function for seven years may be entirely adequate for an older recipient who would otherwise spend those years on dialysis, where five-year survival remains dramatically worse than after transplantation. When allocation ignores these complementarities, the result is a double failure: marginal kidneys are declined for younger patients and never rerouted to older ones, while younger patients wait longer for organs whose longevity they cannot fully exploit.

The technical core of the new study is a hybrid modeling framework. The researchers first built mathematical descriptions of patient choices, capturing how waitlisted candidates weigh the trade-off between waiting time and organ quality when deciding whether to enroll in a priority program. These choice models were then embedded in a sophisticated computer simulation of the U.S. kidney allocation system, trained with machine learning and predictive models on historical transplantation data. The simulation does not merely track organs through queues; it predicts how offer acceptance behavior and program participation would shift under the new rules, allowing the team to test policy variants before any real-world pilot. By iterating over enrollment scenarios and priority structures, the researchers could estimate equilibrium outcomes, in which patients’ enrollment decisions and centers’ acceptance behavior settle into a stable pattern that the simple static projections of earlier studies could not capture.

The projected impact on individual patients is dramatic. Under the simulated program, older participants who currently face a multiyear wait could receive a transplant within a few months or less. Earlier transplantation matters because time on dialysis is itself a health hazard: each additional waiting year is associated with accumulating frailty, cardiovascular damage, and reduced post-transplant survival. Moving elderly candidates off dialysis faster, even with organs of moderate expected longevity, compounds into a meaningful mortality reduction at the population level, which is how the team arrived at its estimate of more than 190 deaths prevented annually. Just as importantly, the simulation showed no detectable harm to non-participants. Because enrolled older patients absorb the marginal organs that would otherwise be discarded, kidneys with longer expected function become available to younger and healthier candidates, improving their match quality as well.

This result challenges a long-standing intuition in organ policy, namely that priority for one group must necessarily disadvantage others. When the scarce resources in question are heterogeneous and preferences are correlated with recipient characteristics, properly designed priority rules can reallocate organs toward the recipients who value them most, expanding the effective supply rather than merely redistributing it. The flexibility of the mechanism is central to this outcome. Unlike top-down mandates, which transplant centers might resist or game, the voluntary structure lets patients self-select based on their own risk tolerance and life circumstances, and lets the program rules be tuned so that the incentive gradient, the reward for accepting a marginal kidney, is strong enough to shift behavior without distorting clinical judgment.

The research was supported by Tunc’s 2024 National Science Foundation CAREER Award, one of the foundation’s most competitive grants for early-career faculty. Beyond Tunc, the author team spans industrial engineering, clinical transplant medicine, and operations management: Ruochen Wang, a former Virginia Tech doctoral student now a postdoctoral fellow at the Queen’s University Smith School of Business; Matthew J. Ellis of Duke; Bekir Tanriover, chief of the Division of Nephrology and professor of medicine at the University of Arizona College of Medicine in Tucson; and Burhan Sandikci, dean and professor in the Faculty of Management at Istanbul Technical University. That composition reflects the study’s dual identity as an operations research contribution and a clinically grounded policy proposal, a balance the authors regard as essential for moving from simulation to implementation.

The team is candid that publication in Management Science is a beginning rather than an endpoint. Tunc noted that the article serves to bring discussions into the community and establish that the work is theoretically rigorous, but that the next step is to carry it to the transplant community itself. A second phase of the research will focus on initial policy conversations with transplant professionals and on practical program designs that clinicians and patients can evaluate in concrete terms. The ultimate pathway to clinical practice runs through clinically oriented journals and working groups within the United Network for Organ Sharing, the nonprofit organization that administers the U.S. organ transplantation system. If those discussions succeed, the study’s central promise, that hundreds of discarded kidneys and hundreds of lives could be reclaimed each year through smarter incentives rather than new supply, may move from simulation to surgical suites within the decade.

