2024 Winner(s)
The Institute for Operations Research and the Management Sciences (INFORMS) proudly awards the 2024 Impact Prize to Professor John Chinneck for his seminal contributions to infeasibility analysis in optimization. His pioneering work has profoundly transformed the landscape of optimization, equipping practitioners with essential tools to diagnose and resolve infeasible models.
Chinneck’s groundbreaking algorithms for identifying irreducible infeasible sets (IIS) have become integral to virtually all major commercial optimization solvers. These algorithms, first implemented in MINOS and later adopted by leading solvers like LINDO, CPLEX, and Gurobi, have empowered users to pinpoint the causes of infeasibility in linear and mixed-integer programming models. This functionality has not only enhanced model debugging but has also facilitated the acceptance and expansion of mathematical programming in industrial applications worldwide.
The impact of Chinneck’s work is evidenced by the widespread adoption of his algorithms, which have been incorporated into numerous solver APIs and modeling languages, such as AMPL, GAMS, and MATLAB, enhancing their robustness and utility. His contributions have significantly improved the efficiency of model development, saving countless hours for optimization practitioners and enabling the development of larger and more complex models.
Beyond academia, Chinneck’s influence extends to practical applications in diverse industries, from radiation treatment planning and computer vision to signal recovery and network analysis. His algorithms have proven crucial in refining model formulations and conducting sensitivity analyses, thereby advancing the field of optimization in both theoretical and practical dimensions.
Chinneck’s work has been recognized and implemented by major corporations and has profoundly influenced the development of optimization software over the past three decades. His contributions have facilitated significant advancements in operational efficiency and decision-making processes across various sectors. For these reasons, INFORMS is honored to present the 2024 Impact Prize to Professor John Chinneck, celebrating his lasting contributions to the field of operations research and management science.
Purpose of the Award
The Impact Prize, awarded once every two years, is intended to recognize widespread impact in the practice of operations research. It may be awarded to an individual or a single set of collaborators. The award may be given for the original research (if these ideas have been widely adopted), and/or for special efforts required to bring the research to a practical form (e.g., implementation as a software package or the communication of a body of research through writings, teaching, and consulting). The important criterion is breadth of use in practice and relevance to operations research. The technical assessment of the quality of the work is considered secondary to the degree to which it has been widely adopted.
The Impact Award is generously sponsored by Princeton Consultants, Inc.
This is not a research award. The awards committee is not judging the quality of a body of work. Instead, emphasis will be placed on evaluating the breadth of the impact of an idea or body of research. If the major contribution of the individual or group is bringing an idea to a practice community, the precise nature of this contribution needs to be understood (leading a software company; lectures and writings).
The prize consists of a framed citation and a cash award of $1,000.
Initial Nominations Due April 30, 2024
The nomination should be brief (no more than 2 pdf pages), and should contain the name of the person or set of collaborators responsible for the contribution; a brief summary of the idea or technology that is the basis of the award; a description of the industry or practitioner population that uses the idea; and the nature of the contribution made by the person or group being nominated (creator of the idea or technology). More space is provided to give greater details on the elements described on the instruction page.