14th AIMMS-MOPTA Optimization Modeling Competition


We are grateful for the continued support provided by AIMMS for the competition.

AIMMS is a technology platform that helps making high quality decisions with an increased level of awareness about impact, trade-offs, opportunities and risks. Whether it’s adapting to fluctuations or contingency scenario planning, questions can be answered with speed and confidence.

“AIMMS, as a company, is not only about technology. We are a dedicated team on a mission to help people make smarter decisions with clarity and ease.” – https://www.aimms.com


Introduction to the Problem

Hospitals are complex and expensive systems to manage. One department of particular interest that poses major managerial challenges is the operating room (OR) department. The OR department generates about 40–70% of revenues and incurs 20–40% of operating costs in a hospital. It also demands significant hospital resources and directly influences patient flow and efficiency of care delivery. Thus, hospital managers are constantly seeking better OR and surgery scheduling approaches to improve OR utilization, surgical care, and quality, as well as to minimize operational costs.

Stochasticity is an intrinsic characteristic of OR and surgery scheduling problems since surgical activities are subject to multiple sources of uncertainty. This competition focuses on an elective surgery planning problem (ESP) in flexible ORs, where emergency patients are accommodated in the existing elective surgery schedule. Elective cases can be scheduled weeks or months in advance. In contrast, the arrival of emergency surgeries is random, and must be performed on the day of arrival. The goal is to construct a plan that specifies the assignments of a subset of elective cases from a waiting list to available OR surgery blocks and the scheduled start times of surgeries assigned to each block. The surgical blocks are typically designed to allow for multiple surgeries to be scheduled during the surgery block’s time length. The plan’s quality is a function of costs related to performing or delaying elective surgeries, costs related to OR overtime and idle time, costs related to surgery waiting times, and costs related to canceling scheduled surgeries to accommodate emergency surgeries. Your team’s goal is to develop an efficient and implementable method to solve ESP that managers can use in practice.

For more details, read the full problem description.


Registration and Submission

Prior to starting on the competition, you must register here (link to be added soon).

You are free to use any software of your choice, but it is recommended to use AIMMS for your submission. All source code must be included, properly documented, and results must be reproducible. AIMMS is an industry-leading rapid model building and deployment platform perfected for over 30 years. AIMMS provides an enjoyable and robust way to not only build optimization models but to deploy them as optimization applications to be used by business professionals. You can develop analytical models and highly interactive end-user interfaces all within the same AIMMS environment.

Your team can obtain AIMMS software and request the free academic license here.

Free introduction courses to AIMMS are available here.

All files should be submitted by the deadline as a single email with a single attached zip file to both kshehadeh(at)lehigh(dot)edu and luz212(at)lehigh(dot)edu. Please start your submission email’s subject line with [MOPTA Competition 2022]. The body of the email should forward the registration confirmation email received at registration time, as the way to authenticate the submitting team. Good luck!


Eligibility

Teams of at most three students can participate. The team leader must be a graduate student, though the other members of the team can be advanced undergraduate students. Each member of the team must be registered as a full-time student at a recognized educational institution during the Spring term of the 2021-2022 Academic Year. Students with any background are eligible. Collaboration between students from different departments is strongly encouraged.

Each team must declare a team advisor with which the team may consult about the problem and their solution. It is the team advisor’s responsibility to ensure that the students have appropriate knowledge for the competition. The team advisor should not be involved as a participant in the solution process.

As the conference is international, so is the competition. Teams from all over the world can participate, as long as at least one team member can come to the conference, should the team make it to the final round.

The official language of the competition is English.

Teams are asked to register (see above) to the competition as soon as they start working on the problem. There is a separate deadline for the submission of solutions (see deadlines).

If you have questions about the problem or the competition format in general, please contact Dr. Karmel Shehadeh at kshehadeh(at)lehigh(dot)edu and Dr. Luis Zuluaga at luz212(at)lehigh(dot).edu. If you have questions about the AIMMS software and licensing related issues, please contact support(at)aimms(dot)com.


Competition Format

The competition consists of a few stages. In the first stage the teams are asked to develop models and solution methods (see the problem description above) and provide an implementation of the optimization models in AIMMS. The teams must submit a complete solution to the problem, including: implementation of the optimization models in AIMMS, ideally already including a graphical user interface that provides the user with graphical and textual output; solutions of the models for the given case study, and for other data sets generated by the teams, if any; a maximum 15 page report that discusses the models developed (along with mathematical background), the solutions obtained, and further recommendations. The AIMMS implementation is recommended for the optimization models only: the teams can use any tool of their choice to build the proposed model(s) and/or solution method or run machine learning algorithms on the case study provided if this is a part of their approach (documented source codes should still be included). The teams should also keep in mind that opportunities to improve the solution approach and the interface will be offered in the next phase. A panel of judges, including representatives from both the conference organizing committee and AIMMS evaluates the submissions, provides feedback to the teams and invites finalists to continue in the second stage of the project and present their work at a dedicated session of the conference. In this second stage, the finalists will receive advice from the panel on ways in which they can improve their model and solution and have time before the conference to continue to improve their solution. After the presentations at the conference, the judges will ask questions. The finalists are ranked based on a combined score for their model, implementation, report, solution, oral presentation, and answers to the judges’ questions. The decision of the judges is final and cannot be appealed.


Prizes

  • Winning team: $1200 to the team, and a certificate for each team member.
  • Second place team: $600 to the team, and a certificate for each team member.
  • Third place team: $300 to the team, and a certificate for each team member.

Also, the highest-ranked finalist that used AIMMS as the software platform to solve the case will be awarded an additional $1000 in prize money.


Copyright

By submitting an entry to the competition you agree that the organizers own the copyright to a copy of your submission. This does not limit your rights to publish your work, give talks, posters, etc., but grants the organizers of the competition the right to use your work.

If the modeling problem is used in any context outside of this modeling competition, it must be properly cited as follows:

Karmel S. Shehadeh and Luis F. Zuluaga (2022). "14th AIMMS-MOPTA Optimization Modeling Competition. Surgery Scheduling in Flexible Operating Rooms Under Uncertainty", Modeling and Optimization: Theory and Application (MOPTA), web site accessed on <include date>.