Subject of Research: Kidney transplant allocation redesign using a voluntary targeted priority mechanism for older donor organs and recipients

Article Title: A new approach to kidney matching could save lives

Article References: A new approach to kidney matching could save lives. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: kidney transplantation, organ allocation, targeted priority mechanism, Eurotransplant Senior Program, Management Science, Virginia Tech, machine learning simulation, donor organ shortage, transplant waiting list, older donor kidneys, health policy, operations research

The review, conducted by Yuankai Zhang, Wei-Hua Lin, Qiong Tian and Xiangpei Hu, synthesizes nearly two decades of scholarship on business-to-consumer online order fulfillment. The authors systematically searched the literature covering work published from 2007 through May 2026. Their search initially surfaced 1,495 records, which they filtered down to 198 articles selected for detailed analysis. Rather than organizing the literature by academic subfield or methodology, the team anchored their classification to the real-world fulfillment process itself: order assignment, warehouse operations, shipping and delivery, and return operations. This process-oriented structure, the authors argue, mirrors the way decisions actually unfold inside an e-commerce company, making the review useful both as a scholarly reference and as a practical roadmap for operations managers.

The starting point of the chain is order assignment, the moment a retailer decides where each item in an order will be fulfilled. In modern e-commerce, this is rarely a trivial question. Large retailers operate networks with multiple fulfillment locations, including national distribution centers, regional warehouses and, increasingly under omni-channel models, physical stores that double as miniature fulfillment hubs. Assigning an order to the wrong node can mean longer delivery distances, higher shipping costs, split shipments that frustrate customers, or stock imbalances that leave popular items stranded in the wrong part of the network. The review shows that research on this stage has grown steadily more sophisticated as retailers’ networks have grown more distributed and as omni-channel strategies have blurred the traditional boundary between stores and warehouses.

Once an order lands at a fulfillment location, warehouse operations take over. This stage encompasses the physical heart of e-commerce: receiving inventory, storing it, picking the specific items a customer ordered, packing them and staging them for shipment. The review highlights why this stage is uniquely difficult in the online retail context. Unlike traditional retail, where a store sells a curated local assortment, B2C fulfillment must cope with very large product assortments, often spanning hundreds of thousands of stock-keeping units. Compounding the challenge, customer orders are typically personalized multi-item baskets rather than uniform bulk shipments, which means picking operations must handle enormous variety at high speed. The reviewed literature examines how these pressures shape decisions about warehouse layout, picking routing, batching of orders, zoning, and the deployment of automation, all of which interact with the upstream assignment decisions that determine what arrives at the warehouse floor in the first place.

The third stage, shipping and delivery, is where the retailer’s promises meet physical reality. Customers increasingly expect tight delivery time windows, sometimes measured in hours rather than days, and the review identifies these compressed windows as one of the four defining characteristics that make B2C fulfillment especially challenging. The other three are the very large product assortments already noted, the multi-location nature of fulfillment networks, and the personalized, multi-item composition of individual orders. Together, these four features distinguish online retail fulfillment from classical logistics problems and explain why the field has generated such a large and specialized body of research. Decisions about carrier selection, shipment consolidation, routing and delivery scheduling must all be made under these constraints, and each of them is constrained in turn by what happened, or failed to happen, in the warehouse hours earlier.

The final stage, return operations, is often treated as an afterthought in popular discussions of e-commerce, yet the review gives it full status as an integral part of the fulfillment process. Returns are a structural feature of online retail, not an anomaly: customers cannot physically inspect goods before buying, so a certain fraction of orders will always flow backward through the network. Handling those returns involves decisions about reverse logistics, inspection, restocking, refurbishing and disposition, and those decisions feed back into inventory positions and fulfillment capacity. The authors emphasize that the four stages they map are not silos but a connected system, and their review is structured to make those connections visible rather than burying them between disciplinary boundaries.

“B2C order fulfillment is not a collection of separate tasks. Decisions made at one stage can create consequences at another,” says Zhang. “By viewing the process as a connected system, we can better identify where coordination, data and new technologies can improve fulfillment decisions.” This systems perspective is the review’s central intellectual contribution. Much of the earlier literature, the authors find, examined individual stages in isolation: a paper might optimize picking routes without considering how order assignment shaped the pick list, or model delivery scheduling without accounting for warehouse cut-off times. The trend in recent research, and the direction the review charts for the future, is toward models that capture these interdependencies explicitly, coordinating decisions across the entire chain rather than optimizing each link separately.

That trend is closely tied to a second shift the review documents: the move from static planning toward integrated, real-time decision-making. Traditional fulfillment planning often assumed stable demand patterns and relatively long planning horizons, allowing decisions to be made in advance and executed as scheduled. Modern e-commerce operates at a tempo that makes such static assumptions increasingly untenable. Order streams arrive continuously, inventory positions change by the minute, and delivery promises are made dynamically at the moment of purchase. The reviewed literature shows researchers responding with approaches that update decisions in real time as new information arrives, embedding fulfillment logic directly into the customer-facing transaction rather than treating it as a back-office batch process.

Underpinning both shifts is the rapid maturation of data analytics and artificial intelligence. The team examined how data analytics, AI and emerging generative AI can support forecasting, scenario generation and operational decision-making across the fulfillment process. Demand forecasting, long a staple of operations research, is being augmented by machine learning models that can exploit far richer behavioral data, which the review identifies as a promising direction under the heading of behavioral analytics. Generative AI, meanwhile, opens possibilities for scenario generation, allowing planners to stress-test fulfillment networks against synthetic but plausible demand patterns. Importantly, the authors frame these technologies not as replacements for classical operations research. “Rather than replacing optimization methods, these technologies are increasingly viewed as decision-support tools that can work alongside optimization,” Zhang adds. In this hybrid vision, machine learning supplies predictions and scenarios, while optimization translates them into concrete, constraint-respecting operational plans.

The review also points toward several forward-looking research frontiers beyond real-time integration and AI. Decision-making under uncertainty remains a persistent challenge, since fulfillment networks must cope with demand spikes, supply disruptions and weather-related delivery delays that no forecast fully anticipates. Sustainability is flagged as a growing concern, encompassing the emissions footprint of expedited shipping, packaging waste and the energy profile of large-scale warehouse automation. And the authors highlight the potential of combining artificial intelligence with digital twins and optimization: virtual replicas of fulfillment networks that can be continuously updated with real operational data and used to test decisions before they are executed in the physical world. For managers, the practical message of the review is to stop evaluating warehouse, delivery and returns decisions in isolation and instead instrument and optimize the entire chain. For researchers, it is a call to build models that match the connected, data-saturated, time-critical reality of modern online retail. For the millions of shoppers who will click “Place Order” today without a second thought, it is a reminder that behind that single click lies a cascade of interlocking decisions, and a research community working to make that cascade faster, cheaper and more responsive.

Subject of Research: B2C e-commerce order fulfillment decision-making across order assignment, warehouse operations, delivery and returns

Article Title: From click to doorstep: the hidden decisions behind e-commerce orders

Article References: From click to doorstep: the hidden decisions behind e-commerce orders. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: e-commerce, order fulfillment, supply chain, warehouse operations, logistics, omni-channel retail, artificial intelligence, optimization, digital twins, returns management, systematic review, operations research

The research team, led by Abadal-Salam T. Hussain of Al-Kitab University in Kirkuk, Iraq, together with colleagues spanning institutions in Iraq, India and Oman, answers both questions with a unified differential-equation and operations-research framework. The framework wraps six representative engineering plants in a single proportional–integral–derivative (PID) evaluation harness: a separately excited DC servomotor, a mass–spring–damper stage, a stirred heat exchanger, a continuously stirred tank reactor, a series RLC circuit, and the famously unstable inverted pendulum on a cart. Alongside these dynamic systems sit three constrained mechanical-design benchmarks drawn from the operations-research literature and the ten-dimensional Rastrigin function, a standard testbed riddled with local minima. By forcing every solver through the same harness, the authors remove the apples-to-oranges comparisons that have long plagued the optimization literature.

The scale of the comparison is unusual for this field. Seven long-established metaheuristics—the genetic algorithm, particle swarm optimization, differential evolution, simulated annealing, the grey wolf optimizer, the whale optimization algorithm, and the sine–cosine algorithm—were run over fifteen seeded repetitions at a budget of exactly 3000 objective evaluations. Crucially, that budget was enforced by an evaluation counter rather than by nominal population and iteration settings, so trajectory-based solvers such as simulated annealing and population-based solvers such as particle swarm optimization were treated identically. This seemingly technical detail matters enormously: many published comparisons inadvertently give some algorithms far more function evaluations than others, making rankings meaningless. Four non-metaheuristic baselines, a hyperparameter sensitivity sweep, and two ablation studies were then deployed to interrogate the framework itself rather than only the solvers inside it.

The headline finding is that the solver ranking reverses across problem classes, a result with immediate practical consequences for anyone choosing an optimizer. On the PID control landscapes, differential evolution earned the best Friedman rank of 1.52 out of 7, while the sine–cosine algorithm fared worst at 6.44. Yet on the multimodal Rastrigin function that ordering inverts almost completely: the sine–cosine algorithm took first place with a rank of 1.20, the whale optimization algorithm followed at 2.07, and differential evolution slumped to 5.60. On the constrained mechanical-design problems, the grey wolf optimizer and differential evolution dominated instead. No single algorithm wins everywhere, and the pattern of wins and losses is systematic rather than random noise, which is precisely what a well-constructed benchmark should expose.

Perhaps the most provocative result concerns what the baselines revealed about why these reversals occur. On the smooth PID surfaces, a humble multi-start Nelder–Mead search—a classical simplex method from 1965—matched every metaheuristic to five decimal places. In other words, on well-behaved control-tuning problems, the expensive nature-inspired machinery buys robustness rather than accuracy: it reaches the same optimum more reliably across random seeds, but it does not find anything better. By contrast, classical Ziegler–Nichols tuning, still taught in every control course, proved 8.9 to 19.8 times worse than the optimized solutions. And once constraints or multimodality entered the picture, no purely local method remained competitive at all. The framework therefore does more than rank solvers; it identifies which classes of problems actually justify deploying a metaheuristic in the first place.

This distinction between robustness and accuracy reframes a debate that has simmered for decades. Critics of metaheuristics have long argued that swarm and evolutionary algorithms are elaborate ways to reinvent hill climbing, while proponents point to hard real-world problems where classical methods fail. The new results suggest both camps are partly right, depending on the landscape. For smooth, low-dimensional, essentially unimodal objectives of the kind that arise when tuning a PID loop around a linearized plant, a multi-start local search is statistically indistinguishable from the fanciest metaheuristic. For rugged, constrained, or high-dimensional objectives—structural design with stress limits, or a ten-dimensional Rastrigin surface—the population-based diversity of evolutionary and swarm methods becomes genuinely load-bearing. The practical advice for engineers is not to pick a favorite algorithm but to diagnose the objective first.

The methodological rigor underpinning these claims deserves attention in its own right. Fifteen seeded runs per solver per problem allow nonparametric statistical ranking through the Friedman test, which compares algorithms by their average ranks across problems rather than by raw scores that may have incomparable scales. The exact evaluation budget neutralizes the most common confound in solver comparisons. The hyperparameter sensitivity sweep addresses the perennial worry that an algorithm’s performance reflects its tuning rather than its intrinsic character, and the ablation studies probe which components of the framework drive its conclusions. Releasing the whole pipeline as a fully specified, reproducible package means other researchers can extend the comparison to new solvers or new plants without reconstructing the harness from scratch—a quiet but significant contribution to a field often criticized for irreproducible experiments.

The six plants were chosen to span the breadth of engineering dynamics, and each stresses the evaluation harness differently. The DC servomotor and the mass–spring–damper stage are classical linear systems where smooth objectives dominate. The stirred heat exchanger and the continuously stirred tank reactor introduce thermal and chemical dynamics with stronger nonlinearities. The series RLC circuit adds electrical resonance, while the inverted pendulum contributes an inherently unstable equilibrium that punishes any tuning error brutally. Wrapping all of them in one PID harness means the same three gains must be found for wildly different dynamics, which isolates the effect of the landscape on solver performance. Combined with the constrained design benchmarks and the Rastrigin function, the suite covers the smooth, constrained, and multimodal regimes that the baselines then dissect.

For the wider engineering community, the implications reach beyond control theory. Structural optimization, power-system dispatch, and machine-learning hyperparameter search all share the same structure: an expensive, gradient-free, possibly non-smooth objective wrapped around an inner simulation. The finding that rankings reverse across problem classes warns against the common practice of validating a new metaheuristic on a handful of toy functions and claiming general superiority. It also cautions journals and reviewers to demand evaluation-count parity and multi-problem statistical testing before accepting performance claims. The framework offers a template for how such evidence should be gathered, and its open release lowers the barrier for others to comply.

There is also a sobering message embedded in the numbers for the field’s publication culture. With hundreds of new nature-inspired metaheuristics proposed each year, often named after animals or physical phenomena, rigorous head-to-head evidence of practical advantage has been scarce. This study’s demonstration that a decades-old simplex method can match seven celebrated algorithms on smooth control landscapes, while classical Ziegler–Nichols tuning lags nearly an order of magnitude behind, cuts in two directions at once: it deflates exaggerated claims for novel optimizers on easy problems, and it simultaneously vindicates optimization itself, showing that proper automated tuning delivers dramatic gains over hand-tuned classical methods. The value lies in knowing which situation you are in—and this framework, for the first time, gives engineers a unified, reproducible way to find out before committing computational budget or design decisions to any particular algorithm.

Subject of Research: Benchmarking metaheuristic optimization algorithms for engineering systems using a unified differential-equation and operations-research framework

Article Title: A unified differential-equation and operations-research framework for benchmarking metaheuristic optimization of engineering systems

Article References: Hussain, A.-S. T., Almulaisi, T., Al-Aabdeen, V. A. Z., Shah, P., Al-Tuhafi, M. M., Fadhil, H. A., Sekhar, R., & Radhi, A. D. (2026). A unified differential-equation and operations-research framework for benchmarking metaheuristic optimization of engineering systems. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-026-02541-8

Image Credits: AI Generated

DOI: 10.1007/s40747-026-02541-8

Keywords: metaheuristic optimization, PID control, differential evolution, benchmarking, engineering systems, operations research, differential equations, Rastrigin function, grey wolf optimizer, statistical ranking, reproducibility, control engineering

The research, led by William Kirschenman, who completed the work as a Ph.D. student at NC State and is now an assistant professor at the Naval Postgraduate School, appears in two peer-reviewed journals: Naval Research Logistics and Omega. For Kirschenman, the problem is not an abstract puzzle. As a former company commander, he experienced firsthand how the order and placement of equipment can determine whether a mission succeeds. Wargaming amphibious landings, he says, revealed just how difficult it becomes to keep a unit organized in the sequence that best supports the mission when the environment is chaotic or contested. That practical frustration became the mathematical motivation for a new generation of packing models.

At the technical core of the first study is a two-dimensional orthogonal packing problem, a variant of the classic bin packing problem in which rectangular items must be placed without overlap inside a confined rectangular container. Traditional formulations of this problem typically minimize the number of bins used or the amount of unused space. The NC State team instead embedded a prioritization matrix into the bin packing framework, allowing items to be clustered with one another or pulled toward specific access points based on assigned priority weights. In other words, the model does not merely ask whether an item fits; it asks whether the item sits where it should relative to the doors, hatches or exits through which it will be retrieved, and whether it sits near the other items it belongs with.

This blending of ideas is what the authors describe as combining bin packing principles with facility layout concepts. Facility layout design, a longstanding field within industrial engineering, concerns itself with the spatial arrangement of departments and workstations to optimize flows and adjacencies. By importing that spatial-priority logic into packing, the researchers created a single mixed-integer linear programming model, or MILP, capable of balancing space efficiency against proximity to access points and adjacency among functionally related items. The result is a formulation that extends the utility of packing optimization to applications demanding far more nuanced layout preferences than simple volume maximization.

There was, however, an immediate computational obstacle. Every additional constraint, every priority level, every adjacency requirement multiplies the number of possible configurations the optimizer must consider. The researchers found that the potential combinations quickly outstripped available computing power when the full model was handed to a commercial solver as one monolithic problem. Their answer was a sliding-window matheuristic, a hybrid technique that breaks the large optimization problem into a sequence of smaller, overlapping subproblems. The algorithm solves each window of the packing space in turn, carrying forward the consequences of earlier decisions, and thereby obtains a good overall solution in a fraction of the time a direct solve would require.

The computational experiments were striking. When the sliding-window matheuristic was tested against a direct MILP solve using a commercially available optimization solver, the matheuristic consistently outperformed the monolithic approach in both runtime and solution quality. It also proved superior among the adapted heuristic and metaheuristic alternatives the team considered. For practitioners, that means high-quality, priority-aware packing plans can be produced on ordinary computing hardware rather than waiting hours or days for an exact solver to grind through an intractable search space, or settling for a crude rule-of-thumb arrangement.

The second study pushed the framework into an even more demanding arena: loading large maritime vessels. On a ship, geometry is only half the battle. Too much weight placed too high or too far to one side can cause the vessel to list, tilting dangerously, so load stability becomes a hard constraint that the packing plan must satisfy. As co-author Brandon McConnell, a research associate professor in NC State’s Edward P. Fitts Department of Industrial and Systems Engineering, explains, adding the stability constraint means working around a fixed center of gravity, and the correctness of the balance cannot be confirmed until the entire area has been packed, which usually forces adjustments. The team studied three solution approaches for this globally constrained problem: a monolithic MILP model, a sliding-window matheuristic, and a sliding-window matheuristic augmented with in-stride load balancing penalties applied during the solve.

Testing revealed a clear winner. The most efficient path was to run the existing sliding-window model first and then adjust selected lower-priority items at the end as needed to achieve stability, a post-processing strategy that selectively relaxes and re-optimizes item positions with minimal disruption to the prioritized layout. This simpler pipeline of matheuristic followed by repair emerged as the recommended practical configuration, offering the strongest overall combination of balance success, solution quality and runtime. The in-stride variant, which enforces balance during the initial solve, remains a narrower alternative for cases where achieving balance in the first stage is paramount. Notably, the matheuristic pipelines generated high-quality, load-balanced solutions for single-vessel scenarios within a few minutes on average, fast enough to let planners rapidly evaluate multiple loading configurations during time-critical deployment planning.

The implications stretch well beyond the military context that inspired the work, although combat loading, the art of arranging equipment on maritime transport so that mission-critical gear can be offloaded first while units stay cohesive and the vessel stays stable, remains the flagship application. The same mathematics applies to warehouse storage, where fast-moving goods should sit nearest the packing stations, and to multi-drop delivery services, where parcels for the last stops on a route must be loaded deepest and parcels for the first stops must be within easy reach. In every case, the model’s ability to encode multiple levels of prioritization, enforce groupings of related items, and respect physical constraints like balance turns a notoriously fiddly human judgment call into a reproducible computation.

The research team also included H. Sebastian Heese, Owens Distinguished Professor of Supply Chain Management; Michael Kay, associate professor of industrial and systems engineering; and Russell King, the Dopaco Distinguished Professor of Industrial and Systems Engineering, all of NC State. Kirschenman, a two-time selectee for the General Omar Bradley Research Fellowship in Mathematics, which helped support the research, sees the work as a bridge between abstract optimization and the messy realities of logistics. As McConnell puts it, the model could help ensure that mission-critical gear is offloaded first, save a warehouse worker’s time and energy, and lead to faster goods retrieval with fewer mistakes for any delivery business. In a world where supply chains are under unprecedented strain, teaching computers to pack with both intelligence and speed may prove one of the quiet but consequential advances of applied operations research.

Subject of Research: Prioritized two-dimensional orthogonal packing optimization with load balancing for military and commercial logistics

Article Title: Cracking the packing code – new models incorporate item priority, location and weight distribution

Article References: Cracking the packing code – new models incorporate item priority, location and weight distribution. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: packing problem, optimization, mixed-integer linear programming, matheuristic, military logistics, load planning, warehouse storage, supply chain, vessel stability, operations research, bin packing, Naval Research Logistics